esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Results of the FEM Simulation
#### 3.1.3 The Results of the Pitch Angle Case  
Based on the FEM model as shown in Figure 5, there are 30\({}^{\circ}\) pitch angles for the tracking receivers, while \(d_{1}\) is 3 m and \(d_{2}\) is 4 m. The magnetic fields measured by tracking receivers, shown as Figure 8, are the radiated magnetic field of the transmitting coil on the line ((0, 3, \(-\)4.5), (0, 3, 4.5)) and the line ((0, 3.5, \(-\)4.5), (0, 3.5, 4.5)) in meters. Due to the pitch angle, the measured magnetic field is the combination of \(H_{\rm y}\) and \(H_{\rm z}\) of the original magnetic field, and the results in the pitch angle case are different from the two cases discussed earlier. The measured magnetic field \(B_{d}(z)\) does not meet the characteristics of origin symmetry anymore. In addition, there are obvious differences between the measured magnetic field \(B_{\rm d1}\) and the original magnetic field \(ActualB_{\rm d0}\) in the amplitude and the position corresponding to the maximum amplitude, which can cause high error in positioning methods that directly use data \(B_{\rm d1}\). In particular, the position offsets corresponding to the zero magnetic field points of receivers 1 and 2 are 74.10 cm and 48.45 cm, respectively. The offset of the zero magnetic field point will cause an unacceptable error in the traditional ELF-AGMs based on the zero magnetic field point. In contrast, \(CompensationB_{\rm d0}\) is the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DOF** & \(x\) & \(z\) & \(y\) \\ \hline MFSI method (cm) & 103.17 & 0.10 & 297.52 \\ True value (cm) & 100.00 & 0.00 & 300.00 \\ Error (cm) & 3.17 & 0.10 & \(-\)2.48 \\ Error (\%) & 3.17 & - & \(-\)0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The tracking error of the algorithm in lateral offset case.  
Figure 7: The radiated magnetic field of the transmitting coil in the lateral offset case. \(B_{\rm y1}\) and \(B_{\rm y2}\) are \(y\)-axis component of the magnetic field on the line ((1, 3, \(-\)4.5), (1, 3, 4.5)) and line ((1, 3.5, \(-\)4.5), (1, 3.5, 4.5)) in meter, respectively. \(CompensationB_{\rm y0}\) is the compensated magnetic field by MFSI method, while \(ActualB_{\rm y0}\) is the original magnetic field on the line ((1, 3, \(-\)4.5), (1, 3, 4.5)) in meter.  
compensated magnetic field by the MFSI method. It can be found that the zero magnetic field point of \(CompensationB_{\rm d0}\) has been compensated from 74.10 cm to 10.45 cm, which greatly improves tracking accuracy. The specific results of MFSI algorithm are shown in Table 3.  
Referring to (17), the pitch angle \(\alpha\) can be calculated out. Furthermore, the tracking error of DOF pitch angle is 0.3962\({}^{\circ}\), which is 15 times lower than the error of 6\({}^{\circ}\) in [30]. Furthermore, referring to (18) and (8), the DOF \(z\) and \(y\) are be tracked, whose accuracies are at least twice that of [27,30].
## 3 Numerical Scheme  
### Framework  
#### 3.1.1 Discretising the Equations of Motion  
The equations of motion can now be discretised by approximating the partial derivatives with their finite difference [17] counterparts. This method allows a full system simulation, and therefore great flexibility of control for the input parameters and gesture reproduction, at the cost of increased computational requirements. This method has seen a myriad of applications in physical modelling sound synthesis, and more generally musical acoustics simulations [3,18]. In this section, the numerical scheme is defined, the discrete energy balance is detailed, and the scheme update is described.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 4 Secure and Efficient Routing Protocol for Jellyfish Attack Prevention in V2X (SecE-V2X)
### Honesty Metrics
#### 4.2.2 Packet Loss Rate  
The packet loss rate refers to the ratio of lost packets to the total packets transmitted or received over a given time period. In order to handle Jellyfish periodic dropping attacks, Equation (2), introduced in [48], is used to calculate the packet loss rate, \(LR(n)\), of a node, \(n\):  
\[LR(n)=\frac{PRD+PSD}{PRC+PSC+PRD} \tag{2}\]  
where PSC is the number of packets sent correctly, \(PRC\) is the number of packets received correctly, \(PSD\) is the number of packets sent but dropped, and \(PRD\) is the number of packets received but dropped.
