esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Evaluation
### Simulation Configurations  
The simulation is implemented by performing a network scan for each scenario in various samples. In our simulation, the terminals are randomly placed around the base station. We acquired 1000 patterns each for two sets of scan response samples of each scenario. Table 4 shows the simulation settings and the parameters. The number of terminals for each pattern in the two sets is 8, and the number of ports scanned are 100 and 1000, respectively.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{2}{c}{**Value**} \\  & **Set 1** & **Set 2** \\ \hline Simulation time & 300 s & 300 s \\ \hline Number of base stations & 1 & \\ \hline Number of terminals & 8 (4 each for UL / DL background traffic) & \\ \hline Terminal arrangement range & 20 m square, 250 m square around the base station & 20 m square, 200 m square around the base station \\ \hline Propagation model & ITU base coverage urban, urban macro-cell scenario [41] \\ \hline Scheduling method & Proportional fairness & \\ \hline System bandwidth & 5 MHz & \\ \hline \multicolumn{3}{c}{Background traffic} \\ \hline Applied traffic volume & 1, 8 Mb/s (per terminal) \\ \hline Occurrence size & 1250, 10,000 bytes \\ \hline Occurrence interval & 10 ms \\ \hline Packet size & 1000 bytes \\ \hline Protocol & UDP \\ \hline Occurrence start time & 1 s \\ \hline \end{tabular}
\end{table}
Table 4: Simulation settings.  
Figure 4: Target network for obtaining scan response that can be used fore cause estimation (only LTE is considered in this evaluation).  
Our simulator is capable of performing a network scan on all 65,536 ports in both UDP and TCP. On a real device, it is not all ports that are commonly used, and most ports are completely disabled or blocked by the firewall. Therefore, the complete 65,536 ports are not needed to show the performance of our method. Additionally, the performance of the proposed estimation method is not limited to the number of ports scanned. Furthermore, in this simulation, ports based on the TCP protocol are not considered. A total of 24,000 data samples (i.e., 8 terminals per pattern \(\times\) 1000 patterns \(\times\) 3 scenarios) were acquired for each of the first and second simulation sets.  
Furthermore, settings such as terminal arrangement range and background traffic volume were set to be different between scenarios and samples generated from the scan response data in different situations. The following differences were made for each scenario: set 1--(i) \"normal\"--20 m, 1 Mb/s; (ii) \"low SNR\"--250 m, 1 Mb/s; and (iii) \"congestion\"--250 m, 8 Mb/s. Set 2-- (i) \"normal\"--20 m, 1 Mb/s; (ii) \"low SNR\"--200 m, 1 Mb/s; and (iii) \"congestion\"--200 m, 8 Mb/s. In the \"low SNR\" scenario, only a specific terminal is fixed at a distance from the eNB, so that the communication quality of a certain terminal deteriorates due to large propagation loss. Furthermore, the simulation setting is configured such that the signal-to-noise ratio for the low SNR is in the range between 3-10.5 dB.
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 4 Experimental Setup
### Classical Machine Learning Methods  
Classical machine learning techniques offer a straightforward approach to model development, often requiring minimal hyperparameter tuning. In this study, we evaluated representative models from three algorithmic families: tree-based, distance-based, and shallow neural networks. Specifically, we selected a Random Forest classifier, a k-Nearest Neighbors (kNN) classifier, and a simple multilayer perceptron (MLP).  
A preliminary analysis of classical machine learning methods for UXO classification was presented in [9]. Unfortunately, that study utilized an extended feature set, including all three components of the magnetic field vector (X, Y, Z--three orthogonal components, measured along the three axes of a 3D Cartesian coordinate system), and assumed the absence of remanent magnetization in UXO objects. This simplification--particularly the omission of remanent magnetization--can lead to an overestimation of classification accuracy. In contrast, the present study focuses on an updated dataset that accounts for remanent magnetization, resulting in reported accuracies that are significantly lower than those in [9]. Moreover, since UXO surveys often employ cesium vapor-based magnetometer sensors that provide only the scalar magnitude of the magnetic field, defined as \(M=\sqrt{X^{2}+Y^{2}+Z^{2}}\), our evaluation is constrained to using this scalar value alone.  
All classical models were implemented using the scikit-learn library (version 1.6.1). Table 2 outlines the parameter settings that differ from default values. Model evaluation was conducted with five-fold cross-validation, as detailed in Section 4.5.
