esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In fact, Cm is a spatiotemporal variable [24; 25], so its constant assumption may cause uncertainty for the retrieved AGB. In contrast, [PERSON] et al [23] simultaneously retrieved the LAI and Cm from PROSAIL model, and then obtained the final AGB through multiplying these two variables. Yet, an empirical relationship between leaf equivalent water thickness and Cm was used in their study, which may reduce the generality of their method. A re-calibration of their method is also needed, when applied to different scenarios or time frames. In summary, empirical variables or relationships are still involved in existing physically-based method, and a fully physically-based method for grassland AGB retrieval method still does not exist. Different data sources were employed to mapping grassland AGB, e.g., Landsat [14; 23], SPOT [1], MERIS [26], and Sentinel [27; 28]. The long-term coverage and high revisiting frequency make MODIS an unparalleled data source, and it has been widely used in grassland AGB estimation [2; 13; 28; 29]. However, the performance of physically-based method on MODIS data has not been assessed. A fully physically-based method for grassland AGB retrieval from MODIS imagery is urgently needed to further improve the accuracy and spatiotemporal coverage of grassland AGB estimation. The objective of this study is to propose a physically-based method to estimate grassland AGB based on inverting the PROSAIL model from MODIS data. The proposed method relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of Cm and LAI, i.e., AGB = Cm \(\times\) LAI. The main improvement of our method is its independence from any field measurement, i.e., it is a fully physically-based method, and no empirical variable or relationship is needed. Performances of the proposed method were evaluated in the Zoige Plateau, China.
## 3 Methods
### Loss Function  
In RANSAC and its variant methods, a predetermined threshold is used to classify data points. Data points in the set with a distance to the model less than the threshold are considered inliers, while those with a greater distance are considered outliers. Some methods follow the RANSAC approach to design their loss functions. For example, ref. [17] applies soft probabilistic hypothesis selection to optimize the model. Other algorithms [31] apply cross-entropy loss to model optimization based on the idea of match exclusion. They also use geometry-induced losses [19] to link the estimated model quality with the distribution of inliers.  
Inspired and influenced by these works, we compare our estimated model with the ground truth model to constrain the model's convergence. We use SVD decomposition [33] to turn the solution \(\hat{\theta}\) into rotation and translation \((\hat{R},\hat{t})\). We know that the rotation error \(\varepsilon_{\mathbf{R}}\) and translation error \(\varepsilon_{\mathbf{t}}\) can be calculated using the following formulas:  
\[\begin{array}{l}\varepsilon_{R}(\hat{R},R)=\cos^{-1}\bigl{(}(\mathbf{tr}(\hat {R}R^{\mathsf{T}})-1)/2\bigr{)}\\ \varepsilon_{t}(\hat{t},t)=\cos^{-1}\bigl{(}\frac{\hat{t}\hat{t}_{t}}{||\hat{t }||}\bigr{)}\end{array} \tag{4}\]  
Figure 8: The detail of spatial distribution encoder and Multi-Perspective Block. The KNN-Attention module is responsible for extracting domain-specific information, while the FPS-Attention module distributes this information through anchor nodes. Due to the inherent randomness of clustering and farthest point sampling, the Multi-Perspective Block effectively integrates multi-layer spatial distributions with single-layer confidence distributions. This fusion mitigates the impact of randomness on model performance.  
where \(R\) and \(t\) are the ground truth rotation matrix and ground truth translation matrix, respectively. The pose loss is subsequently defined as follows:  
\[L_{pose}=\frac{1}{2}(\epsilon_{R}(\hat{R},R)+\epsilon_{t}(\hat{t},t)), \tag{5}\]  
The average symmetric epipolar error is defined as follows:  
\[L_{epi}=\frac{1}{|\mathcal{I}|}\sum_{i\in\mathcal{I}}\varepsilon_{epi}(\theta, \Phi_{i}), \tag{6}\]  
where \(\mathcal{I}\) is the set of inliers selected based on the ground truth model, and \(\theta\) is the ground truth model. \(\Phi_{i}\) is the positional information of data item \(i\), and \(\varepsilon_{epi}\) is the residual error corresponding to the data item.  
