esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of the Proposed Structures  
In this section, we propose two innovative compensation structures: a series inductive compensation structure (SICS) designed to compensate for high frequency attenuation values and a small bit compensation structure (SBCS) for large attenuation bits. The proposed compensation structures effectively broaden the bandwidth of the attenuator and reduce the high-frequency additional phase shift. Furthermore, the compensation structures are equally applicable to T-type attenuators.
## 3 Methodology
### Network Simulation
The fitting process involves estimating the parameters \(\mu\) and \(\sigma\) that best describe the observed data utilizing the maximum likelihood estimation (MLE) method, which maximizes the likelihood function of the log-normal distribution given the data. This likelihood function for a set of independent observations \(\{x_{1},x_{2},\dots,x_{n}\}\) is as follows:  
\[L(\mu,\sigma)=\prod_{i=1}^{n}\frac{1}{x_{i}\sigma\sqrt{2\pi}}\exp\left(-\frac {(\ln x_{i}-\mu)^{2}}{2\sigma^{2}}\right) \tag{3}\]  
Taking the logarithm of the likelihood function to simplify the calculations, we obtain the log-likelihood function:  
\[\ell(\mu,\sigma)=-n\ln(\sigma\sqrt{2\pi})-\sum_{i=1}^{n}\ln(x_{i})-\frac{1}{2 \sigma^{2}}\sum_{i=1}^{n}(\ln x_{i}-\mu)^{2} \tag{4}\]  
To find the values of \(\mu\) and \(\sigma\) that maximize the log-likelihood function, we take the partial derivatives of \(\ell(\mu,\sigma)\) with respect to \(\mu\) and \(\sigma\), set them to zero, and solve the resulting system of equations. The estimates for \(\mu\) and \(\sigma\) are given by the following:  
\[\hat{\mu}=\frac{1}{n}\sum_{i=1}^{n}\ln(x_{i}),\quad\hat{\sigma}^{2}=\frac{1}{ n}\sum_{i=1}^{n}(\ln(x_{i})-\hat{\mu})^{2} \tag{5}\]  
Once the parameters \(\hat{\mu}\) and \(\hat{\sigma}\) are determined, the fitted log-normal distribution can be used to predict the likelihood of different latency values and assess device characteristics since a larger variance in latency may correlate to longer on-device processing. This processing time may indicate the presence of a cybersecurity vulnerability or generally make the device more susceptible to a cyber attack through denial-of-service since each packet takes a larger portion of processing time on the device. This model is particularly useful in understanding latency behavior in large-scale networks, where long-tail distributions are common, and can inform decisions related to network design, quality of service (QoS) optimization, and required device isolation from network traffic to mitigate vulnerability exposure.
## 4 Results and Discussion
### Data Quality Improvement and Application of Results
#### 4.3.3 Future Research and Broader Applications  
This study establishes a scalable methodology for improving data quality in transportation safety research, demonstrating that CNN-based classification can effectively refine crash datasets. Beyond pedestrian safety, this approach can be extended to analyze and enhance crash data for other vulnerable road users, including bicyclists, micro-mobility users [60], horse-and-buggy users [9], and motorcyclists. By applying similar CNN-based feature extraction techniques [8; 24], future studies can further improve the completeness and accuracy of crash datasets across multiple transportation modes.  
Moreover, integrating CNN-extracted features with real-time crash monitoring systems offers an opportunity to develop advanced predictive analytics for transportation safety. This integration enhances risk prediction models, allowing transportation agencies to proactively implement safety measures rather than relying solely on historical crash data. By leveraging real-time insights, agencies can identify high-risk locations, allocate resources more efficiently, and implement timely interventions to mitigate crash risks.  
Ultimately, this study's findings contribute to more effective pedestrian injury prevention strategies, supporting the broader goal of safer and more resilient urban mobility systems. This research lays the foundation for data-driven decision-making that can significantly enhance transportation planning, infrastructure design, and traffic safety policies through improved data accuracy and predictive capabilities.  
