esa-sceva/satcom-chunk-collection huggingface dataset:
# General Moving Object Localization from a Single Flying Camera
## System Design
### Flight Pattern  
To obtain the required image disparity, the drone has to fly in a specific pattern. As pointed out in Section 3.2.2, the drone is instructed to perform the action of flying straight to the left or right with maximum speed. However, the drone takes time to reach its maximum speed from its hovering state. Since the drone is instructed to fly with maximum speed, it will be accelerating, which causes the distance travelled between each time interval to increase as time goes by. This is the intended flight pattern in order to make our algorithm work. If the drone flies with constant velocity, and if the object is also moving with a constant velocity, we will get multiple candidate paths due to the geometric similarity between them.  
Figure 4: Baseline calibration. The drone is flying from the left to the right. The distances C1, C2, etc. are the distances between the positions of the drone where consecutive images are taken. The distances \(\sum\ 1\,\sum\ 2\), etc. are the sum of the distances from the drone’s starting position to the position where the respective image is taken. \(\sum\ 1,\sum\ 2\), etc. correspond to the length of the baseline between the two cameras’ positions.  
This problem is illustrated in Figure 6. At the bottom is the quadrotor drone moving at a constant speed in a horizontal direction (it does not matter whether it is flying to the left or right). The object we are trying to localize is expressed as a peach-colored rectangle moving in an arbitrary direction (along the peach-colored path). However, if the object is at a further distance and moving with a slower velocity (i.e., the red rectangles along the red path), or if it is at a nearer distance with a faster velocity (i.e., the blue rectangles along the blue path), they will all generate the same image disparity in the image sequence. Of course, the size of the object in the image would be different if it is nearer or further away, but as we do not assume that we know the size of the object, we will not be able to determine which case it is. It is fair to assume that the object is moving at a constant speed; therefore, the drone must be in a state of acceleration so as to avoid this depth ambiguity.  
Figure 5: Pedestrian detection.  
Figure 6: Illustration of depth ambiguity. The drone flies from the right to the left at a constant speed, keeping a separation of C between the drone positions \((x_{d},\ y_{d})\) where consecutive images were taken. The object (pedestrian) with position \(\left(x_{p},\ y_{p}\right)\) is the peach-colored rectangle moving in a straight line with a constant speed. We can see that if the object (pedestrian) is moving along the blue line or red line instead, it will be captured at exactly the same position in all the images.
## 3 Main Flow Features  
The description of the flow features in the three considered geometries refers to the echographic measurements shown in Figures 6-8 for healthy, aneurismatic, and coarctated aortas, respectively. We carried out the measurements with a long-axis view, and the investigated portions of the aorta (A and B) are sketched for the sake of clarity. We considered the flow-rate waveform in Figure 1b and three temporal instants along the cardiac cycle: \(t/T=0.156\) (systolic peak), \(t/T=0.208\) (early diastole), and \(t/T=0.260\) (diastole).  
\begin{table}
\begin{tabular}{c c c c c} \hline \(T\) [s] & 0.67 & 0.79 & 0.95 & 1.14 \\ \(SV\) [mL] & 41.4 & 69.1 & 101.5 & 133.8 \\ \(L/D\) [\(-\)] & 8 & 25 & 45 & 62 \\ \hline \end{tabular}
\end{table}
Table 1: Quadrature points for the parameters \(T\), \(SV\), and \(L/D\).  
Figure 4: Waveforms of the inlet flow rate imposed through the pump of the circulatory mock loop for the stochastic sensitivity analysis to \(SV\) and \(T\).  
Figure 5: Distribution in space of the inlet velocity at the systolic peak for different lengths of the inlet duct: (**a**) \(T=1.14\) s and \(SV=41.4\) mL; (**b**) \(T=0.67\) s and \(SV=133.8\) mL.  
Figure 8: (**a**) Sketch of the coarcatated aorta geometry (light gray) and of its sagittal plane (dark gray). Echographic results at (**b**) \(t/T=0.156\), (**c**) \(t/T=0.208\), and (**d**) \(t/T=0.260\).  
