esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Tire force is a critical state parameter for vehicle dynamics control systems during vehicle operation. Compared with tire force estimation methods relying on vehicle dynamics or tire models, intelligent tire technology can provide real-time feedback regarding tire-road interactions to the vehicle control system. To address the demand for accurate tire force prediction in active safety control systems under various operating conditions, this paper proposes an intelligent tire force estimation method, integrating sensor-measured dynamic response parameters and machine learning techniques. A 205/55 R16 radial tire was selected as the research object, and a finite element model was established using the parameterized modeling approach with the ABAQUS finite element simulation software. The validity of the finite element model was verified through indoor static contact and stiffness tests. To investigate the sensitive response areas and variables associated with tire force, the ground deformation area of the inner liner was refined along the transverse and circumferential directions. Variance-based global sensitivity analysis combined with dimensional reduction methods was used to evaluate the sensitivity of acceleration, strain, and displacement responses to variations in longitudinal and lateral forces. Based on the results of the global sensitivity analysis, the influence of longitudinal and lateral forces on sensitive response variables in their respective sensitive response areas was examined, and characteristic values of the corresponding response signal curves were analyzed and extracted. Three intelligent tire force estimation models with different sensor-targeting configurations were established using radial basis function (RBF) neural networks. The mean relative error (MRE) of intelligent tire force estimation for these models remained within 10%, with Model 3 demonstrating an MRE of less than 2% and estimation errors of 1.42% and 1.10% for longitudinal and lateral forces, respectively, indicating strong generalization performance. The results show that tire forces exhibit high sensitivity to acceleration and displacement responses in the crown and sidewall areas, providing methodological guidance for the targeted sensor configuration in intelligent tires. The intelligent tire force estimation method based on the RBF neural network effectively achieves accurate estimation, laying a theoretical foundation for the advancement of vehicle intelligence and technological innovation.  
intelligent tire; finite element analysis; global sensitivity analysis; sensor targeting configuration; sensitive response characteristics; RBF neural network; tire force estimation
# A Study of Stray Neutron Field Measurements for the Neutron Scattering Instruments at CSNS
## 2 Instruments and Methods  
The BSS has been widely applied in spectrometry research due to its desirable characteristics, including wide energy range, isotropic response, good photon discrimination, and simple signal management. Since the maximum neutron energy can reach hundreds of MeV during the spallation reaction induced by protons with an energy of 1.6 GeV, there will be a certain proportion of fast neutrons in stray neutron fields for the scattering instruments at CSNS, for which the ERBSS has been developed. It comprises a bared \({}^{3}\)He-filled spherical proportional counter (type SP9, 2.3 Bar, Centronic Ltd., Croydon, UK), 10 high-density polyethylene (PE) shells with outer diameters of 3.0, 3.5, 4.0, 4.5, 5.0, 6.0, 7.0, 8.0, 10 and 12 inches, and three modified multi-shell spheres--4 Cu5-7, 4 Pb5-7 and 4 Pb6-8. A modified sphere consists, from inside to outside, of an inner PE shell, a metal layer, and an outer PE shell. The metallic layer was made of lead or copper because of their large (n, xn) cross-section for high-energy neutrons, and it was sandwiched between the PE shells to smooth the structures in the response functions resulting from resonances in the cross-section.  
Figure 1 illustrates the neutron response functions of the BSS in the energy range from 1 \(\times\) 10\({}^{-4}\) eV to 1 GeV, which were simulated using MCNPX-2.5.0. The validation of the response function was performed at China Institute of Atomic Energy, with the 144 keV and 1.2 MeV neutron sources, and the deviations obtained were less than 5%. A compact integrated electronics module (type PDT-10A) [14], manufactured by Precision Data Technology Inc., Everett, WA, USA, was used to supply a high voltage of +830 V to the central \({}^{3}\)He sensor, and process signals through a charge-sensitive preamplifier, shaper, and discriminator, and eventually to generate TTL pulses.  