## 5 Discussion
_D\({}^{\text{AE}}\)_ actively mitigates instability early on, as indicated by its initial high magnitude, compared with negligible \(D\) activity in \(PID\), which leads to pronounced \(DK\). Regarding \(t_{p}\) and \(M_{p}\), _PI-D\({}^{\text{AE}}\)_ demonstrates a 99% reduction in \(M_{p}\), ensuring minimal overshoot and improved stability. Although occasional oscillations are present, their magnitudes are negligible, contributing to overall system robustness. The error accumulation metrics show significant improvements in three of four criteria, with only ITAE showing a slight increase, which remains inconsequential in absolute terms. In the overlap analysis of equilibrium and position control signals (Figures 18 and 19), _PI-D\({}^{\text{AE}}\)_ introduces slower transient responses and minor steady-state oscillations below SP. While this behavior slightly impacts position control, the primary focus of this study is equilibrium control. _PI-D\({}^{\text{AE}}\)_ demonstrates superior performance in stabilizing equilibrium by neutralizing \(DK\) and enhancing robustness under dynamic conditions. These results confirm its effectiveness as a robust alternative to traditional \(PID\) controllers, particularly for applications prioritizing stability and precision. In IPC systems with disturbances, as shown in Tables 9 and 10, the _PI-D\({}^{\text{AE}}\)_ controller significantly improved the control signal compared with PID and maintained low \(D\) magnitudes throughout the process. Error accumulation across all the criteria improved by over 82%, reaching up to 97% in the best case. _PI-D_ achieved steady-state control from the start, while PID failed to reach equilibrium. Although \(t_{p}\) for _PI-D\({}^{\text{AE}}\)_ was longer, the (\(M_{p}\)) overshoot was reduced by 99% relative to \(PID\). Sub-impulses in the _PI-D\({}^{\text{AE}}\)_ response were minimal and acceptable when compared with \(PID\), and the former consistently rejected oscillations and always maintained equilibrium. The _PI-D\({}^{\text{AE}}\)_ controller stands out for its structural simplicity and computational efficiency, relying on only four parameters (Kp, Ki, Kd, and an adaptive exponent _AE_) for implementation. In contrast to more-complex approaches such as FL PID controllers, which require 10 to 30 inference rules along with associated gain parameters, or NN-based PID controllers, which involve more than 20 parameters related to network architecture and training data, the _PI-D\({}^{\text{AE}}\)_ design does not require any prior training. Furthermore, while fuzzy and NN-based controllers entail medium and high computational costs, respectively, the _PI-D\({}^{\text{AE}}\)_ maintains a significantly low computational burden. Regarding performance, the _PI-D\({}^{\text{AE}}\)_ demonstrates high robustness to noise, comparable with that of NN-based controllers, yet with a considerably simpler and more direct implementation. [18, 19, 20, 21, 22, 23, 24, 25]. These features make the _PI-D\({}^{\text{AE}}\)_ a highly effective and practical alternative for control systems in which operational efficiency and noise immunity are of critical importance.
# Scattering Reduction and Resonant Trapping of Flexural Waves: Two Rings to Rule Them
## 2 Analytical Methods
### Scattering Reduction of Flexural Waves in a Thin Plate by a Finite Cluster of Masses
#### 2.1.2 Scattering by an Inertial Cluster  
The total flexural displacement field has the form  
\[u(\mathbf{x})=u_{\mathrm{inc}}(\mathbf{x})+\sum_{j=1}^{N}\gamma_{j}G(\beta,|\mathbf{x}-\bm {A}_{j}|), \tag{4}\]  
where the coefficients \(\gamma_{j}\) (\(j=1,\ldots,N\)) are connected to the inertia of the point masses as follows:  
\[\gamma_{j}=-D^{-1}m_{j}\omega^{2}u(\mathbf{A}_{j}). \tag{5}\]  
In particular, when the scattering is produced by the single point mass \(M\) positioned at the origin, we have  
\[u(\mathbf{x})=\mathrm{i}\delta U\beta^{2}G(\beta,|\mathbf{x}-\mathbf{X}^{(0)}|)-D^{-1}M \omega^{2}u(\mathbf{O})G(\beta,|\mathbf{x}|), \tag{6}\]  
and hence the amplitude of the time-harmonic vibrations at the origin is  
\[u(\mathbf{O})=D\frac{\mathrm{i}\delta U\beta^{2}G(\beta,|\mathbf{X}^{(0)}|)}{D+M \omega^{2}G(\beta,\mathbf{O})}=-\frac{64\beta^{2}\rho hLG(\beta,|\mathbf{X}^{(0)}| )}{8\mathrm{i}\rho h+M\beta^{2}}. \tag{7}\]  
We also note that when \(M\to\infty\) while the frequency of the incident wave is fixed, we obtain the case of a scattering by a rigid pin positioned at the origin.  