## 2 Numerical Model and Validation
### Validity Investigation  
The grid and time step independence verification were carried out because the mesh size and the time step determine the accuracy of CFD computational results. The comparisons of the global parameters for the grid and time step independences studies are shown in Tables 1 and 2. In the tables, \(N_{c}\) is the number of nodes for \(1/4\) circle, \(N_{mesh}\) is the total number of the mesh, and \(\Delta t\) is the time step. The global parameters include the fluctuating lift coefficient \(C_{d}^{\prime}\), the mean drag coefficient \(\overline{C}_{d}\), the fluctuating drag coefficient \(C_{d}^{\prime}\), the Strouhal number \(S_{t}\), and the minimum, mean and maximum values \(y_{min}^{+}\), \(\overline{y}^{+}\) and \(y_{max}^{+}\), respectively. In order to guarantee the correctness of the turbulence simulation results and sufficient amount of mesh in the viscous sublayer, the \(y_{max}^{+}\) should be less than 11.63 which is the demarcation point between the log-law region and the viscous sublayer recommended by Versteeg and Malalasekera [PERSON] (2018).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(N_{c}\) & \(N_{mesh}\) & \(\Delta t\)(s) & \(\overline{C}_{d}\) & \(C_{d}^{{}^{\prime}}\) & \(C_{l}^{{}^{\prime}}\) & \(S_{t}\) & \(y_{min}^{+}\) & \(\overline{y}^{+}\) & \(y_{max}^{+}\) \\ \hline
100 & 82,456 & \(2.5\times 10^{-4}\) & 1.0999 & 0.0873 & 0.8329 & 0.267 & 0.745 & 8.331 & 16.232 \\
150 & 92,304 & \(2.5\times 10^{-4}\) & 1.3675 & 0.0949 & 0.9927 & 0.249 & 0.442 & 5.589 & 11.405 \\
200 & 101,504 & \(2.5\times 10^{-4}\) & 1.3420 & 0.0884 & 0.9702 & 0.243 & 0.334 & 4.116 & 8.389 \\
250 & 111,600 & \(2.5\times 10^{-4}\) & 1.2674 & 0.0812 & 0.9211 & 0.243 & 0.247 & 3.187 & 6.902 \\
300 & 120,594 & \(2.5\times 10^{-4}\) & 1.2224 & 0.0822 & 0.8956 & 0.243 & 0.184 & 2.580 & 5.750 \\ \hline \end{tabular}
\end{table}
Table 1: Comparisons of the global parameters for the grid independence study.  
Figure 2: Grid distribution and computational domain near the cylinder surface.  
Through comparison and analyzation of the global parameter results, \(N_{c}~{}=~{}250\) and \(\Delta t~{}=~{}2.5\times 10^{-4}\) s were adopted in the final results discussion. Current results accompanied with some previous simulation and experimental results [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36] for the flow around a single cylinder are shown in Figure 3. The mean drag coefficient \(\overline{C}_{d}\) versus Reynolds is shown in Figure 3a, and it can be observed that \(\overline{C}_{d}\) is very close to previous results at similar Re range. The \(S_{t}\) in the previous 3D flow experiment is less than that in this paper at similar Re range. In the present 2D numerical simulation, the computational domain height is extraordinarily small, so the 3D flow in spanwise direction cannot be fully developed. Thus, the present \(S_{t}\) results are closer to the 2D flow results [28; 34; 35], as shown in Figure 3b. From the previous comparison, it can be concluded that the present numerical model and grids are reliable and have good reference value for further research of TWW flow control.
# Low-Latency Bit-Accurate Architecture for Configurable Precision Floating-Point Division
## 5 Results and Comparisons
### Functional Verification  
The functional verification of the proposed FP division unit is carried out using 5-millions random test cases for normal-normal, normal-subnormal, subnormal-normal, and subnormal-subnormal operands combination, along with the other exceptional case verification for quadruple mode.  
The proposed Predict-Correct FP division unit with QP produces a maximum of 1-ULP (unit at last place) precision loss. The statistical correct rate of the proposed divider in the case of the 5-millions random QP test cases is 99.996% compared against Bigfloat data results using Python.