In addition to the ERBSS, a carefully calibrated NADED was also employed in this work for two main reasons. The NADED of a volume smaller compared to the ERBSS was considered to be easier to operate. However, the feasibility of using it alone to measure the stray neutrons inside the neutron instruments needs to be investigated because of its relatively low sensitivity to fast neutrons. Moreover, since the neutron spectra measured by the ERBSS can be converted into H*(10) values using the fluence-to-ambient dose equivalent conversion coefficients established by ICRP [15] and ICRU [16], it was expected that the two measurement methods could cross-check each other. The adopted NADED (type N3020) [17] was purchased from SIM-MAX Technology Inc., Shanghai, China. It includes a highly sensitive \({}^{6}\)LiI(Eu) scintillator, and can directly display and record the neutron ambient dose rate. The neutron response energy range from 0.025 eV to 16 MeV and the dynamic range is from 0.1 to 1.0 \(\times\) 10\({}^{5}\) uSv/h.
# Vehicle Trajectory Reconstruction Using Lagrange-Interpolation-Based Framework
## 1 Introduction
(2) Two kinds of Lagrange interpolation polynomials are developed for reconstructing outliers between speed and acceleration in vehicle trajectory data, which improve computation efficiency. The remainder of this study is organized as follows. Section 2 describes the problems of vehicle trajectory data that the paper is dealing with. Section 3 describes the methodology of the proposed vehicle reconstruction framework in the paper. Section 4 shows the processing reconstruction results of the framework by experiments. Section 5 discusses the reconstruction results between the proposed method and the baseline method. Section 6 concludes this work and recommends further research directions.
## 1 Introduction
### Original Contributions  
In this context, the main contribution is designing and developing a three-layer framework for optimal placement of SCADA measurements with clustering-based EDS selection to estimate the state of the MV distribution networks. Each layer integrates original approaches to solving the following assumed objectives:  
* The first layer allows the determination of the classes of the EDSs with similar features from the viewpoint of requested loads based on the K-means clustering algorithm.
* The second layer identifies the \"candidate\" classes and the pilot EDSs (representing the optimal solution) with the SCADA measurements placed. The optimal placement corresponds to the minimisation of the estimation errors obtained using the multiple linear regression models between the EDSs from the classes not included in the set of the \"candidate\" classes and the pilot EDSs.
* The third layer allows the state estimation of the EDNs based on the load values measured in the pilot EDEs (with the SCADA system implemented) and the other EDSs obtained through the regression models. Also, the layer contains a module that verifies that it satisfies all the technical constraints, having integrated the functions to implement the strategies for optimal operation of the EDNs.  
The results obtained in the case of a real urban EDN with 39 EDSs demonstrated that the proposed framework could have lower integration costs (the optimal solution corresponds to 12 of 39 EDSs integrated into the SCADA system) and substantially improve the state estimation of the EDN.
# A Multi-Carrier Waveform Design for 5G and beyond Communication Systems
## 4 Numerical Results
In the end, it can be concluded that the proposed waveform with low PAPR can accommodate massive numbers of mobile users compared to the standard UFMC waveform, via power domain \begin{table}
\begin{tabular}{c c c c c} \hline
**Multi-Carrier Waveform** & **4 QAM** & **16 QAM** & **64 QAM** & **256 QAM** \\ \hline NOMA-UFMC & 11.8 & 11.8 & 11.8 & 11.8 \\ GCL Precoding Based Power Domain UFMC & 7.8 & 8.8 & 9.3 & 9.5 \\ SLM (\(V\) = 2) and GCL Precoding Based Power Domain UFMC & 7.4 & 8.2 & 8.7 & 8.8 \\ SLM (\(V\) = 4) and GCL Precoding Based Power Domain UFMC & 6.9 & 7.7 & 7.8 & 7.9 \\ SLM (\(V\) = 16) and GCL Precoding Based Power Domain UFMC & 6.4 & 7.2 & 7.4 & 7.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparisons of the PAPR in dBs at \(10^{-3}\) clip rate. Figure 6: PAPR analysis using 256-QAM. multiplexing. The main advantage of the proposed waveform over the other 5G candidate waveforms is capacity and throughput gain, ultra-high speed, anywhere anytime connectivity and multiple device compatibility to support a huge number of mobile users.
