esa-sceva/satcom-chunk-collection huggingface dataset:
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 6 Performance Evaluation
### Proactive Experimental Results
The results show a minimal initial deployment time in the E2E domain, with more significant setup requirements in the UMU and TID domains due to their complex, resource-intensive tasks, such as 5G core deployment, channel protection, and active monitoring, achieving a full B5G network deployment in under 300 s. It is important to note that the enforcement time for critical components like the I2 NSF agent and STA varies due to dependency constraints, impacting overall readiness. These findings demonstrate the framework's potential to streamline multi-domain security deployment and suggest opportunities to further reduce deployment times through optimized resource allocation, parallel task execution, and container-based deployments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Process** & **Percentage of Time** \\ \hline UMU SMD & Total time & 180 s \\  & Deployment time & 36\% \\  & I2 NSF agent (startup) & 55\% \\  & I2 NSF agent (configuration) & 3.32\% \\  & Enforce PoT & 3.97\% \\ \hline TID SMD & Total time & 95 s \\  & Deployment time & 55\% \\  & STA (startup) & 26\% \\  & STA (configuration) & 15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage distribution of time for UMU and TID SMDs. Figure 10: E2E B5G slice deployment and configuration initial time. (**a**) Use case initial time; (**b**) time consumed per procedure per SMD.
## 7 Experimental Results and Analysis
### Avalanche Effect Test  
The \"avalanche effect\" describes a state of unstable equilibrium. Changing the plaintext or SK may result in a significant change in the ciphertext. Suppose a block cipher or cryptographic hash function lacks significant avalanche characteristics. In that case, it may result in a lack of good randomness, making it impossible for a cryptanalyst to accurately predict the input information based on the output information. As a result of this situation, the algorithm may be partially or even wholly corrupted. Therefore, it is critical to ensure that the development of cryptographic algorithms and devices can effectively resist the avalanche effect.  
An essential research task is establishing a key and sequence that can effectively withstand the avalanche. As technology has evolved, block ciphers and hash functions are no longer limited to products and blocks of data, making both methods of critical importance for securing data. Because of these unique features, even the slightest changes can be quickly captured by the algorithm so that each digit of the output will significantly impact each digit of the input until the algorithm is completed.  
Based on this, this paper explains the \"avalanche effect\" of the traditional DES algorithm and chunking algorithm in terms of changing one plaintext and one key. In the \"avalanche effect\" experiment, we used a slight difference in the plaintext or secret key to update the position of a piece of data and carefully explained the steps of the experiment and the final results. In addition, by analyzing multiple sets of data, the chance factor in the experiment can be effectively reduced, and the accuracy of the \"avalanche effect\" algorithm can be improved by changing the position of the plaintext or the key.  