This study represents the first attempt to classify pedestrian crash diagrams using automated DL techniques, marking a significant advancement in crash data analysis. By leveraging CNN architectures, such as AlexNet, ResNet-50, and VGG-19, this research establishes a baseline for automated feature extraction from pedestrian crash diagrams. These CNN models were selected due to their well-documented effectiveness in image classification tasks and ability to recognize intricate spatial patterns within crash diagrams. However, as deep learning techniques continue to evolve, emerging models such as Vision Transformers and hybrid CNN-Transformer architectures offer promising avenues for improving classification accuracy and computational efficiency. Future research should explore integrating these advanced models to enhance the robustness of pedestrian crash diagram classification. Future studies could further optimize automated crash data extraction by incorporating Transformer-based classifiers, enabling the more precise identification of critical safety features and contributing to data-driven decision-making in transportation safety research.
## 3 Parameter Estimation  
### Quantum Cramer-Rao Bound  
In quantum metrology, the parameters encoded in some quantum carrier are usually taken to be phases \(\Theta=(\theta_{1},\theta_{2},...,\theta_{n})\). The optimization goal is to lower the variance of the estimators as much as possible. However, the lowest possible variance on each (unbiased) estimator \(\tilde{\theta}_{i}\) is limited by the so-called _Quantum Cramer-Rao Bound_ (QCRB) [16; 17], namely  
\[\Delta^{2}\tilde{\theta}_{i}\geq\frac{(F^{-1}(\Theta))_{ii}}{N}, \tag{4}\]  
where \(F(\Theta)\) is the so-called quantum Fisher information matrix (QFIM) and \(N\) is the number of repetitions of the scheme. If a measurement scheme saturates the QCRB, it is called _optimal_ since no other scheme can do better.  
For a state \(\rho\), the QFIM is defined through the symmetric logarithmic derivative (SLD) \(L_{\theta_{i}}\) for \(i=1,2,...,n\), which is the selfadjoint operator satisfying the following Lyapunov equation [1]:  
\[\frac{\partial\rho}{\partial\theta_{i}}=\frac{L_{\theta_{i}}\rho+\rho L_{ \theta_{i}}}{2}. \tag{5}\]  
The matrix elements of the QFIM are then defined in terms of the SLDs as \(F_{ij}(\Theta)=\Re\left[\text{Tr}\left(\rho L_{\theta_{i}}L_{\theta_{j}} \right)\right]\), where symbol \(\Re\) denotes the real part of the expression since the QFIM is real.  
In the case of a pure state \(|\psi\rangle\), simple expressions can be found for both the SLDs and the QFIM in [18]. The SLD for a pure state is written as  
\[L_{\theta_{i}}=|\psi\rangle\langle\partial_{i}\psi|+|\partial_{i}\psi\rangle \langle\psi|, \tag{6}\]  
and the matrix elements of the QFIM are given by  
\[F_{ij}(\Theta)=4\ \Re\left[\langle\partial_{i}\psi|\partial_{j}\psi\rangle- \langle\partial_{i}\psi|\psi\rangle\langle\psi|\partial_{j}\psi\rangle\right], \tag{7}\]  
where \(\partial_{i}\) denotes partial derivative with respect to the \(i\)th parameter \(\theta_{i}\) in both Equations (6) and (7). We remark that the QFIM depends on the states but is not depending on the measurement being performed since it gives a bound on the best measurement. In the next subsection, we derive the optimal lower bound on the QCRB for our parameters \(a\) and \(b\).
