esa-sceva/satcom-chunk-collection huggingface dataset:
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 2 Related Work  
Recent efforts in securing IoT systems in smart cities have explored both post-quantum cryptography (PQC) and QKD. While some approaches aim to strengthen the existing encryption methods, others introduce hybrid or fully quantum solutions. A hybrid encryption scheme has been proposed that combines symmetric and post-quantum cryptography for smart grids, although it lacks real-time intrusion adaptability [10]. Quantum threats to IoT systems emphasize the importance of post-quantum cryptography, although practical integration frameworks have not been fully established [11]. An ML-enhanced QKD system has been proposed for IoT communication, with a primary focus on threat detection rather than secure key management [12]. A lightweight post-quantum authentication protocol has been explored for dense IoT networks; however, its scalability under high-entropy attacks has not been demonstrated [13].  
A conceptual model for Quantum IoT (QIoT) networks has been proposed to explore future-ready secure architectures; however, it remains largely theoretical, with no concrete deployment models tailored for IoT environments [14]. Quantum machine learning techniques have been integrated with Security Information and Event Management (SIEM) systems to enhance threat prediction in smart city contexts; however, these approaches do not address security mechanisms at the IoT device level [15]. An optimized HQC-based encryption scheme tailored for the IoT is presented using a hardware/software co-design approach, achieving reduced latency; however, its performance is constrained in static and predefined deployment environments [16]. CR-QKD was introduced for the sharing of mobile keys in a wide area, but the scheme is not optimized for IoT power constraints [17].  
PQC algorithms have been analyzed regarding the IoT, identifying challenges but offering no system-level integration model [18]. The PQC performance was evaluated on embedded systems but not in the context of distributed urban networks [19]. Quantum's Quantum Origin uses quantum entropy for key generation, although integration with IoT protocols like MQTT remains undefined [20]. Table 1 highlights the primary limitations of the state-of-the-art quantum-secured IoT frameworks and illustrates how the proposed QESIF overcomes these gaps through entropy-aware key generation, adaptive intrusion detection, and energy-efficient protocol switching.
## 4 Numerical Simulation
### Model Development
#### 4.1.1 OpenSees Simulation Model  
The numerical model used for high-strength bridge columns is shown in Figure 16. All degrees of freedom at the supported end were fixed, while those at the loaded end were left free to replicate the experimental conditions. Force-based fiber beam-column elements with five integration points were used to model the column element, as it considers the nonlinear deformation of the structural members and allows for plasticity along the length of the element. OpenSees employs the fiber-element technique for the modeling of RC structural members. The member cross-section is represented by steel and concrete fibers that are defined as their uniaxial constitutive material models. The \"Concrete04,\" a uniaxial concrete material, was used to model the confined concrete behavior in the core concrete and the unconfined concrete behavior in the cover concrete. Each parameter of the unconfined concrete was obtained from the tests of the actual concrete specimens, while those of the confined concrete were calibrated using XTRACT [41] and NewRC-Mocur2020 [42], considering the influence of hoops and ties.  
Similarly, the \"Hysteretic\" material model in OpenSees, a uniaxial bilinear hysteretic material object with a pinching effect, was used to model the longitudinal rebar. The post-yielding ratio was in the range of 0.5 to 2.5% of [PERSON]'s modulus of steel. The pinching factor for strain or deformation during reloading (pinch-X) and the pinching factor for stress or force during reloading (pinch-Y) were set as 0.4 and 0.6, respectively [43].  
Figure 16: Numerical models for bridge (a)column; (b) frame.
## 3 Results
### Impact of Meteorological Factors on GPP
#### 3.3.3 VPD-Driven Suppression of GPP  
To comprehensively evaluate the mechanisms through which the four meteorological factors influence GPP, we employed SEM for multi-factor path analysis. SEM, as a multivariate statistical method that integrates factor analysis and causal pathways, effectively elucidates the multi-level interactions within complex systems.  