Chamfer Loss \(L_{cham}\) is applied to constrain the spatial distribution of the sampling to be relatively sparse:  
\[L_{cham}(X_{s},X_{m})=\frac{1}{|X_{s}|}\sum_{x_{i}\in X_{s}}\min_{x_{m}\in X_{ m}}||\mathrm{x_{s}-x_{m}}||_{2}^{2} \tag{7}\]  
Here, \(X_{m}\) represents the matches in the minimal sampling set. The ultimate optimization objective is defined as  
\[L=L_{pose}+\alpha L_{epi}+\beta L_{cham} \tag{8}\]
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background
### Model of the Propagation
#### 2.1.2 Okumura-Hata Model  
The best-known empirical model is the Okumura-Hata model, which is based on Okumura measurements taken in the Tokyo area of Japan. This model considers several factors, essentially the nature of the environment, by specifying its degree of urbanization (dense urban, suburban, rural). The Okumura-Hata model is adapted for the limits:  
* Frequency range: (150 to 1000) MHz
* Height of the mobile terminal (H\({}_{\text{m}}\)): (1 to 10) m
* Height of the base station (H\({}_{\text{b}}\)): (10 to 200) m
* Distance (d): (1 to 20) km  
For this model of Okumura-Hata, the equation of path loss is given by the following equations:  
* For the urban environment [2; 4; 17; 18; 19]:\[\text{Lu}=69.55+26.16\log(\text{F})-13.82\log(\text{h}_{\text{b}})-\text{a}(\text{ h}_{\text{m}})+(44.9-6.55\log(\text{h}_{\text{b}}))\log(\text{d}) \tag{5}\] \[\text{With: a(h}_{\text{m}})=(1.1\log(\text{F})-0.7)\ h_{\text{m}}-(1.56 \log(\text{F})-0.8).\]
* For dense urban environment: \[\text{Lud}=\text{Lu}=69.55+26.16\log(\text{F})-13.82\log(\text{h}_{\text{b}})- \text{a}(\text{h}_{\text{m}})+(44.9-6.55\log(\text{h}_{\text{b}}))\log(\text{d})\] (6) \[\text{With a(h}_{\text{m}})=8.29\log(1.54\ h_{\text{m}})^{2}-1.1.\] Suburban environment: \[\text{Lsu}=\text{Lu}-2(\log(\text{F}/28))^{2}-5.4.\]
* For the rural environment: \[\text{Lr}=\text{Lu}-4.78\left(\log(\text{F})\right)^{2}+18.33\log(\text{F})- 35.94\] (7) With: F: Designates the frequency (MHz). \[\text{h}_{\text{m}}\] : Designates the size of the mobile terminal (m). \[\text{h}_{\text{b}}\] : Designates the height of the base station (m). \[\text{d}\] : Designates the distance (km). \[\text{a(h}_{\text{m}})\] : Correction factor for the height of receiver antenna.
## 4 Experiment and Analysis
### Experimental Results and Analysis  
#### 4.2.1 Analysis of Sugarcane Leaf Posture Changes  
The quick camera recorded the process of the sugarcane leaf being picked up by the picking mechanism, as shown in Figure 16.  
Figure 16: Quick camera experiment of sugarcane leaf-picking process. (**a**) represents the situation for the experiment at 0.2325 s, (**b**) represents the situation for the experiment at 1.0695 s, and (**c**) represents the situation for the experiment at 1.86 s.  
It can be seen that at 0.2325 s, the middle section of the cane leaf had been picked up by the elastic teeth. Both sides of the sugarcane leaf were bent by the elastic teeth, and then hung down to touch the ground. The overall posture of the cane leaves is a \"C\" shape. At 1.0695 s, the sugarcane leaf was lifted above the guard by the elastic teeth and then pushed downwards by the elastic teeth. The bending of the sugarcane leaf on both sides had become greater. The overall posture of the sugarcane leaf was in the shape of a logarithmic curve. At 1.86 s, the two sides of sugarcane leaf hit the door frame. The middle of the sugarcane leaf stopped at the end of the guard plate. At this time, the whole posture of the sugarcane leaf is a \"V\" shape. The posture of sugarcane leaf in the experiment is in good agreement with that in the simulation.