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 2 Related Work
### Traditional IoV Authentication Technology  
Traditional IoV solutions mainly protect data security through encryption. [PERSON] et al. [20] used elliptic curves and bilinear pairings to construct signatures, to hide the identity of a message sender, but they could not meet the low-complexity requirements of IoV. In [21], [PERSON] et al. proposed an efficient anonymous authentication method based on zero-knowledge proof (ZKP) and elliptic curve cryptography (ECC), where a third trusted institution can effectively track users through their verification keys. In [22], [PERSON] et al. designed an accelerated key negotiation scheme for transmitting sensitive messages, which greatly improved the efficiency of RSU signature verification. In [23], [PERSON] et al. proposed a secure and efficient conditional privacy anonymous batch authentication scheme (EABAS-CP) based on elliptic curve cryptography, which was suitable for IoV environments. This scheme supports batch signature verification, and the random oracle model (RoM) can use the elliptic curve discrete logarithm problem (ECDLP) to provide unforgeability against adaptive message attacks. Although the aforementioned solutions are highly secure and well-established, the difficulty of management significantly increases as the system scales. Additionally, the drawbacks of high computational resource consumption and prolonged latency render it unsuitable for the dynamic network topology characteristic of the IoV application environment.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 8 Challenges and Future Directions
### Refinement of Rainfall Retrieval and Mapping Algorithms  
When the rainfall intensity is strong, the attenuation of microwave signal is obvious, and the classification of wet/dry weather, the determination of zero baseline, and the calculation of rain rate are more accurate. On the contrary, when the rainfall intensity islight, the attenuation of microwave signal caused by rain is easily covered by abnormal signal fluctuations or WAA, and uncertainty is greater compared with heavy rain, so the calculation method of rain rate should be refined according to different rainfall intensities. It can be combined with other measurement methods to achieve more accurate rainfall measurement. The network density and topology of microwave links have a great influence on the construction of rainfall map [126]. In general, the higher the density, the higher the resolution of the constructed rainfall field. The design of CML topology should refer to the topology optimization algorithms of wireless sensor networks (WSNs) to further expand the sensing range and improve the sensing accuracy. Based on the existing GIS spatial interpolation and tomographic reconstruction techniques, more flexible algorithms of rainfall mapping should be designed to meet the complex geographical conditions.
## 1 Introduction
It has a number of advantages over other approaches, particularly in urban environments, due to its quick implementation and the low cost of the equipment required for data acquisition. The HVSR technique has proven its reliability for estimating ground fundamental frequencies, as confirmed by several research studies [20; 21; 22; 23; 24; 25]. Examples of its application can be found all over the world [26; 27; 28; 29; 30] and even outer space (e.g., [31]) The Lower Cheliff Basin has been the subject of few studies using ambient noise data to characterize the sedimentary cover (e.g., [32; 33]). HVSR analysis can be complemented by other geotechnical data as well as ambient noise array measurement techniques, both active and passive, such as frequency-wavenumber (f-k) [34; 35; 36; 37], spatial autocorrelation (SPAC) [38; 39], extended spatial autocorrelation (ESAC) [40; 41] and refraction microtremor (REMI) [42] techniques. Passive methods offer the possibility of modeling deeper Vs structures than active techniques, since seismic waves generated by passive sources (natural sources) are richer in low frequencies than the energy spectra of active sources [43; 44]. To analyze the influence of sedimentary structure on ground motion, it is essential to well characterize the soil structure. Some geophysical, geotechnical, and geological properties of the soil have to be determined: for example, the P- and S-wave velocity structures (Vp and Vs), the density (\(\rho\)), and the thickness of sedimentary deposits. The Vs structure of the surface layers can be obtained in several ways, for example, by inversion of the HVSR or/and surface wave dispersion curves extracted from ambient noise recordings. The main purpose of this study is to assess the site effects and sedimentary structure beneath the city of Ain Temouchent using ambient vibrations data in order to enhance the seismic hazard assessment in the region. For this purpose, ambient noise was recorded at 62 sites inside the city, using single station measurements. In addition, array recordings were performed at 4 potential sites. The HVSR technique was applied to single-station recordings to estimate the fundamental frequency of the soil (\(f_{0}\)), the corresponding amplitude (\(A_{0}\)), and the vulnerability index (\(Kg\)). The inversion of HVSR curves was used to estimate the shear wave velocity in sedimentary layers. In addition, the SPAC technique was applied to array measurements to retrieve the dispersion curves of the fundamental mode of Rayleigh waves. The inversion of dispersion curves was performed separately to estimate more constrained Vs profiles, particularly at shallow layers. Based on the inversion results (Vs profiles) and available borehole data, which were also used as input to the inversion process, different cross-sections were established to track the lateral evolution of sedimentary layers in the area. Finally, several maps showing the variation in \(f_{0}\), \(A_{0}\), \(Kg\), soil class based on Vs30, and bedrock depth are proposed. This work contributes to existing studies in the region, with the aim of helping to assess the region's seismic hazard.