Figure 12: Encryption length result test.
# Time-Dependent Retarded Microwave Electromagnetic Motors
## 4 Example  
Let us consider a set of two functions  
\[f^{1}(t)=\sin\phi_{1}(t),\qquad f^{2}(t)=\sin\phi_{2}(t) \tag{26}\]  
in which \(\phi_{1}(t)\) and \(\phi_{2}(t)\) are arbitrary time-dependent phases. We use Equation (23) to choose corresponding \(g\) functions:  
\[g^{1}(t)=\tau\dot{\phi}_{1}(t)\cos\phi_{1}(t),\qquad g^{2}(t)=\tau\dot{\phi}_ {2}(t)\cos\phi_{2}(t). \tag{27}\]  
We shall assume two subsystems each described by only one component in the sum:  
\[\vec{J}_{1}(\vec{x},t)=\vec{J}_{1}\vec{J}_{1}^{1}(\vec{x})g^{1}(t),\qquad\vec{ J}_{2}(\vec{x},t)=\vec{J}_{2}\vec{J}_{2}^{2}(\vec{x})g^{2}(t). \tag{28}\]  
When taking into account Equation (25), it follows that  
\[\rho_{1}(\vec{x},t)=\bar{\rho}_{1}\rho_{1}^{1}(\vec{x})f^{1}(t),\qquad\rho_{2} (\vec{x},t)=\bar{\rho}_{2}\rho_{2}^{2}(\vec{x})f^{2}(t). \tag{29}\]Only the following terms in Equations (16) and (17) are nonzero:  
\[\vec{\Gamma}^{21}=\frac{1}{2h^{6}}\int d^{3}x_{1}d^{3}x_{2}\rho_{1}^{1}\rho_{2}^{2 }\frac{\vec{x}_{1}-\vec{x}_{2}}{|\vec{x}_{1}-\vec{x}_{2}|}, \tag{30}\]  
\[\vec{\Lambda}_{21}^{21}\equiv\frac{1}{h^{5}}\int d^{3}x_{1}d^{3}x_{2}\frac{\rho _{2}^{2}\vec{j}_{1}^{1}}{|\vec{x}_{1}-\vec{x}_{2}|},\qquad\vec{\Lambda}_{12}^{ 12}\equiv\frac{1}{h^{5}}\int d^{3}x_{1}d^{3}x_{2}\frac{\rho_{1}^{1}\vec{j}_{2} ^{2}}{|\vec{x}_{1}-\vec{x}_{2}|}. \tag{31}\]  
Assuming that \(\tau_{1}=\tau_{2}\), it follows from Equation (19) that \(\vec{\tau}=\frac{\tau_{1}}{2}\), and we shall take \(\tau=\tau\). Also, it follows from Equation (19) that  
\[\vec{\Lambda}^{12}=\frac{1}{2}\vec{\Lambda}_{12}^{12},\qquad\vec{\Lambda}^{2 1}=\frac{1}{2}\vec{\Lambda}_{21}^{21}. \tag{32}\]  
Inserting the above results into Equation (20) will yield  
\[\vec{P}(t)=\frac{\mu_{0}}{4\pi}Q_{1}Q_{2}\bigg{[}\vec{\Gamma}^{21}(f^{2} \partial_{t}f^{1}-f^{1}\partial_{t}f^{2})-\frac{1}{\tau}\vec{\Lambda}^{21}f^{ 2}g^{1}-\frac{1}{\tau}\vec{\Lambda}^{12}f^{1}g^{2}\bigg{]}. \tag{33}\]  
Let us make the following assumptions. First, we assume that the phases differ by a constant value \(\Delta\phi\). This means that the derivatives of the phases are the same. By taking into account Equations (26) and (27), we thus obtain  
\[\vec{P}(t)=\frac{\mu_{0}}{4\pi}Q_{1}Q_{2}\dot{\phi}\Big{[}\vec{\Gamma}^{21} \sin\Delta\phi-\vec{\Lambda}^{21}\sin\phi_{2}\cos\phi_{1}-\vec{\Lambda}^{12} \sin\phi_{1}\cos\phi_{2}\Big{]}. \tag{34}\]  
The \(\Gamma\) and \(\Lambda\) constants are determined by the distribution of charges and currents. Let us consider two thin wires of current, as depicted in Figure 3.  
In this case, we obtain  
\[g^{1}(t)=\tau\dot{\phi}_{1}(t)\cos\phi_{1}(t),\qquad g^{2}(t)=\tau\dot{\phi}_ {2}(t)\cos\phi_{2}(t). \tag{27}\]0 \[g^{1}(t)=\tau\dot{\phi}_{1}(t)\cos\phi_{1}(t),\qquad g^{2}(t)=\tau\dot{\phi}_ {2}(t)\cos\phi_{2}(t). \tag{27}\]1  
in which \(\delta\) is Dirac's delta function, \(u\) is a step function, and \(\hat{x}\) is a unit vector in the \(x\) direction.