Figure 6: (**a**) Sketch of the healthy aorta geometry (light gray) and of its sagittal plane (dark gray). Echographic results at (**b**) \(t/T=0.156\), (**c**) \(t/T=0.208\), and (**d**) \(t/T=0.260\).  
Figure 7: (**a**) Sketch of the aneurys aorta geometry (light gray) and of its sagittal plane (dark gray). Echographic results at (**b**) \(t/T=0.156\), (**c**) \(t/T=0.208\), and (**d**) \(t/T=0.260\).  
No significant flow separations or recirculations were found in the healthy aorta at the systolic peak, as shown in Figure 6b. The flow exhibits a regular behavior both along the ascending and descending aortic portions at the systolic peak and also in the early diastole (Figure 6c). Some irregularities are found during the diastole, typical of flow deceleration (Figure 6d). A significantly different flow pattern occurs in the ascending aorta (A view) when affected by the aneurystic pathology. The increase in the vessel cross-section leads to wide flow recirculations, both at the systolic peak and during the diastole (Figure 7b-d). Similar flow patterns are instead found for healthy and aneurismatic aortas in DA (B view). The coarctated aorta does not exhibit flow separations in the ascending aorta (A view in Figure 8) and in the descending aorta upstream of the striction induced by the coarctation (B view in Figure 8; the flow entering the coarctation can be clearly viewed on the right side of the figure). A similar flow pattern as for the healthy aorta is found upstream of the coarctation.
## 6 Simulation Setup and Performance Metrics
### Performance Metrics
#### 6.2.2 Delay (s)  
Delay refers to the amount of time a signal takes across the network from a source UAV to a destination UAV. Delay is a key factor for measuring the performance of a communication network. The model aims to limit the delay by facilitating direct communication between the UAV and with the ground station. End-to-end delays are comprised of processing, queuing, and transmission delay of the link in a network. Mathematically, the average end-to-end delay can be shown with the help of Equation (8):  
\[\text{D}_{\text{end-to-end}}\ =\sum_{\text{t}=1}^{\text{N}}(\text{T}_{ \text{t}}+\text{R}_{\text{t}}+\text{B}_{\text{t}}+\text{P}_{\text{rt}}) \tag{8}\]  
where T\({}_{\text{t}}\) is Transmission time, R\({}_{\text{t}}\) is retransmission time, B\({}_{\text{t}}\) is buffer time, and P\({}_{\text{rt}}\) is processing time.
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
However, to the best of our knowledge, there have been few results concerning consensus-based DSE with FDI attacks, which serves as the main motivation of this paper. In this paper, we aim to combat FDI attacks on radar networks. The contributions of this paper are summarized as follows:  
1. A novel, consensus-based DSE algorithm is proposed to enhance the resilience of radar networks against FDI attacks. Only local information exchange is needed in the proposed method. Different from the existing works, a real-time, residual-based detector is introduced to the DSE framework to detect FDI attacks effectively by analyzing the statistical properties of the residual. The proposed method eliminates the hypothesis in [24]; that is, less than half of the sensors are attacked. Note that even if half of the sensors are under an FDI attack, the proposed method is also effective. 2. To estimate the FDI attack parameters, a simple yet effective attack parameter estimation method is proposed based on a pseudo-measurement equation, which has the advantage of decoupled estimation of the state and attack parameters compared with augmented state filters. 3. To mitigate the FDI attack and achieve global consistency in a timely manner, a novel hybrid consensus method is proposed by combining the CM and CI methods, which can provide estimation accuracy improvement when the number of consensus iterations is moderate. The paper is outlined as follows. Section 2 formulates the problem. Moreover, the topology structure, target dynamics and radar measurement model are introduced. Section 3 derives the novel DSE algorithm with FDI attacks. In Section 4, the effectiveness of the proposed consensus-based DSE method is demonstrated via simulation. In Section 5, the conclusions are given.