# Hybrid Model-Based Traffic Network Control Using Population Games
## 4 Population Game-Based Model for Urban Traffic Control
### Fictitious Play  
In fictitious play, a player \(i\) plays a pure best response (BR) action \(a_{i}\) based on the joint distribution of the selected actions of its opponents. This can be expressed by  
\[a_{i}^{t}\in BR_{i}(\mathbf{p_{\_i}^{t}}), \tag{20}\]  
where \(a_{i}^{t}\) represents the action performed by agent \(i\) at time \(t\), and \(\mathbf{p_{\_i}^{t}}\) is the joint probability distribution of actions performed by all players except \(i\). The marginal probability for each player \(i\) up to time \(t\) is calculated using the expression  
\[p_{i}^{t}=\frac{\gamma_{i}^{t}(a_{i})}{t}, \tag{21}\]  
where \(\gamma_{i}^{t}(a_{i})=\sum_{\tau=0}^{t-1}\mathcal{I}\{a_{i}^{\tau}=a_{i}\}\) is an \(S\)-dimensional vector that counts the times that player \(i\) has played each action. According to the above expressions, it is noticeable that in fictitious play, each agent requires historical data in order to make decisions. Consequently, in our context, this control strategy is assumed to follow a fixed-time approach.  
The payoff for an agent \(i\) is a function that depends on its current action and the action set \(a_{\_i}\) played by the agents different to \(i\). In our case, this payoff depends on the queue length of links converging to a node. This is presented in Definition 2.  
**Definition 2**.: _The payoff function in a fixed-time scheme modeled as a fictitious play based-control strategy depends on the accumulative queue length of the incidents links. This is given by_  
\[U(a_{i},a_{\_i})=\sum_{i=1}^{n}l_{\_i}, \tag{22}\]  
_which must be minimized._  
According to Definitions 1 and 2, fitness and payoff functions determine the control strategies' dynamics in adaptive and fixed-time schemes, respectively. For the adaptive control case, given by the online population dynamics described in Section 4.2, the green time is continuously updated according to the fitness function variation, which impacts the transition between the modes of the hybrid system described in Section 3.2. In the fixed-time case, the payoff function depends exclusively on the queue lengths of incident links to a specific node, which means that the mode transitions are determined by the current network configuration and the modes that have provided the best queue length reduction historically.  
Both functions are strongly related with the traffic network dynamics for which they were taught, i.e., to associate good fitness or payoffs in links with low queue lengthsand acceptable green times. The results of the dynamic changes in the functions' values are described through the performance parameters for different scenarios, as shown in Section 7.
###### Abstract  
Mission- and safety-critical applications tend to incorporate triple modular redundancy (TMR) in their hardware implementation to reliably withstand the fault or failure of any one of the function modules during normal operation, and the function module may be a circuit or a system. In a TMR implementation, two identical copies of a function module are used in addition to the original function module, and the correct operation of at least two function modules is required. In TMR, the corresponding primary outputs of the three function modules are combined using majority voters, which determine the actual primary outputs based on the Boolean majority. Hence, the majority voter is an important component that is useful for conveying the correct operation of a TMR implementation. In the existing literature, many designs of three-input majority voters for TMR have been discussed. However, most of these correspond to the synchronous design style and just one corresponds to the bundled-data asynchronous design style, which is not delay insensitive and hence non-robust. To our knowledge, a robust delay insensitive design of the three-input majority voter has not been considered. In this context, this article presents the designs of robust quasi delay insensitive (QDI) three-input majority voters based on QDI logic synthesis methods, and analyzes which majority voters are preferable in terms of speed, power, and area. We implement example QDI TMR circuits using a QDI full adder as the function module and QDI majority voters using 32/28 nm complementary metal oxide semiconductor (CMOS) technology. The QDI TMR implementations use the delay insensitive dual rail code for data encoding, and four-phase return-to-zero and four-phase return-to-one handshake protocols for data communication.  
fault tolerance; redundancy; TMR; logic circuits; quasi delay insensitive (QDI); CMOS
## 5 Security Analysis
### Abstract Requirements for Secure Multiparty Computation  
We examine the properties of validity, agreement, termination, and privacy to postulate the security of our protocol, as depicted in [23], Definition 1.0. Since the key is produced employing (true) random numbers obtained individually by each party \(P_{i}\), \(i=1,2,3\). Individual contributions to the key are never transmitted, and individual inputs remain secret to other processes (apart from the shares given away). Malicious processes cannot prevent the computation from taking place or influence the computation in favorable ways (paragraph adapted from [23]). Therefore, validity is ensured in the sense that the final result is obtained with at least all the correct inputs in the chain of computation as the party currently working will utilize its data and the value received from the previous party to perform the calculation, which can only be achieved if accurate values are supplied. In contrast, agreement is achieved directly during the first half of the protocol, even in the presence of a dishonest attacker, since replacing randoms with other numbers indistinguishable from random will produce the same key agreement. Therefore, only in the second half of the procedure is it possible that the distribution of \(s_{1}\) and \(s_{2}\) to \(P_{2}\) and \(P_{1}\), respectively, if intercepted by a dishonest party, will produce a discrepancy in the OTP key \(K\) calculated by \(P_{1}\) and derived by \(P_{2}\), see Figure 2, with the result that, in this case, the message will not be recovered. This can be avoided using Algorithm 2, the KeyGen security check, as depicted above.  