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 7 Simulation Results
### Mean Square Error (MSE)
Figure 10a,b show the comparison of MSE performances of the proposed overlap banded LMMSE technique with traditional and proposed interpolation methods for different subvector values (i.e., S = 10, 20, 30, and 40) to determine the optimum operating region of the channel estimator for both TU and EPA channels, respectively. The number of subvectors will be decided depending on the available bandwidth in the NB-IoT downlink. It is observed that the MSE performance increases up to a level for a certain number of increased subvector values. In each scenario,the proposed overlap banded SVD LMMSE channel estimation method with the proposed polyphase decomposed FIR filter interpolation exhibits better performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **TU 1 Hz Channel** & **EPA 1 Hz Channel** \\ \hline LS [30] & \(-2.1\) & \(-1.1\) \\ LMMSE [44] & \(-8.4\) & \(-8\) \\ SVD LMMSE & \(-7.2\) & \(-7\) \\ Banded SVD LMMSE & \(-5\) & \(-4\) \\ Overlap banded SVD LMMSE with & \(-6.3\) & \(-5.9\) \\ conventional interpolation (Proposed) & \(-7.1\) & \(-6.9\) \\ Overlap banded SVD LMMSE with proposed & \(-7.1\) & \(-6.9\) \\ interpolation (Proposed) & \(-7.1\) & \(-6.9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Received SNR (dB) at MSE = \(10^{-3}\) for different channel estimators. Figure 10: Mean square error (MSE) comparison of the proposed technique with conventional and proposed interpolation methods for different subvector values: (**a**) TU 1 Hz; (**b**) EPA 1 Hz. Figure 9: Mean square error (MSE) comparison of the proposed technique for conventional and proposed interpolation methods with traditional LMMSE and its other counterpart versions: (**a**) TU 1 Hz; (**b**) EPA 1 Hz.
## 2 pH Control Equipment and Control Model
### Analysis of the Water and Fertilizer pH Adjustment Process  
In order to obtain the water and fertilizer mixture, fertilizer and water are fed into the mixing tank on a cyclical basis. The configuration of aqueous fertilizer mixing solutions requires the periodic opening and closing of valves to feed fertilizer and water into the mixing tank. In the actual fertilizer mixing process, in addition to the process of agitation and mixing, delayed factors such as changes in the flow rate of the liquid in the pipeline and the use of the monitoring module can introduce time lags into the pH adjustment.  
Both water and fertilizer are usually weakly alkaline; thus, an acidic regulating liquid is required to adjust the pH. When the three liquids of water, fertilizer and a regulating liquid, are mixed, it can be seen as a neutralization process between a strong acid and a weak base. The pH of the fertilizer in the mixture is made to be similar to the pH set by the system. The inherent nonlinearity of the acid-base neutralization process also has an impact on the pH adjustment process. As the pH adjustment in the actual fertilizer mixing process is influenced by a variety of variable disturbances, a simplified control model is developed to analyze it qualitatively from a mechanistic point of view. It is assumed that the fertilizer drum is fed with raw fertilizer, acid and pure water as inputs to the pH model so that the fertilizer is rapidly integrated with the water and that the concentration of the mixed solution is used as an output, and that the mixed fertilizer in the drum is always full and of the same concentration at the top and bottom, as shown in Figure 2.  
According to the material balance principle and the law of atomic conservation, the equilibrium state of the system should comply with the conservation of fertilizer and volume conservation, as to construct the soil conductivity and acidity concentration control process equation.  
\[\left\{\begin{aligned} V\frac{dH_{1}}{dH}=L_{a}H_{a}-L_{1}H_{1}\\ V\frac{dx_{2}}{dH}=L_{n}H_{n}+L_{w}H_{w}-L_{1}H_{2}\\ L_{1}=L_{n}+L_{w}+L_{a}\\ 10^{-pH}-10^{pH-14}-H_{1}+\frac{1}{1+10^{Pb_{b}+pH-14}}H_{2}=0 \end{aligned}\right\} \tag{1}\]  
where \(V\) is the volume of mixed liquid, \(L_{a}\) is the acid input flow, \(C_{a}\) is the acid concentration, \(L_{n}\) is the fertilizer input flow, \(H_{n}\) is the fertilizer concentration, \(L_{w}\) is the water input flow, \(H_{w}\) is the water input concentration, \(L_{out}\) is the output flow of the system, \(H_{1}\) is the concentration of acid in the mixture, \(H_{2}\) is the concentration of alkali in the mixture, \(K_{b}\) is the weak-base ionization equilibrium constant, \(pK_{b}=-\log(K_{b})\), pH is the output variable of the process, \(K_{b}\) is the ionization constant of the weak base and \(K_{w}\) is the ionization constant of water (\(10^{-14}\)).  