## 3 Experimental Setup
### Aerodynamic Features  
The GVV signals from IBIF, SNF, and Kalman methods are divided in 50 ms, non-overlapping frames. Voicing is detected by calculating the normalized autocorrelation of the ACC signal and the main peak exceeding a threshold of 0.8. If the frame is voiced, measures are extracted from the GVV waveform, its time-derivative, and spectrum. Figure 9a shows an ACC frame and (b) a GVV waveform, the spectrum (c) and the time-derivative waveform (d). The features used in this work are described in Table 1. Some of these aerodynamic features, such as AC flow (ACFL) and maximum flow declination rate (MFDR), have been shown to be useful to discriminate between subjects with PVH and healthy controls [5; 9; 16; 29; 58]. Instead of estimating time-domain features based on the detection of glottal opening and/or closing instants, the normalized amplitude quotient (NAQ) is calculated in this study, due to its robustness to noisy measurements and its correlation to the close quotient of the glottal cycle [59]. For time-domain measures (ACFL, MFDR, NAQ and \(f_{0}\)), the median for all cycles within the 50 ms frame is obtained. The difference in magnitude of the first and second harmonic (H1-H2) is computed from the GVV spectrum.  
Figure 8: Neck-skin frequency response for a healthy female subject, full length (blue) and truncated version with a Hann window (red).  
Figure 9: (**a**) ACC frame, (**b**) GVV frame, (**c**) spectrum from (**b**), and (**d**) time-derivative from (**b**).
## 3 Results and Discussion
### Physiological Ability and Functionality Study
#### 3.2.2 Phosphate Solubilization Ability  
UPMC1393 and UPMC1376 showed a clearing zone as an indicator of the ability to solubilize phosphate (Figure 2). [PERSON] [41] found phosphate-solubilizing rhizobacteria to be one of the finest alternatives to inorganic phosphate fertilizers for improving plant productivity and development since phosphorus has been identified as one of the most  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & **Nitrogen Fixation Ability** & **Phosphate Solubilizing** & **Potassium Solubilizing** \\  & **(Diameter in cm)** & **Ability (Diameter in cm)** & **Ability (Diameter in cm)** \\ \hline UPMC 703 & \(-\)(0.00) & \(-\)(0.00) & \(+\)(3.75 \(\pm\) 2.50 \({}^{\text{a}}\)) \\ UPMC 704 & \(-\)(0.00) & \(-\)(0.00) & \(+\)(4.25 \(\pm\) 1.5 \({}^{\text{a}}\)) \\ \hline \hline \end{tabular} Notes: \({}^{a}\)\({}^{-}\)\({}^{\text{}}\) indicates absence of growth; \({}^{a}\)\({}^{+}\)\({}^{\text{}}\) indicates slow growth. The test was carried out using three replicates for each isolate. Column means followed by the same letter indicate insignificant (\(p>0.05\)) difference using Duncan’s Multiple Range Test.  
\end{table}
Table 4: _Cont._  
Figure 1: Nitrogen fixation ability of (**a**) UPMC1166 and (**b**) UPMC 1168. Example of changes in media color from pale green to blue is an indicator nitrogen fixation process which was carried out on bacteria with the increase in pH by the formation of ammonia and nitrates. Note: Arrows show the color changes from green to blue.  
critical nutrients for plant growth [69]. According to [PERSON] and [PERSON] [70], the best isolates as phosphate solubilizers were F372 (15 mg/L), which was associated with _Saccharum officinarum_, followed by ESB4 (14 mg/L). [PERSON] et al. [71] obtained similar consistent phosphate solubilization findings in both agar and broth assays.  