# A Blockchain Solution for Remote Sensing Data Management Model
###### Abstract  
A large number of raw data collected by satellites are processed by the production chain to obtain a large number of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data provide a critical foundation for data analysis and decision-making. Traditional centralized cloud computing systems are vulnerable to attack and, once the central server is successfully attacked, all data will be lost. Distributed ledger technology (DLT) is an innovative computer technology that can ensure information security and traceability, is tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data have the characteristics of a large data volume, a spatiotemporal nature, global scale, and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain technology. The novelty of this paper is the proposal of a multi-level blockchain architecture in which the system collects remote sensing data and stores them in the Interplanetary File System (IPFS) network; after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. The distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management.  
blockchain; remote sensing data management; distributed ledger technology; trusted service; security +
Footnote †: journal: Applied  
doi:10.3390/app13179609
## 3 Methodology
### Machine Learning Methods
#### 3.4.4 Multilayer Perceptron  
A multilayer perceptron (MLP) is a type of artificial neural network consisting of multiple layers of neurons [56]. It serves as a key component in more complex neural network architectures and is widely employed in various ML applications, including classification, regression, and signal processing.  
An MLP comprises several layers of neurons as depicted in Figure 4, including an input layer, an output layer, and at least one hidden layer. A multilayer perceptron (MLP) is a fully connected neural network, meaning that each neuron in one layer is linked to every neuron in the subsequent layer. The input layer receives data, which can include numerical or encoded categorical features. Hidden layers are responsible for learning complex relationships within the data; the more hidden layers and neurons present, the more sophisticated the models that can be trained. The output layer generates predictionsfor specific tasks, whether for regression or classification. Hidden layers commonly employ activation functions, such as ReLU (Rectified Linear Unit), sigmoid, or tanh, enabling the network to capture nonlinear relationships. MLPs are trained using backpropagation, in which the gradients of the loss (error) over the parameters (weights) of the network are calculated and adjusted using an optimization algorithm, such as gradient descent. Listed below are the hyperparameters employed for Grid Search optimization:  
* Number of neurons (units): {16, 32, 64, 128};
* Activation function (activation): {'tanh','relu'};
* Dropout rate (dropout): {0.0, 0.2, 0.5};
* Recurrent layer dropout rate (recurrent_dropout): {0.0, 0.2, 0.5};
* Optimizer (optimizer): {'adam','sgd'};
* Learning rate (learning_rate): {0.001, 0.01, 0.1}.
## 2 High Data Rate Modulator Design Description
### ACM Mode Adaptation  
The ACM mode adaptation represents an interface for the incoming stream named \"transfer frames\". The input interface maps an electrical signal to bits. The purpose of ACM is to provide the SCCC encoding unit with information blocks of varying length \(K\) with an embedded synchronization pattern that will aid the receiver to recover the binary data.  
In order for the receiver to work properly, it requires that the incoming signal has sufficient bit transition density and allows the proper synchronization of the decoder. It is important to highlight that this standard is designed for high-speed data telemetry transmission, so usually large amounts of data will be transmitted. The pseudo-randomizer block diagram is shown in Figure 2. All the registers are initialized to all-ones state, then at each clock cycle, we generate a random sequence that repeats itself every 255 bits. Incoming transfer frames are randomized by XOR-ing each incoming bit with each output bit of the pseudo-randomizer. Once the transfer frame was processed, all registers must be re-initialized to the all-ones state.  
A synchronization method is necessary to simplify the receiver structure and obtain sufficient performance. It is necessary that the receiver correlates the decoded binary  
Figure 1: Functional block diagram.  
Figure 2: Pseudo-randomizer architecture.  
stream with a pattern in order to find the starting index of each embedded randomized data block. Thus, a 32-bit asynchronous synchronization marker (ASM) 0x1 ACFFC1D is attached at the end of each transfer frame to create a channel-data-access-unit (CADU).  