## 5 Laboratory Test Description
### Considered Noise Signals  
For these tests, a few representative sources of noise have been selected from the comprehensive review of sources of noise presented in Section 3. Specific samples from all the main categories presented in Section 3.2 (namely, normative noises, noises measured in the field, and noises measured in the lab) have been chosen.  
Regarding normative noises, the noise that fits the limits established in the EMI norm EN 50065 with frequency steps of 1.953 kHz has been selected as for representing the worst case, since the noise pulses are more frequent and the envelope remains the same (c.f. Figures 6 and 7).  
Regarding the noises measured in the field, the noise generated by a community antenna has been chosen for several reasons: (1) because it represents a very common noise, considering that this kind of devices are present in every single building; and (2) because it represents a harsh noise which can prevent the communications in PRIME networks, as it was pointed out in Section 3.2.3.  
Finally, regarding the noises measured in the lab, the noise generated by the charging station of an EV (notably, the Nissan Leaf) has been chosen as an example of consumption device and the noise generated by the PV panel inverters available at the LINTER has been selected as an example of generation equipment. In addition, both PV panels and electric vehicles represent, together with storage, the most novel equipment at the low voltage power networks of the Smart Grid, so the considered noises increase the potential impact of the study.
## 3 Background
### Iot Protocol Stack Development
#### 3.1.2 Network Layer Protocols Comparison  
Some of the most used network layer protocols used in IoT applications are IPv6 over Low-Power Wireless Personal Area Network (6 LoWPAN), ZigBee and Bluetooth Low Energy (BLE). A comparative review of these network layer protocol is given as follows:  
6 LoWPAN is based on IP version 6 (IPv6), it features 128-bit hexadecimal addresses, and uses the 802.15.4 radio frequency. It enables the use of IP in low-power and lossy wireless networks, such as WSNs (Wireless Sensor Networks), IoT (Internet of Things) and M2M applications. One of the most unique characteristics of 6 LoWPAN is that it allows header compression and encapsulation, making it more lightweight and secure than ZigBee. The most notable advantage of 6 LoWPAN is that it supports IP networks natively [36].  
The ZigBee protocol is based on the IEEE 802.15.4 standard, which allows for less battery consumption in IoT networks by keeping nodes in low power mode for the most part. However, unlike 6 LoWPAN, which has interoperability, ZigBee cannot readily communicate with other protocols.  
Bluetooth 5 is the latest version of BLE. It supports IP networks however, BLE is unable to establish a self-healing mesh network, which is becoming particularly crucial for IoT applications. An additional gateway is required to communicate with the internet when utilizing ZigBee or classic Bluetooth, which adds extra overhead.  
It can be deduced from the above discussion that due to its compression and encapsulation capabilities, interoperability and low network overhead, 6 LoWPAN is the prime option for the proposed CSFSA framework.
## 3 Methods
### Proposed IMNet Architecture  
In general, the IMNS can be used to create a limitless number of final architectures by combining the proposed SubNets, or other SubNet architectures. Here, we propose one specific example that we believe effectively balances performance and computational complexity for the medical imaging applications mentioned in Section 1. The proposed IMNet architecture is illustrated in Figure 9. Figure 9 shows five SubNets, \(A\), \(B\), \(C\), \(D\), and \(E\), that are incrementally added to produce the final network. We use relatively small and compact SubNet modules to maintain a small computational cost. The details for each SubNet are provided in Table 2. Figure 7a shows the workflow for adding the SubNet in series, and Figure 7b shows the addition of a parallel SubNet.  
First, we use all of the available minibatches to train the SubNet \(A\) and minimize the loss to obtain the optimum parameters using Equation (10). After training for SubNet \(A\) is complete, we lock in the learnable parameters for this module and refer to it as IMNet \(A\). This network is used to generate the feature maps that will be the input to the new SubNet \(B\). The first convolutional layer of the SubNet \(B\) receives all the feature maps from \(L-1\) layer of SubNet \(A\) as input. In this case, the SubNet \(B\) is connected in a series configuration that can be denoted as \(A+B\) and we refer to this as IMNet \(A,B\).  