Three highly efficient phosphate-solubilizing bacteria, _Pseudomonas fluorescens_[72], _Burkholderia gladioli_ and _Penicillium aculeatum_[73], have been identified as potential biofilifizers for oil palm trees. The effects of phosphate-solubilizing bacteria on commercial plant growth have also been reported by [PERSON] et al. [74] who investigated the ability of three phosphate-solubilizing bacteria (_Ralmella aqutilis_ (KM977991), _Pantoea annatis_ (KM977993), and _Enterobacter_ sp. (KM977992) to release potassium (K) from mica to understand their impact on rice plant growth at an early initial stage of development.
## Appendix B Proof of Theorem 2  
From (44), OP at IoT device \(D_{n}\) is rewritten as follows:  
\[OP_{D_{n}}(t)=\max\left\{\underbrace{1-\Pr\biggl{\{}\min\limits_{i=\left\{N _{r},..,n\right\}}\biggl{\{}R_{R_{r}-N_{i}}^{T_{1}^{(i)}}\biggr{\}}\geq\mathcal{ R}\biggr{\}}}_{Z_{n}},\underbrace{1-\Pr\biggl{\{}\min\limits_{i=\left\{N_{r},..,n \right\}}\biggl{\{}R_{D_{n}-x_{i}}^{T_{2}^{(i)}}\biggr{\}}\geq\mathcal{R} \biggr{\}}}_{Q_{n}}\right\}\Biggr{\}}. \tag{20}\]After the algebraic transformation, expression \(Z_{n}\) in (A3) could be obtained by applying PDF given by [21] (Equation (55)) for \(i>1\) or [21] (Equation (56)) for \(i=1\) as follows:  
\[Z_{n} =1-\Pr\biggl{\{}\left|h_{S,R_{r}}\right|^{2}\geq\frac{\gamma}{(1- \lambda_{R_{r}})\beta_{n}\rho\sigma_{S,R_{r}}}\biggr{\}}\] \[=1-\int\limits_{\frac{\gamma}{\left(1-\lambda_{R_{r}}\right) \beta_{n}\rho}}^{\infty}\frac{1}{\sigma_{S,R_{r}}}\exp\biggl{(}-\frac{x}{ \sigma_{S,R_{r}}}\biggr{)}dx\] \[=1-\exp\biggl{(}-\frac{\gamma}{(1-\lambda_{R_{r}})\beta_{n}\rho \sigma_{S,R_{r}}}\biggr{)},\] (A4) \[\text{s.t.}\qquad\beta_{n} =\min\limits_{i=\{N,\ldots,n\}}\{\beta_{i}\},\] (A5)  
where \(\beta_{i}\) in expression (A5) is given by (42). By substituting (26) for \(t=odd\) or (27) for \(t=even\) into (A3) and applying PDF given by [21] (Equation (55)) for \(i>1\) or [21] (Equation (56)) for \(i=1\), expression \(Q_{n}\) in (A3) could be obtained as follows:  
\[Q_{n} =1-\Pr\biggl{\{}\left|h_{R_{r},D_{n}}\right|^{2}\geq\frac{\gamma }{\eta\lambda_{R_{r}}\beta_{n}\rho\sigma_{S,R_{r}}}\biggr{\}}\] \[=1-\int\limits_{\frac{\gamma}{\eta\lambda_{R_{r}}\beta_{n}\rho \sigma_{S,R_{r}}}}^{\infty}\frac{1}{\sigma_{R_{r},D_{n}}}\exp\biggl{(}-\frac{x }{\sigma_{R_{r},D_{n}}}\biggr{)}dx\] \[=1-\exp\biggl{(}-\frac{\gamma}{\eta\lambda_{R_{r}}\beta_{n}\rho \sigma_{S,R_{r}}\sigma_{R_{r},D_{n}}}\biggr{)},\] (A6)  
where \(\beta_{n}\) in (A6) is given by (A5). From results presented in study [18], the OP result at IoT device referred to OP result at either IoT relays or IoT device. Therefore, expression (A3) could be rewritten as follows:  
\[OP_{D_{n}}(t) =\max\{Z_{n},Q_{n}\}\] \[=1-\exp\biggl{(}-\frac{\gamma}{\Omega_{n}}\biggr{)},\] (A7) s.t. \[Z_{n} =1-\Pr\biggl{\{}\left|h_{S,R_{r}}\right|^{2}\geq\frac{\gamma}{(1- \lambda_{R_{r}})\beta_{n}\rho\sigma_{S,R_{r}}}\biggr{\}}\]0 (A8) \[Z_{n} =1-\Pr\biggl{\{}\left|h_{S,R_{r}}\right|^{2}\geq\frac{\gamma}{(1- \lambda_{R_{r}})\beta_{n}\rho\sigma_{S,R_{r}}}\biggr{\}}\]1 (A9) \[Z_{n} =1-\Pr\biggl{\{}\left|h_{S,R_{r}}\right|^{2}\geq\frac{\gamma}{(1- \lambda_{R_{r}})\beta_{n}\rho\sigma_{S,R_{r}}}\biggr{\}}\]2 (A10)  
In Lemma 2, IoT hub \(S\), relays and devices have been equipped multiple antennas with \(A_{S}\), \(A_{R}\) and \(A_{D}\), respectively.