Finally, termination and privacy are inherent in the process. Termination means that every correct process eventually computes the result intended by the protocol, and privacy means that the specific inputs of the individual parties are never transmitted, calculated, or potentially revealed.
## 5 Examples
### Service Environment Clustering  
The altitude data of 153 takeoff-to-landing flight profiles were clustered for the service environment. The number of clusters was \(l=4\); the fuzzy clustering results are shown in Figure 8 and Table 1. For only \(hp_{\max}\) and the 2D vector \((hp_{\max},n_{t})\), the results are very different: the latter is more distinguishable because it considers the FCDF or the detailed process.  
Figure 8b shows the relationship between flight profile samples and each cluster center. The training frequency of the second and fourth clusters is significantly higher than that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Cluster Mark**} & \multicolumn{2}{c}{\(hp_{\max}\)} & \multicolumn{2}{c}{\((hp_{\max}n_{t})\)} \\ \cline{2-4}  & **Maximum Altitude (\%)** & **Maximum Altitude (\%)** & **Training Frequency** \\ \hline
1 & 33.01 & 18.93 & 1.65 \\
2 & 55.54 & 35.59 & 2.78 \\
3 & 74.68 & 57.24 & 1.83 \\
4 & 81.03 & 78.14 & 3.69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cluster centers from the FCM method.  
Figure 8: Cluster results of service environment based on maximum altitude and FCDF: (**a**) FCM with the variable of maximum flight altitude; (**b**) FCM with 2 variables of maximum flight altitude and FCDF.  
Figure 7: Effect from parameters of altitude feature extraction method: (**a**) sliding window width \(k\); (**b**) intervals of compressed data \(\Delta n\); (**c**) altitude threshold \(c\).  
of the other two, indicating a significant correlation between the FCDF and the maximum flight altitude. The training frequency does not increase with the maximum altitude; that is, it is not the case that the larger the maximum altitude, the greater the training frequency.  
It is worth noting that the determination of \(l\) relies on the effectiveness of the classification and training missions. The clustering result in this section is only from altitude, but it will be used to further classify the service environment with Mach, turning times, flight time, etc., so an unreasonable \(l\) may induce an empty sample in some clusters. The determination of the clustering number \(l\) is often made according to practical experience, and there is little research on parameter determination using fuzzy clustering methods [25].
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 2 Overview of Signal Modelling and Compressed Sensing  
In this section, we analyze and mathematically model the problem to be solved in the article. Afterwards, we give a brief overview of the compressed sensing reconstruction method.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Security Attributes of ECSSP-SC
#### 6.1.5 Availability  
As per Assumption 1, the password of the \(USR\) is not exposed to the attacker. Furthermore, the password \(PWD\) of the \(USR\) is updated synchronously with the service provider and the smart card that is issued to the user. Except for this, there is no synchronous updating of information in the protocol. Thus, ECSSP-SC satisfies de-synchronization and provides availability.
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 4 Experimental Results and Analysis
### Experimental Datasets  
CWRU Dataset:  
The CWRU dataset from Case Western Reserve University is a standard dataset for bearing fault diagnosis. The data was collected using a bearing vibration test rig, which includes a 2-horsepower motor, torque sensor/encoder, power meter, and electronic control equipment. Single-point damage faults were introduced on the outer race, inner race, and balls of the bearing through electrical discharge machining. The damage diameters are 0.007, 0.014, and 0.021 inches, respectively. Accelerometer sensors were installed on both the fan end (FE) and the drive end (DE) to collect vibration signal data. The sampling frequency for FE was 12 kHz, while for DE, it was 12 kHz and 48 kHz.  