## 2 Preliminaries
### Schnorr Signature  
The Schnorr signature [20] is a digital signature scheme based on the discrete logarithm problems proposed by [PERSON] in 1991. It has the characteristics of simplicity, efficiency, and security, and is widely used in many security protocols. The core of this algorithm is to generate a pair of signatures using the user's private key and public key, which can prove that the sender of the message has the corresponding private key without exposing the private key itself. The algorithm consists of three main steps: the setup phase, signature generation, and verification.  
1. Setup: Assume group \(\mathbb{G}\) is a cyclic group of order \(n\). The signer chooses a private signing key \(x\) from the set \(\mathbb{Z}_{n}\). The public verification key is \(y=g^{x}\).  
Signature generation: The signer selects a random number \(k\), calculates the temporary public key \(R=g^{k}modp\), and then computes the hash values, \(e=Hash(R||m)\) and \(s=k-xe\), to generate the signature pair \((s,e)\), where \(g\) is a generator of the group \(\mathbb{G}\), and \(k\) is an element of the set \(\mathbb{Z}_{n}\).
3. Signature verification: The verifier confirms the validity of the signature by calculating the hash value \(e=Hash(R||m)\) and verifying whether \(g^{s}g^{y}modp\) is equal to \(R\).
# Effect of PODE on Emission Characteristics of a China VI Heavy-Duty Diesel Engine
## 3 Results and Discussions
### Emission Characteristics of PN-NOx  
For diesel engines, conventional control measures of NOx emissions usually lead to the increase in soot emissions due to the inherent trade-off relationship between these two pollutants [20,28,29]. Some previous studies have pointed out that the addition of PODE increased NOx emissions [8,12] due to the presence of PODE, higher temperatures and oxygen-richness in the combustion chamber. However, there are also other studies that have reported that lower NOx emissions were obtained for blended fuels, especially for low and medium loads [5,14,15], as a result of the higher cetane number and reduced lower heating values of the blends. Interestingly, [PERSON] et al. [30] found that the trade-off between the NOx and PM emissions was significantly improved by blending PODE at 0% EGR, 1900 r/min speed, low and high loads. However, this issue at different EGR rates has not been reported yet. As a result, Figure 12 plots the PNC-NOx trade-off relationship for different test fuels under various EGR rates (0%, 5%, 10%, 15% and 20%) with 1700 r/min speed and 50% load.  
As illustrated in Figure 12, the PN-NOx emission characteristics of the engine fueled with four different fuels, in which the ordinate represents the PN emissions and the abscissa represents the NOx emissions. It can be seen that the use of DP10 can achieve a substantial reduction in PN emissions and a slight reduction in NOx emissions at the same time. The use of DP20 further reduces PN and NOx emissions. Even though the use of DP30 leads to higher PN emissions, NOx emissions are still lower than diesel. When  
Figure 12: Trade-off between NOx and PN emissions.  
adopting DP30 and EGR rate of 10%, NOx emission is 567 ppm that is 51.7% lower than that of diesel. The reduction in PN emissions due to the addition of PODE is in line with expectations. Firstly, the dilution effect of PODE decreased the concentration of aromatics, i.e., the precursor of soot formation [31]. Secondly, PODE has higher oxygen content, resulting in a reduction in soot formation [9]. Thirdly, due to the lack of a C-C bond, the soot-free nature of the PODE significantly reduces the formation of PAHs. For NOx reduction, the greater cetane number of PODE shortens the ignition delay, resulting in a lower premixed combustion ratio [30]. On the other hand, the reduction in combustion time and peak heat release rates also resulted in a fall in the mean in-cylinder gas temperature (as shown in Figure 7), which aided NOx generation reduction.
# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 2 Related Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Manage Natural** & **Authors** & **Problem** & **Main Work** \\ \hline \multirow{2}{*}{Remove noises} & [PERSON] et al. (2006) [12] & Data sparsity & Detect noises and remove them \\  & [PERSON] et al. (2013) [26] & Data sparsity & Detect noises and remove them \\ \hline \multirow{7}{*}{Correct noises} & [PERSON] et al. (2009) [22] & Depending on additional information & Re-rating \\  & [PERSON] and [PERSON] (2013) [13] & Depending on additional information & Interactive recommender systems \\  & [PERSON] et al. (2014) [14] & Time-consuming & Re-predicting \\  & [PERSON] et al. (2016) [23] & Time-consuming & Re-predicting \\  & [PERSON] et al. (2017) [19] & Time-consuming & Re-predicting \\  & [PERSON] et al. (2017) [24] & Time-consuming & Re-predicting \\  & [PERSON] et al. (2018) [25] & Time-consuming & Re-predicting \\  & [PERSON] et al. (2019) [20] & - & Threshold value \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing works about managing natural noises. values weighted by probabilities. Experimental results on some real datasets indicate that our proposed scheme presents a significant improvement on managing natural noises.