Figure 2: Fertilizer pH adjustment concentration analysis chart.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 4 Simulation
### Performance in an Ideal Multipath Channel  
In this subsection, we evaluate the performance of GOCDM in an ideal multipath channel. The simulation is based on a measured deep-sea shadow-zone communication channel, as depicted in Figure 4. Since this subsection focuses solely on an ideal multipath channel, time-varying effects are not considered. We simulate two cases with subcarrier counts of 1024 and 2048, respectively. When the number of subcarriers is 2048, the cyclic prefix is longer than the channel's multipath delay, ensuring an ideal multipath channel condition. Conversely, when the number of subcarriers is 1024, the cyclic prefix is shorter than the channel's multipath delay, allowing for a comparison of different modulation schemes under conditions where the cyclic prefix is insufficient.  
The results are presented in Figure 5. From Figure 5a, it can be observed that GOCDM exhibits performance similar to OCDM. This is because GOCDM inherits the advantages of conventional OCDM, where subcarriers achieve full frequency diversity, as theoretically analyzed in Section 3. In Figure 5b, when the cyclic prefix is insufficient, both GOCDM and OCDM experience significant performance degradation, while OFDM shows less degradation. This is because, compared to OCDM, OFDM demonstrates better robustness in frequency-selective fading conditions with poor channel estimation.  
Additionally, in Figure 5b, the BER curves of OCDM and GOCDM do not coincide. When the cyclic prefix is insufficient, the leading portion of each data block is affected. As \(N\) decreases, the proportion of original data impacted by GOCDM-modulated data decreases; however, the severity of the impact increases. Particularly when \(M\) equals \(K\), GOCDM degenerates into SC-FDE, where the initial few information bits of each data block suffer severe interference. At lower signal-to-noise ratios, the SNR constitutes the dominant factor affecting bit error rates, rendering the impact of different \(M\) values on BER negligible. Conversely, at higher SNR levels, inter-symbol interference dominates as the primary error contributor. As a single-carrier modulation scheme, SC-FDE experiences the most severe inter-symbol interference, consequently exhibiting a higher BER. Therefore, selecting an appropriate \(N\) based on the specific multipath delay characteristics is crucial. The constellation diagrams for \(K\) = 2048 and an SNR of 25 dB are shown in Figure 6. From these diagrams, it is evident that both OCDM and GOCDM offer superior resistance to multipath interference compared to OFDM.  
Figure 4: Measured deep-sea shadow zone channel multipath.  
Figure 5: The BER of OFDM, OCDM, SC-FDE, and GOCDM: (**a**) \(K\) = 2048, and (**b**) \(K\) = 1024.  
Figure 6: \(Cont\).
# Thermodynamics of Plutonium Monocarbide from Anharmonic and Relativistic Theory
## 2 Computational Methods
### CALPHAD Method  
We apply the thermodynamic CALPHAD technique to compute the heat of formation, heat capacity, and Gibbs energy of PuC as functions of temperature. Our first-principles thermodynamic data are compared with the CALPHAD results and, in the case of the specific heat and formation enthalpy, with the experimental data too. Importantly, CALPHAD is able to, also, interrogate the significance of the sub-stoichiometric formation of PuC\({}_{1-\text{x}}\). Generally, the fundamental objective of the CALPHAD scheme is to model the Gibbs energy of individual phases pertaining to binary and ternary systems to optimally reproduce carefully reviewed phase diagrams and thermodynamic properties. From the Gibbs energy one computes phase stability and thermodynamic properties of multi-component systems [39; 40; 41].  
Here, the CALPHAD data is compared to the first-principles modeling, but, additionally, we understand that the interaction between CALPHAD and theory improves the thermodynamic-modeling capability particularly for materials with many unknown variables. For example, ab initio results such as heats of formation can directly provide important constraints to the CALPHAD modeling framework in the absence of experimental data. Furthermore, we know that optimization of parameters and minimizing errors within in the CALPHAD technique is an inverse problem with infinite degrees of freedom [42]. As a consequence, many combinations of parameters chosen by the user can produce coinciding phase diagrams. The use of DFT-predicted properties related to the CALPHAD assessment constrains the optimization and certifies the resulting thermodynamical database, both in terms of phase stability and energetics. CALPHAD has been successful in predicting complex phase transformation in plutonium alloys [43; 44] and first-principles-informed CALPHAD assessments for actinide systems have become customary in recent years [45; 46; 47; 48].  