On the other hand, for the ring-shaped finite cluster of \(N\) point masses, shown in Figure 2, with the flexural displacement \(u(\mathbf{x})\) defined by (4), the amplitudes \(u(\mathbf{A}_{j})\) (\(j=1,\ldots,N\)) of time-harmonic flexural displacements satisfy the system of \(2N\) linear algebraic equations  
\[u(\mathbf{A}_{q})=u_{\mathrm{inc}}(\mathbf{A}_{q})-D^{-1}\omega^{2}\sum_{j=1}^{N}m_{j} u(\mathbf{A}_{j})G(\beta,|\mathbf{A}_{q}-\mathbf{A}_{j}|), \tag{8}\]  
which is consistent with the classical Foldy approach for scattering problems in domains with finite clusters of point defects [34].
## 3 System Model
### Task Model  
At each discrete time slot \(t\), IoTD \(k\in\mathcal{K}\) generates an MLLM service request characterized by the tuple \(\mathcal{T}_{k}(t)=\langle u_{k}(t),o_{k}(t),T_{k}^{\max}(t),a_{k}(t)\rangle\), where \(u_{k}(t)\) denotes the multimodal input data size (bits), encompassing text, image, and video content, \(o_{k}(t)\in O_{1},O_{2},\ldots,O_{\max}\) represents the discrete output length level, determined by the service type and input characteristics, \(T_{k}^{\max}(t)\) specifies the maximum tolerable latency from task initiation to result reception, and \(a_{k}(t)\) indicates the minimum acceptable inference accuracy.  
The output length \(o_{k}(t)\), selected from the discrete set \(\{O_{1},O_{2},\ldots,O_{\max}\}\), is determined by the specific MLLM application and the characteristics of the input data \(u_{k}(t)\). For example, in translation tasks, \(o_{k}(t)\) is typically proportional to the input size \(u_{k}(t)\), reflecting the length of the translated text. In contrast, video summarization yields a fixed-length summary (e.g., a paragraph), while visual question answering produces a short fixed output (e.g., a few words). The discrete levels \(O_{1},O_{2},\ldots,O_{\max}\) are predefined based on the expected output sizes for different application types, allowing the system to accommodate varying computational and bandwidth demands.  
Given the computational limitations of IoTDs, all MLLM tasks need to be offloaded to UAVs or LEO satellites. Upon task completion, the server node transmits the processed outcomes back to the IoTD. Formally, let \(x_{k}(t)=(x_{k,m}(t),x_{k,n}(t))\) denote a binary variable for IoTD \(k\), capturing the task offloading choice at time slot \(t\). Here, \(x_{k,m}(t)=1\) signifies that IoTD \(k\) assigns the task to UAV \(m\), whereas \(x_{k,n}(t)=1\) indicates that the task is offloaded to LEO satellite \(n\), with \(x_{k,n}(t)=0\) otherwise. A key constraint ensures that, within each time slot, an IoTD's task is allocated to precisely one execution node. The subsequent constraints governing task offloading are as follows:  
\[\sum_{m\in\mathcal{M}}x_{k,m}(t)+\sum_{n\in\mathcal{N}}x_{k,n}(t)=1,\quad \forall k\in\mathcal{K}. \tag{1}\]  
Additionally, accuracy constraints must be satisfied based on the selected computing node:  
\[x_{k,m}(t)\cdot A_{m}+x_{k,n}(t)\cdot A_{n}\geq a_{k}(t),\quad\forall k\in \mathcal{K}. \tag{2}\]  
While IoTD locations and wireless channels remain stable within each time slot, inter-slot variations arise from device mobility and environmental dynamics, necessitating adaptive resource allocation strategies.