## 4 Discussion
### Future Research Directions
By transforming farm management into an information- and knowledge-driven business, these systems aim to optimize agricultural productivity while minimizing environmental impacts. \"Smart Farming\" leverages crop-growth models and remote sensing data to make precise decisions on crop selection, sowing schedules, fertilizer and pesticide applications, and harvesting times. This approach enables efficient, location-specific management of fields, contributing to both sustainable agriculture and food security [200]. * Embedding climate adaption considerations into the design and interpretation of RS datasets will enhance their relevance for addressing climate resilience challenges. * Establishing new publicly accessible reference databases, such as the World Cereal database provided by ESA, will facilitate broader sharing of high-quality reference data for future research and applications [212].
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 3 Methodology
### Loss Function
#### 3.4.1 Point-Line Extraction Loss  
In the training stage of the point and line extraction branch, the output included the root node's confidence map, key-point map, and displacement map. The losses of these three tasks were combined into Equation (32), defined as follows:  
\[L_{PLE}=L_{root}+L_{p}+L_{dis} \tag{32}\]  
The ground truth of the root-point confidence map was constructed by marking the root-point positions on a zero map. A weighted binary cross-entropy loss \(L_{root}\) was used to supervise this task, which was defined as:  
\[L_{root}=-\sum_{i}\widetilde{R}_{i}\log R_{i}+(1-\widetilde{R}_{i})\log(1-R_{ i}) \tag{33}\]where \(\widetilde{R}_{i}\) and \(R_{i}\) are the prediction and true label of the root node of the line segment, respectively. The true value of the key point was marked with the ORB feature. \(L_{p}(X,Y)\) was defined as:  
\[L_{p}(X,Y)=\frac{1}{H_{c}u_{c}}\sum_{l}l_{p}\Big{(}T^{ij},\widetilde{T^{ij}} \Big{)} \tag{34}\]  
\[l_{p}\Big{(}T^{ij},\widetilde{T^{ij}}\Big{)}=-\log(\frac{\exp(T^{ij}_{k})}{ \sum_{1}^{64}\exp(T^{ij}_{k})}) \tag{35}\]  
The displacement part of the line segment relative to the root node was used to locate the length and angle of the line segment, which used the L1 loss and L1 smoothing loss, respectively defined as:  
\[L_{dis}=\sum_{i=1}^{m}\Bigg{\{}\begin{array}{ll}\big{|}\theta^{i}-\hat{ \theta}^{i}\big{|}+0.5*(\rho^{i}-\hat{\rho}^{i})^{2}&\quad if\big{|}\rho^{i}- \hat{\rho}^{i}\big{|}<1\\ \big{|}\theta^{i}-\hat{\theta}^{i}\big{|}+\big{|}\rho^{i}-\hat{\theta}^{i} \big{|}-0.5&\quad otherwise\end{array} \tag{36}\]  
where \(\theta^{i}\) and \(\rho^{i}\) represent the actual line segment's length and angle, and \(\hat{\theta}^{i}\) and \(\hat{\rho}^{i}\) are the predicted line segment's length and angle, respectively.
# Three-Dimensional Reconstruction-Based Vibration Measurement of Bridge Model Using UAVs
## 2 Methods
### Three-Dimensional Reconstruction Method  
As shown in Figure 1, the vibration measurement based on 3D reconstruction is composed of three primary steps. Step 1 is to track displacement by DIC which is the same as Section 2.2. Step 2b is the UAV camera calibration, through which the projection matrices of the UAV camera can be estimated. Step 3b is to remove the false displacement induced by UAV motion to obtain the actual displacement by combining the output data from Step 1 and Step 2b.  
Figure 3: Homography transformation between frames.