Next, we lock the current IMNet \(A,B\), and add a new SubNet \(C\) in parallel. This combination of SubNets may be expressed as \((A+B)||C\) and we refer to this as IMNet \(A\)-\(C\) for notational convenience. We use the equations mentioned above to generate feature maps using IMNet \(A,B\) and concatenate these with the feature maps generated by the  
Figure 9: IMNet architecture used here in the experimental results. SubNets \(A\), \(B\), \(C\), \(D\) and \(E\) are added incrementally in order to produce the full network shown. Details of the SubNets are provided in Table 2.  
new SubNet \(C\). We use a reshape layer to match the feature map of IMNet \(A,B\) with output feature maps of SubNet \(C\) and concatenate the feature maps in the depth dimension. SubNet \(D\) is then added in series to produce the configuration \((A+B)||C+D\), denoted here as IMNet \(A\)-\(D\). Finally, we lock IMNet \(A\)-\(D\) and add the last new SubNet \(E\) in a parallel configuration. This IMNS sequence can be represented as \([(A+B)||C+D]||E\) and we denote this as IMNet \(A\)-\(E\). We selected this configuration because we find that alternating between series and parallel SubNets is generally effective, as these two additions tend to complement each other.
## 4 Design of the DRFTM
### Access to Evidence of Trust  
#### 4.1.1 Communication Trust  
To a certain extent, the communication success rate between adjacent nodes reflects the credibility of their neighbor nodes to some extent. Therefore, the communication success rate is usually an important indicator to evaluate the communication trust degree between adjacent nodes. Assuming node \(n_{i}\) and its \(m\) neighboring nodes \(\left(n_{1},\ n_{2},\ \ldots,\ n_{j},\ \ldots,\ n_{m}\right)\), this paper incorporates the beta distribution to simulate the behavior of an individual node [26]. The beta distribution is a density function that acts as a conjugate prior distribution to the Bernoulli or binomial distribution and is suitable for modeling trust distributions and is expressed in terms of the function \(\Gamma\) as [27]:  
\[P(x)=\frac{\Gamma(a+b)}{\Gamma(a)\Gamma(b)}x^{a-1}(1-x)^{b-1},\forall 0\leq x \leq 1, \tag{2}\]  
where \(a\geq 0,\ b\geq 0\). For communication trust, \(a\) represents normal behavior, i.e., the ability to successfully transmit between nodes \(n_{i}\) and \(n_{j}\), \(b\) represents abnormal behavior, i.e., the failure of transmission between nodes \(n_{i}\) and \(n_{j}\), and the total interaction count between nodes \(n_{i}\) and \(n_{j}\) is equal to the sum of the normal transmission count and the abnormal transmission count, which is represented as \((a+b)\).  
When forecasting the behavior of node \(n_{i}\), node \(n_{i}\)'s conduct is denoted by \(\sigma\) and follows a uniform distribution: \(P(\sigma)=Beta(1,1)\) in the absence of a priori knowledge. The posterior probabilities obey a Beta distribution with the following parameters: \(P(\sigma)=Beta(a+1,b+1)\). Based on this beta distribution, node \(n_{j}\) computes the communication trust value for node \(n_{i}\) as  
\[T_{i}^{com}=E(Beta(a+1,b+1))=\frac{a+1}{a+b+2}. \tag{3}\]
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Algorithms
#### 3.2.6 Transformer Architecture  
Among the possible architectures used in deep learning, we decided to emphasize the transformer, a kind of sequence-to-sequence architecture usually composed by an encoder and a decoder. Initially proposed for natural language processing, it is currently also used in many other fields. The encoder maps internally the input sequence to a high-dimensional one that is subsequently processed by the decoder to produce the output. The transformer learns how to create this internal mapping thanks to dedicated training sequences. In this scenario, the attention mechanism plays a crucial role, assigning different weights to each value of the sequence, in order to capture relationships among them [18].  