## 3 Detection of Damage Occurrence
### Time Domain Methods  
It is well known that the measurements are always made in the time domain, and therefore it is straightforward to use the time domain response for damage detection. The measurement contains  
Figure 1: Flowchart of damage identification.  
abundant information about the structure and preserves the possible nonlinear behavior as well. In addition, it eliminates the need to perform frequency transformation and the associated errors such as leakage and truncation, and thus enhances the accuracy and practicality of the time domain approaches [15]. [PERSON] and [PERSON] [16] reviewed the time-domain methods used in the context of structural diagnosis and vibration problems.
## 3 Results
### Comparison of GPP Estimates Using Landsat/Sentinel-2 Data and MODIS Data  
In comparison to major grain crops (e.g., maize, soybean, winter wheat, and rice), only a few studies have assessed GPP estimates of sugarcane plantations from light use efficiency models with MODIS data [35; 47; 126]. GPP\({}_{\mathrm{EC}}\) estimates are widely used to assess GPP calculations from light use efficiency models [127; 128; 47]. In our study analyzing data from 2015-2017 in Brazil and 2018-2020 in the USA, the GPP estimates from the VPM simulations with MODIS images and local climate data agreed reasonably well with the GPP\({}_{\mathrm{EC}}\) data, being consistent with the results reported in [PERSON] et al. 2020 [35], where GPP estimates were evaluated using 2005-2007 data from an eddy flux tower site in Brazil and 2017 data from an eddy flux tower site in USA.  
The results of our study showed that the temporal agreement between GPP\({}_{\mathrm{EC}}\) and GPP\({}_{\mathrm{VPM-L5-S2}}\) (R\({}^{2}\) 0.82 and 0.74) was stronger than the temporal agreement between GPP\({}_{\mathrm{EC}}\) and GPP\({}_{\mathrm{VPM-MOD}}\) (R\({}^{2}\) 0.63 and 0.62), which could largely be attributed to two factors: (1) individual MODIS pixels often included multiple crop fields that have different management practices and cultivation calendars (green-up dates and harvest dates), and (2) the footprints of the eddy flux tower sites were much smaller than the MODIS pixels (500 m), corresponding well with the MODIS limitations reported in other studies [129; 130; 131] investigating the vegetation carbon uptake in crops. In comparison, vegetation indices from Landsat and Sentinel-2 images, which are used to calculate fPAR\({}_{\mathrm{chl}}\) and W\({}_{\mathrm{scalar}}\), often reflect the vegetation canopy dynamics from one crop field within the footprint of the eddy flux tower site [132; 133].  
The stronger correlations between EVI\({}_{\mathrm{LS-S2}}\) and GPP\({}_{\mathrm{EC}}\) highlighted in Section 4.1 underscore the influence of vegetation indices on the VPM performance. On the other hand, the EVI values at both an MSR and HSR displayed similar results in estimating the site-specific optimal temperature, which is a crucial parameter to enhance the accuracy of GPP estimates. These results were consistent with other studies, where authors such as [PERSON] (2022) [134] have highlighted the potential of HSR (10 m) vegetation index time series in assessing relevant agronomic parameters. In our study, the EVI was used as opposed to the NDVI given its limitations in canopies, which can become oversaturated, as with sugarcane. The results of our investigation evidenced the potential of the VPM fed with Landsat and Sentinel-2 images for estimating the GPP of sugarcane plantations under different climate zones, sugarcane varieties, and crop practices. Finally, the results underscored the potential of the VPM as a tool for crop growth monitoring in precision agriculture that addresses some of the complexity and scalability issues of typical crop models [135; 136].
# Location-Aware Measurement for Cyber Mimic Defense: You Cannot Improve What You Cannot Measure
## 2 Preliminaries
### CMD Framework  
As depicted in Figure 1, the cyber mimic defense system [9] primarily comprises six components: input agent, online set of executors, back-up executor pool, arbiter, scheduler, and output agent. During runtime, the input agent acquires input data and duplicates them to distribute among the heterogeneous executors in the online executor set. Each executor independently processes the data and produces an output. The arbiter then adjudicates the outputs of each executor based on a predefined algorithm to determine the final output. Additionally, the arbiter provides feedback on the adjudication to the scheduler, which uses this information to dynamically update the online executor set using specific strategies. The functional details of each component are described as follows.  