## 3 Our Dedicated and Scalable Multiplier Architectures
### Dedicated Polynomial Multiplication Architectures  
For NIST-defined binary fields over \(GF(2^{163})\) to \(GF(2^{571})\), our digit-parallel polynomial multiplication architectures are illustrated in Figures 2-6. All our proposed dedicated multipliers take two \(m\)-bit polynomials as input and result in a \(2\times m-1\)-bit polynomial as output. The value of \(m\) determines the underlying elliptic curve field (i.e., 163, 233, 283, 409 and 571). Further descriptions of these multipliers are given below.  
Figure 1: Flowchart of our dedicated and scalable multiplier architectures.  
Figure 4: Digit-parallel multiplier over \(GF(2^{283})\).  
Figure 3: Digit-parallel multiplier over \(GF(2^{233})\).  
Figure 2: Digit-parallel multiplier over \(GF(2^{163})\).  
In order to perform a polynomial multiplication over \(GF(2^{m})\), our dedicated and scalable architectures take two \(m\)-bit polynomials, i.e., \(A(x)\) and \(B(x)\), as input and result in an \(m\)-bit polynomial (i.e., \(C(x)\)) as output. It is important to note that for digit-level polynomial multiplications, we have created digits of a polynomial \(B(x)\), while \(A(x)\) is considered with \(m\) bit. Therefore, a total of five 32-bit digits and one 3-bit digit are required for the implementation of a polynomial multiplication over \(GF(2^{163})\), as given in Figure 2. Similarly, as shown in Figures 3-6, a total of eight (seven with 32-bit and one digit with 9-bit), nine (eight with 32-bit and one digit with 27-bit), thirteen (twelve with 32-bit and one digit with 25-bit) and eighteen (seventeen with 32-bit and one digit with 27-bit) digits are required for polynomial multiplication over \(GF(2^{m})\) with \(m=233,283,409\) and 571. After creating the digits of a polynomial \(B(x)\), each digit of a polynomial \(B(x)\) is multiplied with an input polynomial \(A(x)\). The used M1 to M18 multipliers in Figures 2-6 perform the polynomial multiplication and result in an output polynomial depending on the lengths of the used inputs. For example, M1 to M5 multipliers (in Figure 2) result in output polynomials of length \(32+163-1\) bits, while M6 results in \(3+163-1\) bits. This is also applicable to the remaining dedicated and scalable multiplication architectures. After the multiplication of each 32-bit digit with an \(m\)-bit input polynomial \(A(x)\), a concatenation is applied to generate a resultant polynomial of length \([(2\times m)-1]\) bits. A resultant polynomial of \(m\)-bit size is generated by employing a NIST-recommended reduction algorithm (shown in Algorithms A1-A5).
# Research Progress on the Influence of Thermo-Chemical Effects on the Swelling Pressure of Bentonite
## 4 Effect Mechanism of Swelling Properties  
Generally, the mechanistic explanation of macroscopic phenomena ultimately requires microscopic-scale evaluations. Thermo-chemical effects will have a great impact on the microstructure of the soil, and as a consequence, it is essential to consider the variations occurring in the microstructure of bentonite with the hydration process when describing the expansion behavior of bentonite. Aside from pore distribution, another important factor affecting the microstructure of soil is water distribution in the pores, which has a strong control effect on the mechanical and seepage characteristics of unsaturated soil.
## 4 Local Updates to Handle Mobility of Sensors for Geocasting
### Convex Hull and HCL Propagation After MN Changes Its Parent  
If the hop count level (HCL) of MN changes after the new parent selection, MN sends its new HCL (in HCL_UPDATE message at time t20 in Figure 5) to its current child nodes. The child nodes also update their HCLs to the sink. This HCL update message is propagated (at time t21 in the figure) until it reaches to the leaf nodes of the branch. Also, MN sends a HELLO_TO_PARENT message including it convex hull to its new parent. The parent node receiving a HELLO_TO_PARENT message recalculates its convex hull. The convex hull changes are propagated up the tree until the changes reach to the sink.  
After receiving the first beacon packets from MN, child nodes and the parent node of MN start to wait for another MN beacon packet for a beacon interval. In the following, we explain the actions executed by the parent and child nodes of MN when there is no other beacon from MN.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Fec
#### 5.1.2 FEC for WSN
However, most of the previous work on the design of ECSs and energy analysis is focused on a multi-hop WSN model and very limited research work conducted on a modified multi-hop WSN model. Therefore, researchers need to focus on design and energy analysis of ECSs on modified multi-hop WSN model to cover realistic network situation. Another key finding is that there is a lack of enough experimental results using currently available WSN node.