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
###### Abstract  
Anomaly detection plays a crucial role in preserving industrial plant health. Detecting and identifying anomalies helps prevent any production system from damage and failure. In complex systems, such as oil and gas, many components need to be kept operational. Predicting which parts will break down in a time interval or identifying which ones are working under abnormal conditions can significantly increase their reliability. Moreover, it underlines how the use of artificial intelligence is also emerging in the process industry and not only in manufacturing. In particular, the state-of-the-art analysis reveals a growing interest in the subject and that most identified algorithms are based on neural network approaches in their various forms. In this paper, an approach for fault detection and identification was developed using a Self-Organizing Map algorithm, as the results of the obtained map are intuitive and easy to understand. In order to assign each node in the output map a single class that is unique, the purity of each node is examined. The samples are identified and mapped in a two-dimensional space, clustering all readings into six macro-areas: (i) steady-state area, (ii) water anomaly macro-area, (iii) air-water anomaly area, (iv) tank anomaly area, (v) air anomaly macro-area, (vi) and steady-state transition area. Moreover, through the confusion matrix, it is found that the algorithm achieves an overall accuracy of 90 per cent and can classify and recognize the state of the system. The proposed algorithm was tested on an experimental plant at Universita Politecnica delle Marche.  
machine learning unsupervised learning; Industry 4.0; anomaly detection; smart industrial plant; predictive maintenance
# Practical V2I Secure Communication Schemes for Heterogeneous VANETs
## 1 Introduction
Two many-to-one heterogeneous signcryption schemes are proposed, which allow a large number of vehicles registered in the PKI/IBC freely communicating with a RSU registered in IBC/PKI. Two other one-to-many heterogeneous signcryption schemes are proposed, which allow a sender RSU in PKI/IBC freely broadcasts a message to many vehicles in IBC/PKI, which are especially designed for a RSU broadcasting message to vehicles in V2I communication scenario of VANETs. 2. Inspired by the idea of signcryption and aggregation, our proposed many-to-one heterogeneous aggregate signcryption schemes support batch verification, which not only can save a lot of time compared with the traditional sequential verifications, but also can save the computational, communicational, and storage cost. In addition, all the proposed schemes can realize confidentiality, authentication, integrity, and non-repudiation at the same time. 3. Numerical analyses and simulation results show the computation costs, communication costs, storage, and the aggregate ciphertext length of our proposed schemes are preferable to the existing ones. The organization of our article is shown below: A survey of related work is described in Section 2. Some preliminaries are provided in Section 3. Four practical, heterogeneous signcryption proposals are presented in Section 4. The security proof and performance analysis are presented in Sections 5 and 6, respectively. In the end, we summarize the conclusions in Section 7.