**Input Agent**: The input agent obtains the input, and copies and distributes it to each online executor.  
**Online executor set**: Each online executor possesses an equivalent function and operates independently to process input. It is crucial to ensure a high degree of heterogeneity among the executors to mitigate common-mode vulnerabilities effectively. Once the calculations are completed, the results are transmitted to the arbiter for further processing.  
**Backup executor pool**: The backup of online executors. The scheduler periodically or selectively chooses an instance to replace the currently active online executor set.  
**Arbiter**: The arbiter adjudicates the output results of each executor based on a predefined algorithm and provides feedback regarding any suspicious executor to the scheduler.  
**Scheduler**: The scheduler dynamically dispatches executors based on the operational status of online executors, handling tasks such as offline cleaning of suspicious executors and periodic replacement of executors.  
**Output Agent**: The output agent obtains the voting results from the arbiter, formats it if necessary, and then outputs the final result.  
With its endogenous security mechanism rooted in dynamic, heterogeneous, and redundancy strategies, cyber mimic defense establishes a spatiotemporal inconsistency scenario, preventing attackers from replicating past successes. It enhances the concealability and camouflage of the target defense scenario and behavior. Even in the event of an attack, the attacker cannot simultaneously breach all the actuators (exponential difficulty) [9], ensuring that the functions protected by the imitation system remain undisturbed and achievable. As  
Figure 1: The Framework for Cyber Mimic Defense.  
a result, cyber mimic defense gains a more robust advantage when dealing with persistent, stealthy, and high-intensity offensive and defensive scenarios, especially in the presence of uncertain threats including unknown vulnerabilities, backdoors, and viruses.
## 3 Results and Discussion
### Comparison of Settlement Amount Based on Grouting Mixing Ratio  
Figure 15 shows the amount of settlement based on the mixing ratio of the grouting chemicals, and the separation distance between the grouting and structure. The settlement amount of the structure decreased as the grouting mixing ratio increased. However, when the mixing ratio exceeded a certain value, the tendency to increase was due to the amount of subsidence. This is believed to be due to the generation of an additional load from the grouting. A comparison between the amount of settlement, based on the separation distance between the grouting chemical injection unit and the structure, showed that  
Figure 14: Grouting mixing ratio of 0.5 and separation distance of 1 cm: (a) Settlement (b) Pore Water Pressure Ratio.  
Figure 13: Grouting mixing ratio of 0.5 and separation distance of 0 cm: (a) Settlement (b) Pore Water Pressure Ratio.  
the amount of settlement of the structure decreased when the separation distance was 0 cm. When the separation distance between the grouting chemical injection part and the structure increased, the structure and grouting exhibited independent behavior; we concluded that there was no effect of suppressing the sinking of the structure or reinforcing the ground. Table 4 summarizes the blending ratio of the grouting chemicals and the amount of settlement, based on the separation distance between the grouting and structure (Figure 15).  
The results of this study confirmed that when the grouting mixing ratio was less than 0.4, the condensation strength of the grouting chemical was weak and was thus destroyed by a dynamic load. Accordingly, the minimum mixing ratio of the grouting chemical was selected as 0.4, and an equation representing the correlation between the grouting mixing ratio and settlement  
Based on the results of this study, the correlation equation between the grouting mixing ratio and the amount of sedimentation was calculated, as shown in Figure 18. As shown in the graph of Figure 18, when the ground is reinforced with grouting, it has a great effect on reducing the amount of structural settlement; from this, it can be confirmed that the ground must be reinforced. As a result of checking the correlation between the grouting mixing ratio and the structure settlement amount, when the grouting mixing ratio is at least 0.4, the settlement amount tends to converge to a similar value, which is not significantly different due to the difference in the grouting mixing ratio. However, the grouting mixing ratio is closely related to the grouting strength, and the grouting strength is an important factor in determining the ground reinforcement ability and the safety of the structure; therefore, using a minimum grouting mixing ratio of 0.45 or more is considered safe.