Then, a stream of CADUs is fed into the slicer, whose main function is to split its continuous input into a sequence of well-defined information blocks of length \(K\) in accordance with the selected ACM format. Again, this is specified by the standard in order to simplify the implementation.  
Although the ASM block seems to be simple, an efficient implementation as the one proposed in this paper is very important for the main goal of high-speed data transmission. If it is not properly designed, it will introduce several delays that can even generate problems in synchronization.
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
### Strategies to Improve Ionic Conductivity of SPEs
#### 3.2.1 Incorporation of More Than One Salt
[PERSON] and co-workers [57] have studied the influence of primary, secondary and ternary salts on PEO-based SPEs incorporated with halloysite nanoclay (HNC) for application in Li-ion batteries. For PEO-LiTFSI-HNC SPEs, the ambient conductivity was ~5.62 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\), and there was a slight increment when a secondary salt, i.e., lithium bis(oxalate)borate (LiBOB), was added, but then there was a decrement in conductivity (~1.99 \(\times\) 10\({}^{-5}\) S cm\({}^{-1}\)) was observed with the addition of a third salt, lithium nitrate (LiNO\({}_{3}\)) [57]. Nonetheless, \(T_{g}\) values were enhanced from \(-\)47.02 to \(-\)40.48 \({}^{\circ}\)C for singleto ternary salt-containing SPEs [57]. Mechanical strength also improved as proven from dynamic mechanical analysis (DMA). The authors have concluded that each respective salts have their own role with LiTFSI being the main contributor to ionic conduction, whereas the presence of LiBOB and LiNO\({}_{3}\) salts was to ensure the emergence of cathode electrolyte interphase and solid electrolyte interphase SEI on cathode and anode, respectively [57]. Each of these salts have complemented each other and yielded Li-ion-cell performance with better electrochemical stability. Cells employed one and two salts suffered failure within the first five cycles. In contrast, the lithium nickel cobalt manganese oxide (NMC)/Li cell with SPE containing trio salts exhibited the discharge capacity of \(\sim\)116 mAh g\({}^{-1}\) at 60 th cycle [57]
## 4 Discussion
### Analysis of NDVI, EVI, and SIF Simulation and Prediction Results  
Presently, CA-Markov models are predominantly employed for simulating and predicting land use patterns [30; 31], highlighting the need for more research on simulating and predicting with vegetation remote sensing products. This study examined the dynamics of terrestrial vegetation remote sensing products in China over an extended time series, using NDVI, EVI, and SIF datasets. The CA-Markov prediction model was applied to forecast the spatial and quantitative changes in vegetation remote sensing products in China from 2020 to 2025. These findings offer technical support and references for efficient land use, spatial planning and layout, regional ecological security, and sustainable regional socio-economic development [32].  
When implementing the CA-Markov model in the simulation process, the impact of natural, social, and economic factors on vegetation cover change should be comprehensively considered, given the complexity of the model transformation rules [33; 34]. However, during the study, it was observed that the model needed to accurately represent anthropogenic factors such as population, economy, and policies, which significantly influence town centers, as crucial factors in addition to the changes in vegetation cover. Consequently, future research should continuously improve the cell transformation laws of the CA-Markov model and consider combining various models to achieve higher precision and accuracy in predicting vegetation change.  
The process of vegetation change is complex and influenced by numerous factors. Different regions experience distinct vegetation changes, which reflect various ecological methods and are attributed to their unique environmental significance. Nonetheless, only a limited number of studies have investigated the multiple vegetation remote sensing products considered in this research. As a result, further investigation is required to adjust model parameters based on a comprehensive analysis of diverse factors. Future studies should perform more in-depth and extensive analyses of vegetation change to explore a more comprehensive and targeted reflection of regional vegetation change characteristics. This would provide valuable references and suggestions for the feasibility of vegetation ecological construction in China.