Specifically, for the plutonium-carbide system, we utilize the CALPHAD assessment performed by [PERSON] et al. [16] that integrates a critical review of available experimental data and previous CALPHAD assessment and apply it to calculate thermodynamic properties of ideal-stoichiometry PuC as well as PuC\({}_{0.869}\) (specific heat). In the case of the Gibbs energy, we only consider the ideal-stoichiometry PuC for an appropriate comparison with the first-principles results.
## 4 System Methodology
### Proposed System Workflow
#### 4.2.1 Phase 1: System Initialization  
Preloaded values: \(E_{p}(a,b)\), \(p\), \(Z_{p}\), \(G\), \(H_{i}(\,)\), \(ID_{SMi}\), \(ID_{MS}\), \(HID_{SMi}\)', and \(HID_{MS}\)'. Initially, the system initiator initializes the permissioned blockchain which is constructed with the \(DC_{j}\) of AMI infrastructure. As the blockchain is permissioned, the \(DC_{j}\) are pre-selected to ensure decentralization. Before the registration, \(ID_{SMi}\) and \(ID_{MS}\) are assigned to the corresponding entities, and \(HID_{SMi}\) and \(HID_{MS}\) are pre-stored in the blockchain.  
Figure 3: Hyperledger Fabric organization structure integrated with PBFT consensus algorithm.  
Figure 4: Proposed system model architecture designed with a set of smart meters, smart contract (aka chain code) integrated Hyperledger Fabric blockchain, and MDMS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definition** \\ \hline \(DB\) & Blockchain database \\ \(SM_{i}\) & \(i\)th smart meter where \(i=1,\ 2,\ 3\dots\) \\ \(DC_{j}\) & \(j\)th data concentrator where \(j=1,\ 2,\ 3\dots\) \\ \(E_{p}(a,b)\) & A non-singular elliptic curve \(y^{2}=x^{3}\ +ax+b\ (mod\ p)\) over finite field \\ \(Z_{p}\) with \(4a^{3}+27b^{2}\
eq 0\) \\ \(p\) & A large prime number \\ \(Z_{p}\) & \(0,\ 1,\ 2,\ 3,\dots p-1,\) a finite field \\ \(G\) & A base point on \(E_{p}(a,b)\) \\ \(x.G\) & Point multiplication where \(x\in\ Z_{p}\) \\ \(\tau\) & Timestamp \\ \(H_{i}()\) & Collision resistant one-way hash function, where \(i=1,\ 2,\ 3\dots\) \\ \(H1D\) & Hashed identity \\ \(P_{r}\), \(P_{u}\) & Private and public key pair \\ \(Si_{SSMi}\) & \(SM_{s}^{\prime}\) ECDSA digital signature \\ \(L_{prr}\), \(L_{Pu}\) & Length of private and public keys \\ \(L_{sign}\) & Length of ECDSA signature \\ \(L_{\tau}\) & Length of timestamp \\ \(T_{\tau}\) & Timestamp generation time \\ \(T_{tx}\) & Transaction encryption time \\ \(T_{sigu}\) & Signature generation time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Index of notations used in this paper and their definition.
# FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot
## 3 System Implementation
### Image Preprocessing Module Design  
The method of image preprocessing is divided into histogram equalization and Gaussian filtering.  
Histogram equalization can be divided into two parts: statistics and mapping [18]. Since statistics work must wait until the image has been processed, this means that the processing method will lose the advantage of pipeline processing, which would vastly reduce the processing speed. Because the inspection robots do not have sudden changes in the lighting environment during its operation, the method of counting the grayscale histogram of this frame can be used to map the next frame. Using the histogram equalization IP core and the software algorithm designed based on this method, the processing result of the working image of the underground belt conveyor is shown in Figure 3. Compared with the previous pure-software algorithm, the method proposed in this paper only needs two clock cycles to get a result and greatly saves logic resources, but it will cause the mapped image to lose 2 gray levels, that is, the pixel gray value mapping at level 254. From the histograms, the loss of 2 gray levels can be ignored.  