A subsequent exploitation of such a technology is proposed in [268], where transformer is used for end-to-end object detection, thanks to the DETection TRansformer (DETR) model. The same model finds practical usage in agronomy, as reported in [61], where such a method is exploited to detect images of diseased rice leaves.  
In general, using transformer in agronomy is not a trivial task, due to challenges emerging from language-to-vision adaptation. For this reason, ST [269] and Vision Transformer (ViT) [270] are proposed. [PERSON] et al. in [271] show the usage of ST within the YOLO model, to identify common maize leaf spot, gray spot, and rust diseases. In [272], ST is used in an ensemble with Residual Neural Network (ResNN). The accuracy of this method is also proved by [273], testing it with pictures taken in a complex natural environment. In the same way, ViT is employed to detect leaf disease in conjunction with transfer learning in [274]. Finally, in [275], [PERSON] et al. use ViT together with acoustic sensors in order to detect cotton pest insects.
## 3 Materials and Methods
### MagEMNet Architecture
#### 3.3.2 Squeeze-and-Excitation (SE) Blocks  
To refine and enhance the learned feature representations, SE blocks are incorporated following each convolutional layer, as shown in Figure 7. The SE blocks introduce an adaptive attention mechanism that emphasizes informative feature channels while suppressing less relevant ones, effectively improving the model's sensitivity to important features within the data. This is achieved through a two-step \"squeeze\" and \"excitation\" process.  
In the squeeze step, global average pooling is applied to each feature map across spatial dimensions, transforming the feature map into a channel descriptor vector. This vector captures the global spatial context, condensing the spatial information into a compact representation. Formally, given an input feature map \(\textbf{X}\in\mathbb{R}^{b\times c\times h\times w}\), where \(b\) is the batch size, \(c\) is the number of channels, and \(h\) and \(w\) are the spatial dimensions, the global context vector **s** is computed as  
\[\textbf{s}=\frac{1}{h\times w}\sum_{i=1}^{h}\sum_{j=1}^{w}\textbf{X}_{ij}. \tag{7}\]  
The excitation step then passes **s** through two fully connected (FC) layers. The first FC layer reduces the dimensionality of **s** by a factor of 16, effectively capturing essential information while reducing computational complexity. This bottleneck operation is followed by a ReLU activation, adding non-linearity and enhancing the network's representation power. The second FC layer restores the original channel dimensions, and a sigmoid activation is applied to produce an attention map \(\textbf{y}\in\mathbb{R}^{b\times c\times 1\times 1}\). The resulting attention map is then used to recalibrate the original feature map by channel-wise multiplication:  
\[\textbf{Z}=\textbf{X}\cdot\textbf{y}, \tag{8}\]  
where **Z** denotes the refined feature map, selectively amplified based on the importance of each channel.  
Figure 7: Squeeze-and-Excitation (SE) block diagram.
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 3 Proposed Approach
### Supervised Machine-Learning Classifiers
#### 3.3.4 Decision Tree (DT)  
The DT classifier is one well-known supervised machine-learning method used for regression and classification. It is favored due to its great performance and simplicity, especially in a highly interpretable scenario. The method builds a hierarchical structure that looks like a tree such that every non-leaf node represents a decision based on a particular feature, every branch represents the outcome of this decision, and every terminal node represents the final classification label or value [33]. A decision tree is constructed by partitioning the training data into sub-samples based on the most distinguishing feature. As new data come in, the data attributes are traced from the root of the tree, progressing through various nodes, until reaching a leaf node that signifies the classification of the data [35]. In particular, decision trees are easy to understand and use, rendering them valuable for elucidating decision-making processes. On top of that, they are able to model complex nonlinear relationships in the data, and they operate without imposing assumptions about the underlying data distribution.
## 4 Discussion  
### Antenna Placement  
In our setup, omnidirectional antennas were employed, designed to disseminate signals equally across all directions. This led to the strategic placement of antennas in a parallel arrangement, as illustrated in Figure 4, across all our tests. We validated this setup by analyzing the Received Signal Strength Indicator (RSSI), which measures signal intensity. Our findings confirmed that a parallel alignment of the antennas facilitated the most effective signal concentration.