The results of the model (Figure 6) indicate that TEM, PRE, SSR, and VPD significantly regulated GPP (\(p<0.05\)). Among these factors, VPD demonstrated a significant negative correlation with GPP, with the direction and magnitude of its standardized path coefficient indicating a systematic suppressive effect in arid regions. The results indicate that although partial correlation analysis demonstrated regional positive effects of VPD on GPP, the integrated impact of VPD on GPP across the arid ecosystems of Xinjiang revealed a significant negative response (path coefficient \(=-0.58\), \(p<0.05\)). This discrepancy in responses be  
Figure 5: Spatial distribution of ridge regression coefficients of meteorological factors in Xinjiang from 2001 to 2020. (**a**) Vapor pressure deficit (VPD). (**b**) Temperature (TEM). (**c**) Downward surface shortwave radiation (SSR). (**d**) Precipitation (PRE). Proportion of influence intensity levels of each meteorological factor: (**e**) VPD; (**f**) TEM, (**g**) SSR; (**h**) PRE.  
tween local and regional scales suggests that the ecological effects of VPD on GPP exhibited notable spatial heterogeneity and environmental dependency. It is particularly noteworthy that under extreme arid conditions, a unique threshold response relationship between VPD and GPP may be established.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 3 The Receiving Method of the Integrated Signal in the TDD Mode
### The Acquisition Process of Integrated Signal  
The acquisition process should give the rough code phase and Doppler frequency to the tracking process. Serial acquisition, parallel acquisition and parallel code phase searching are the three major methods for acquisition [29]. For BOC signals, due to the existence of subcarriers, the acquisition process needs to preprocess the signal. The acquisition methods of a pure BOCos signal are shown in [30,31]. Ref. [32] gives a BPSK-like technique to acquire the BOC signal as two BPSK signals. However, the method will cause a 3 dB signal loss. This paper restores the upper and lower sidebands to center frequency for processing, which will effectively avoid 3 dB signal loss. The technique extracts the signals of the upper and lower sidebands of the BOC signal to the baseband and regards them as a C/A code for acquisition. And the acquisition method for C/A code is discussed in [33,34]. In this paper, the TT&C signals are extracted by using this technique, and there is no need for an independent filtering process of APSK signals  
The preprocessing in the acquisition process is as follows.  
Step 1. The control unit shifts the frequency of the carrier numerically controlled oscillator (NCO) by \(f_{\text{I}}\). In addition, the carrier NCO generates the in-phase and orthogonal carrier signals based on the frequency tuning word and mixes with the input signals.  
Step 2. The subcarrier NCO generates the in-phase or orthogonal subcarrier signals and mixes them with the output signal of step 1.  
Step 3. A low-pass filter is used to filter the signal and retain the baseband signal with a 10 MHz bandwidth.  
Figure 2: The BOC(30,5)cos and 16 APSK integrated signal system. (**a**) Transmission link structure (**b**) Receiving link structure.  
The signal acquisition diagram is shown in Figure 3a.  
Step 1 converts the IF signal to the baseband without filtering the high-frequency signals. The power spectrum of the processed signal is shown in Figure 3b. Step 2 restores the BOCcos signal to the spread spectrum signal. In addition, it will move the baseband APSK signal to the 30 MHz \(+f_{I}\) frequency. In this stage, the power spectrum of the signal changes to that shown in Figure 3c. Finally, step 3 can filter the spread spectrum signal in the baseband as Figure 3d.  
After the preprocessing of the IF signal, the integrated signal can be compatible with the existing BOC signal processing system. In the paper, we adopt the fast Fourier transform (FFT) for the correlation to obtain rough estimates of the PN code and Doppler frequency.  
Figure 3: _Cont._
# Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
## 3 Time-Frequency Analysis Method Based on Wavelet Threshold Denoising and HHT
### Time-Frequency Analysis Method Combining Wavelet Threshold Denoising and HHT
RMSE reflects the proximity of the denoised signal to the original signal. The smaller the value is, the more obvious the obtained filtering effect is. Meanwhile, SNR reflects the ratio between the denoised and the noisy signal, and its value symbolism is contrary to that of RMSE. With regard to the denoising the bridge GNSS dynamic observation data, the pre-processing of dynamic data must retain the useful signal as much as possible whilst removing the noise to the greatest extent. Thus, the two evaluation indicators must be considered together when evaluating the denoising effect of non-stationary signals. That is, the _RSME_ of the denoised and original signals and the SNR of the filtered signal should meet the requirements.
# Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes
###### Abstract  
Highway systems play a key role in providing mobility to society, especially during emergency situations, including earthquakes. Bridges in highway systems are susceptible to damage from earthquakes, causing traffic capacity loss leading to a serious impact on surrounding areas. To better prepare for such scenarios, it is important to estimate capacity loss and traffic disruptions from earthquakes. For this purpose, a traffic-capacity-analysisbased methodology was developed to model the performance of a transportation network immediately following an earthquake using a macroscopic multi-level urban traffic planning simulation model EEME4. This method employs the second order linear approximation (SOLA) traffic assignment and calculates total system travel time for various capacity loss scenarios due to bridge damage from earthquakes. It has been applied to Pohang City in Korea to evaluate the performance of traffic networks in various situations. The results indicate a significant increase in travel time and a decrease in travel speed as the intensity of an earthquake increases. However, the impact on traffic volume varies depending on the bridges. It is assumed that the location of the bridges and traffic routing patterns might be the main reason. The results are expected to help estimate the impact on transportation networks when earthquakes cause traffic capacity loss on bridges.  
bridge traffic analysis; system travel time; macroscopic assignment; earthquake analysis +
Footnote †: journal: Applied Sciences  
17 February 2021  
Accepted: 1 April 2021  
Published: 3 April 2021
###### Abstract  
The automatic inspection of belt conveyors gathers increasing attention in the mining industry. The utilization of mobile robots to perform the inspection allows increasing the frequency and precision of inspection data collection. One of the issues that needs to be solved is the location of inspected objects, such as, for example, conveyor idlers in the vicinity of the robot. This paper presents a novel approach to analyze the 3D LIDAR data to detect idler frames in real time with high accuracy. Our method processes a point cloud image to determine positions of the frames relative to the robot. The detection algorithm utilizes density histograms, Euclidean clustering, and a dimension-based classifier. The proposed data flow focuses on separate processing of single scans independently, to minimize the computational load, necessary for real-time performance. The algorithm is verified with data recorded in a raw material processing plant by comparing the results with human-labeled objects. The proposed process is capable of detecting idler frames in a single 3D scan with accuracy above 83%. The average processing time of a single scan is under 22 ms, with a maximum of 75 ms, ensuring that idler frames are detected within the scan acquisition period, allowing continuous operation without delays. These results demonstrate that the algorithm enables the fast and accurate detection and localization of idler frames in real-world scenarios.  
density histogram; RANSAC; Euclidean clustering +
Footnote †: journal: Applied sciences
## 3 Methodology
### Time Series 2D Transformation  
For bearing vibration signals, the variations at each time point are influenced not only by the adjacent time regions but also by the changes in preceding and subsequent periods. This variation is referred to as intra-cycle variation and inter-cycle variation: intra-cycle variation refers to short-term patterns within a single cycle, while inter-cycle variation reflects long-term trends and patterns across several consecutive cycles. However, it is often challenging for one-dimensional time series to simultaneously showcase these two types of variations. To overcome this limitation, this section proposes a method for temporal two-dimensional transformation based on periodic characteristics, which extends the original vibration data into a two-dimensional space, as illustrated in Figure 2.  
In this transformation, each column represents all observation points within a single time cycle, while each row contains observations at the same phase across different cycles.  
Figure 2: Schematic diagram of the periodic feature-based time series 2D transformation.  
By extending the original one-dimensional data into a two-dimensional space using this method, the resulting two-dimensional tensor accommodates time points within a cycle and sequentially connects multiple cycles. This showcases the complete structure of the time series on a two-dimensional plane, effectively overcoming the representational limitations of the original one-dimensional time series. The approach unifies both intra-cycle and inter-cycle variations in a two-dimensional space, facilitating the input of the transformed data into deep learning models for feature recognition and subsequent training. This provides a new perspective for enhancing fault diagnosis of vibration signals, making complex temporal patterns easier to identify and analyze, ultimately contributing to improved accuracy and efficiency in fault diagnosis.