The discrete mathematical expression of Gaussian filtering is:  
\[h(i,j)=\frac{1}{2\pi\sigma^{2}}e^{-\frac{(i-r)^{2}+(j-r)^{2}}{2\sigma^{2}}} \tag{1}\]  
In Equation (1): \(\sigma\)--Smoothing coefficient; \(i\)--the abscissa of the Gaussian template; \(j\)--the ordinate of the Gaussian template; \(r\)--the radius of the Gaussian template.  
Figure 3: Comparison of histogram equalization results. (**a**) Original image; (**b**) software processing result; (**c**) FPGA processing result.  
Figure 2: Line segment detection algorithm IP core.  
When using FPGA to implement Gaussian filtering, in order to save logic resources and improve processing speed, fixed-point calculations are used instead of floating-point calculations and shifts are used instead of divisions. In this paper, \(\sigma=1\), the template size is \(5\times 5\), and the coefficient in the template is the theoretical value multiplied by 256. After the calculation is completed, the effect of the coefficient is eliminated by shifting to the right by 8 bits. Due to the central symmetry of the Gaussian template, in order to improve the utilization of logic resources, the convolution process is divided into row convolution and column convolution. The constructed logic circuit is shown in Figure 4.  
The processing results of the Gaussian filtering hardware module constructed in this paper and the Gaussian filtering implemented by the software on the working image of the underground belt conveyor are shown in Figure 5. As the algorithm IP core does not copy the image frame pixels during the processing, the results will appear as black edges (image reduction), and the convolution result in Gaussian filtering will be truncated to an integer; the error can be ignored.
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 4 Evaluation
### Results
Another example is the missed cars in the top-left image. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Entire Annotated Area**} & \multicolumn{4}{c}{**Driving Corridor**} \\ \hline
**Class** & \multirow{2}{*}{**SRFF Resolution**} & \multirow{2}{*}{**Pedestrians**} & \multirow{2}{*}{**Cyclists**} & \multirow{2}{*}{**Cars**} & \multirow{2}{*}{**All**} & \multirow{2}{*}{**Pedestrians**} & \multirow{2}{*}{**Cyclists**} & \multirow{2}{*}{**Cars**} & \multirow{2}{*}{**All**} \\
**Metric** & & & & & & & & & \\ \hline _PointPillars_[9] & - & 35.8 & **65.3** & 40.2 & 47.1 & 45.5 & **85.4** & 72.2 & _67.7_ \\ \hline +_SRFF-CBAM_ & \(160\times 160\) & **36.8** & 65.0 & 36.7 & 46.2 & **47.2** & 84.3 & 69.1 & 66.9 \\ +_SRFF-CBAM_ & \(80\times 80\) & 29.7 & 60.2 & 33.8 & 41.2 & 39.0 & 73.2 & 68.5 & 60.2 \\ +_SRFF-CBAM_ & \(40\times 40\) & 30.6 & 63.1 & 35.8 & 43.2 & 43.4 & 81.4 & 68.7 & 64.5 \\ +_SRFF-CBAM_ & Comb & 32.9 & 63.3 & 36.1 & 44.1 & 43.2 & 76.9 & 67.9 & 62.7 \\ \hline +_SRFF-CSAM_ & \(160\times 160\) & 34.2 & 64.6 & 39.5 & 46.1 & 43.8 & 84.5 & 71.1 & 66.5 \\ +_SRFF-CSAM_ & \(80\times 80\) & 31.9 & 60.2 & 34.3 & 42.2 & 39.2 & 81.9 & 68.8 & 63.3 \\ +_SRFF-CSAM_ & \(40\times 40\) & 29.7 & 62.0 & 32.1 & 41.2 & 36.5 & 81.1 & 65.7 & 61.1 \\ +_SRFF-CSAM_ & Comb & 30.3 & 59.4 & 34.3 & 41.4 & 40.9 & 81.1 & 68.4 & 63.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of the influence of our SRFF module on the PointPillars [9] detection network, using different resolutions. We trained and evaluated on the VoD [5] dataset.