## 4 Conclusions  
This study demonstrated that the technology of SMFC can speed up the removal of the macrophyte litter in sediments from a large shallow lake. Electricity was also produced during the 700 days of operation without frequent loading or extensive maintenance, which realized energy recycling. However, the application effect was significantly influenced by the sediment sources. The main reason was due to the differences in the microbial community structure of sediments. In this study, we found that SMFCs using the sediment from the macrophyte-dominated area promoted more bacterial abundance of _Treponema_, _Clostridium_, _GOUITA_19 and _LCP_-6, which could degrade the more recalcitrant substances. Moreover, it was found that sediments from the macrophyte-dominated area became more humified and had a higher electricity generation compared to that from algae-dominated area. Considering enhanced organic matter biodegradation, SMFCs could be used as a new promising remediation technology for the grass type lakes caused by eutrophication and rehabilitation.  
Further works should also be explored, such as the influence of environmental conditions, which contain the changing of temperature, pH, electron sinks, and the appropriate electrical load.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/18/3703/s1](http://www.mdpi.com/2076-3417/9/18/3703/s1)]([http://www.mdpi.com/2076-3417/9/18/3703/s1](http://www.mdpi.com/2076-3417/9/18/3703/s1)), Figure S1: Location and photos of sampling sites in Lake Taihu, China, Figure S2: Schematic diagram (A) and photo (B) of the experiments, Figure S3: Current (A) and power (B) generation along with time from SMFCs inoculated with TH1 and TH2 sedimentary samples. Current and power were represented as mean voltages and standard deviations (n = 2), Table S1: Characteristic of the experimental set-up, Table S2: Characteristics of the basic physicochemical properties of sediments and water in the experimental sites, Table S3: Performance of solid organic matter-fed SMFCs to date.  
Figure 6: Relative abundance percentages of bacterial genus communities in original sediments, bulk sediments and electrode biofilm from the two sampling sites at the end of the experiments.  
**Author Contributions:** N.S., preparation, creation and/or presentation of the published work, specifically writing the initial draft; H.J., ideas; formulation or evolution of overarching research goals and aims; Z.Y., review and editing.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (41501528, 51879256, 51679228, 51839011, 51861125201), and the Special Foundation on Water Pollution Control and Treatment of China (2017 ZX07204005).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of a New Rubber Buckling-Restrained Brace System for Structures
## 7 Comparison of BRB and BRBR
#### Weight Comparison of BRB and RBRB  
Comparison between the prototypes' weights proved that replacing the concrete volume with rubber reduced the weight by 30%, from 80 kg in RBRB to 56 kg in BRB. This weight reduction offers several valuable advantages for structures. This weight reduction can improve the seismic behavior of the structure by decreasing the size of structural elements and also ease the transportation and installation process.  
In phase 1, the RBRB operates with both the steel core and rubber components working together to resist lateral forces, which results in higher effective damping (0.648). This phase optimizes energy dissipation and load-bearing capacity. However, as the core begins to fail due to excessive deformation, phase 2 becomes active, where the rubber components take over and continue to resist forces. The effective damping in phase 2 (0.521) is lower because the rubber components, while providing continued resistance, are not as effective at dissipating energy compared to the combined action of the steel core and rubber in phase 1.  
1. It decreases the total seismic weight of the structure, thereby enhancing its performance level.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Effective Stiffness** & **Effective Damping** & **Dissipated Energy** \\  & **(kN/mm)** & **(kN.s/mm)** & **(kN\(\cdot\)mm)** \\ \hline BRB & 14.5 & 0.603 & 13,416 \\ \hline RBRB (Phase 1) & 34 & 0.648 & 10,027 \\ \hline Rubber (Phase 2) & 2.2 & 0.521 & 12,432 \\ \hline RBRB + Rubber (Full) & 36.2 & - & 22,459 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental comparison values for BRB and RBRB in effective stiffness, effective damping, and dissipated energy.  
Figure 44: BRB and RBRB energy dissipation.  
Figure 43: BRB and RBRB effective damping.  
It improves the structure's performance under P-Delta effects.
3. It reduces the weight of the bracing system, leading to smaller structural elements and greater economic efficiency.
4. It simplifies transportation and installation procedures.