## 2 The Proposed Approach for Fault Diagnosis of Wheelset Bearing
### Maximal Overlap Discrete Wavelet Packet Transform  
Different from WPT, MODWPT [37; 38; 39] ignores down-sampling process at each decomposition level and enjoys a full-time resolution, which is indispensable to the implementation of this proposed approach in this paper. In order to introduce the MODWPT understandably, the maximal overlap discrete wavelet transform (MODWT) is introduced at first briefly. MODWT can be seen as a modified edition of the discrete wavelet transform (DWT), which is a highly redundant non-orthogonal wavelet transform and does not have a strict requirement for the sample length of \(N\)[39]. Here, \(\{g_{l},l=0,1,\ldots,L-1\}\) and \(\{h_{l},l=0,\)\(1,\ldots,L-1\}\) are assumed as low-pass and high-pass filters of MODWT, and \(L\) represents the length of the filter. \(g_{l}\) and \(h_{l}\) satisfy the equation constraints as follows:  
\[\begin{array}{c}\sum_{l=0}^{L-1}g_{l}^{2}=\frac{1}{2},\sum_{l=0}^{L-1}g_{l}g_ {l+2n}=\sum_{-\infty}^{+\infty}g_{l}g_{l+2n}=0\\ h_{l}=(-1)^{l}g_{L-l-1},g_{l}=(-1)^{l+1}h_{L-l-1}\end{array} \tag{1}\]  
In order to overcome the shortcoming that the points of signal data half at each decomposition level due to down-sampling process, a new filter at different \(j\)th level by inserting \(2^{j-1}\) zeros in \(\{g_{l}\}\) and \(\{h_{l}\}\)is rebuilt by MODWT:  
\[\begin{array}{c}\left\{\overline{g}_{l}\right\}=\{g_{0},0,\ldots,0,g_{1},0, \ldots,0,\ldots,g_{L-2},0,\ldots,0,g_{L-1}\}\\ \left\{\overline{h}_{l}\right\}=\{h_{0},0,\ldots,0,h_{1},0,\ldots,0,\ldots,h_{ L-2},0,\ldots,0,h_{L-1}\}\end{array} \tag{2}\]  
Based on Mallat pyramid algorithm, the \(j\)th level scaling transform coefficients \(V^{\prime}_{j,l}\), and wavelet transform coefficients \(W^{\prime}_{j,l}\) of MODWT are as follows:  
\[\begin{array}{c}V^{\prime}_{j}=\sum_{l=0}^{L-1}\overline{g}_{l}V^{\prime}_{ j-1,(t-2^{j-1}l)\text{mod}N}\left(t=0,\ldots,N-1\right)\\ W^{\prime}_{i,l}=\sum_{l=0}^{L-1}\overline{h}_{l}V^{\prime}_{j-1,(t-2^{j-1}l) \text{mod}N}\left(t=0,\ldots,N-1\right)\end{array} \tag{3}\]  
Similar to DWT, MODWT also cannot decompose the high-frequency band of the signal. As we know, the discrete wavelet packet transform (DWPT) can make up for the drawback that the DWT can't further decompose the high-frequency band. Therefore, similar to the DWPT deriving from DWT method, the MODWPT is developed on the basis of MODWT to further decompose the high-frequency band. The MODWPT not only has all the advantages of MODWT, but can also decompose the high-frequency band. The MODWPT coefficients \(W_{j,n}\) can be calculated as:  
\[\begin{array}{c}W_{j,n,t}=\sum_{l=0}^{L-1}r_{n,t}W_{J-1,[n/2],(t-2^{j-1}l) \text{mod}N}\left(t=0,\ldots,N-1\right)\\ r_{n,t}=\left\{\begin{array}{ll}\overline{g}_{l},&\text{if $\text{mod}4=0$ or $3$ ;}\\ \overline{h}_{l},&\text{if $\text{mod}4=1$ or $4$.}\end{array}\right.\end{array} \tag{4}\]  
where \(j\) is the decomposition level, and \(n\) is the number of frequency bands under each decomposition level.