The experimental data selected for this experiment were sampled at a frequency of 12 kHz, with a fault diameter of 0.007 inches, a load range of 0 to 3 hp, and selected bearing conditions of H, BF, IRF, and ORF located at the 6 o'clock position. The CWRU dataset underwent sliding window processing, with a window size of 2048 and a step size of 400, resulting in approximately 4 \(\times\) 299 samples.  
MFPT Dataset:  
The MFPT dataset comes from the public bearing dataset of the Mechanical Failure Prevention Technology Society. This dataset contains vibration signals under different operating conditions, including inner race faults, outer race faults, and normal conditions. Vibration data for normal conditions was collected using a test rig with NICE bearings at a 270 lb load and a sampling rate of 97,656 Hz, with a sampling time of 6 s. Seven datasets for outer race faults were collected, with load variations of 25, 50, 100, 150, 200, 250, and 300 lbs, at a sampling rate of 48,828 Hz and a sampling time of 3 s. Seven datasets for inner race faults were collected with load variations of 0, 50, 100, 150, 200, 250, and 300 lbs, at a sampling rate of 48,848 Hz and a sampling time of 3 s.  
The experimental data selected for this experiment are as follows: since the vibration signals of bearingsings in a healthy state are generally smooth and less affected by load, the distribution of vibration signal data collected under different loads can be considered approximately equal. In the experimental design, all healthy state samples are selected under 270 lb with a sampling rate of 97,656 Hz. Vibration data for outer race fault and inner race fault are generated with a sampling rate of 48,828 Hz and loads of 150 lb, 200 lb, 250 lb, and 300 lb, respectively, for 3 s, forming four datasets. The MFPT vibration data was processed using a sliding window of 2048 steps with a stride of 400, resulting in approximately 3 \(\times\) 362 samples.  
We utilized 80% of the samples as the training set to train the model and the remaining 20% as the test set to validate the model's performance.
## 2 System and Channel Models
### Phase-2 (\(P_{2}\)): \(D_{1}\to\) (\(D_{2}\), FU) Data Transmission
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**A\({}_{\text{BS}}\) = 2 and \(M_{1}\) = 16**} & \multicolumn{2}{c}{**A\({}_{\text{BS}}\) = 4 and \(M_{1}\) = 8**} \\ \hline
**Input Bits, \(X_{1}\)** & **Antenna** & **Transmit** & **Antenna** & **Transmit** \\
**\(X_{1}\)** & **Index/Number** & **Symbol** & **Index/Number** & **Symbol** \\ \hline
00000 & 0 & 0000 & 00 (0) & 000 \\ \hline
00001 & 0 & 0001 & 00 (0) & 001 \\
00010 & 0 & 0010 & 00 (0) & 010 \\ \hline
00011 & 0 & 0011 & 00 (0) & 011 \\ \hline
00100 & 0 & 0100 & 00 (0) & 100 \\
00101 & 0 & 0101 & 00 (0) & 101 \\ \hline
00110 & 0 & 0110 & 00 (0) & 110 \\
00111 & 0 & 0111 & 00 (0) & 111 \\ \hline
01000 & 0 & 1000 & 01 (1) & 000 \\
01001 & 0 & 1001 & 01 (1) & 001 \\ \hline
01011 & 0 & 1011 & 01 (1) & 101 \\ \hline
01110 & 0 & 1110 & 01 (1) & 111 \\ \hline
10000 & 1 & 0000 & 10 (2) & 000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An example of mapping table at BS for data transmission during \(P_{1}\). Transmission of 5-bits employing 2 antennas (A\({}_{\text{BS}}\) = 2) and 16-QAM/16-PSK (\(M_{1}\) = 16) or, 4 antennas (A\({}_{\text{BS}}\) = 4) and 8-QAM/8-QPSK (\(M_{1}\) = 8).
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 4 Simulation and Results Analysis
### Results Analysis  
This section will present an analysis of the proposed SRPL-RP. We tested the proposed SRPL-RP in the grid-center network topology, grid-random network topology, and random network topology. We ran and repeated the simulations 60 times for the three types of topologies at different time stages: the network convergence, the network stability, and the network at the end of the simulation. This measures the changes in the security accuracy and the network performance levels of the proposed protocol concerning times of 3 min, 15 min, 30 min, 45 min, and 60 min. The extracted results of the proposed SRPL-RP were used for comparison with the existing countermeasures, which were the Standard RPL with Attacks, SBIDS [13], and RPL+Shield [14], to measure the effectiveness and performance.