# Development of a Thermal Analysis Tool for Linear Machines
## 1 Introduction
Commercial packages, such as _Motor-CAD_, have facilitated the usage of such models for rotary machines, thanks to predefined thermal networks. _Motor-CAD_ even comes prepared with typical values of interface gaps between different parts of the machine, so the calibration of the models is usually performed by modifying simple parameters [5]. Analytical methods allow a very fast computation of working temperatures [1; 3; 4; 15; 16], so they are the way to go if iterative optimization methods are employed. As a reference, [PERSON] et al. [20], computed the transient thermal behavior of a linear permanent-magnet machine and a linear induction machine for electromagnetic launch systems. The computation was based on simple formulations, which allowed the authors to evaluate the temperature rise of the machines under different current density values. This analysis would be very time consuming if numerical methods were adopted. However, there are not linear machine models in _Motor-CAD_ yet. Thus, if a thermal network model is to be used, the network must be defined and its parameters calculated by the designer. For linear machines, both 2D [17; 18; 21] and 3D [19; 22; 23; 24] LP thermal networks have been used in the literature. In order to simplify the thermal networks, some of the authors propose to employ symmetries, thus halving the amount of elements per each applied symmetry [22; 23]. The secondary of the linear machines can also be left out of the model in long secondary applications because it can be assumed that its working temperature will be the same as the ambient temperature [22]. In general, the more elements there are in a LP model, the higher its accuracy will be. However, these models allow a precise prediction of the temperatures of a machine, even when a low number of elements is used [25]. The issue with the models in the literature is that they are specific to the machine that is being analyzed. The thermal model that is presented in this paper aims to serve as a generic thermal analysis tool for long-stroke linear machines. The model is developed using the thermal network library from _MATLAB Simulink_, which is a widely known software package between engineers. Hence, it is easy to implement. The article givesthe necessary guidelines for linear machine designers to implement the generic model, in order to use it as a reference when designing the machines. In order to validate the generic nature of the tool, the model is validated via several thermal tests to a linear induction machine and a linear switched-flux machine. Some recommendations are also given for the calibration procedure of the thermal model. The article is organized as follows: Section 2 introduces an overview of the thermal model. Section 3 describes the thermal networks of the different elements of the machine. The validation of the model is given in Section 4. The results are discussed in Section 5, and, finally, the conclusions of the work are listed in Section 6.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Calculation of \(Et_{0}\)  
Penman-Monteith (P-M) formula was used to estimate daily \(ET_{0}\) in this article which was proposed by the FAO (Food and Agriculture Organization). The P-M method estimates \(ET_{0}\) from hypothetical reference grass with an assumed height of 0.12 m, a fixed surface resistance of 70 m\(\cdot\)s\({}^{-1}\), and an albedo of 0.23 [30]. The formula is:  
\[ET_{0}=\frac{0.408\Delta(R_{n}-G)+\gamma\frac{900}{T_{mean}+273}U_{2}(e_{s}-e_ {a})}{\Delta+\gamma(1+0.34U_{2})} \tag{1}\]  
where \(ET_{0}\) is the daily reference evapotranspiration (mm/d), \(\Delta\) is the slope of the vapor pressure curve and \(\gamma\) is the psychometric constant (kPa/\({}^{\circ}\)C), \(R_{n}\) is the net radiation and \(G\) is the soil heat flux density (MJ\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)), \(T_{mean}\) is the mean air temperature (\({}^{\circ}\)C), \(U_{2}\) is the wind speed at 2 m height (m/s), \(e_{s}\) is the saturation vapor pressure and \(e_{a}\) is the actual vapor pressure (kPa).  
Reference evapotranspiration and pan evaporation both represent evaporative capacity of a basin under certain climatic conditions and their good relationship had been manifested by previous research [31]. The pan coefficient and correlation between them were calculated to verify the applicability of the P-M model (Figure 2). The correlation coefficient between pan evaporation and reference evapotranspiration was 0.71 with a pan coefficient of 1.75, which indicates that the P-M model is suitable for the analysis of reference evapotranspiration on the TP.  
Figure 1: Location of the study area and the distribution of meteorological stations.