esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis and Results
### Encryption Quality Metrics
#### 5.2.3 Histogram Analysis  
The definition of a histogram is a statistical graphical distribution of each discrete intensity level (also known as a \"gray level\") in a digital image into user-specified ranges. It displays the gray scale, the density of the gray-level distribution, the average luminance of an image, picture contrast, and so on. The histogram's horizontal axis displays the potential intensity values, while the vertical axis displays the number of pixels for each of these intensities [5]. The proposed MPFrFT and FrFT-coded OFDM ciphering approaches reflect identical histograms of the relevant source images, according to the reported histogram analysis. As a result, an encrypted image's statistical metrics are the same as those of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{3}{c}{**Encryption Scheme**} \\ \cline{2-3}  & \multicolumn{1}{c}{**MPFrFT**} & \multicolumn{1}{c}{**FrFT**} \\ \hline \multirow{2}{*}{\begin{tabular}{c} Camera \\ Peppers \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 27.8645 \\ 24.0981 \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 26.8317 \\ 24.2064 \\ \end{tabular} } \\ \hline \hline Boat & 21.8502 & 22.8703 \\ \hline \hline \end{tabular}
\end{table}
Table 11: _UACI_ comparison among the proposed MPFrFT and FrFT-coded OFDM using Cameraman, Peppers and Boat standard gray-scale test images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{3}{c}{**Encryption Scheme**} \\ \cline{2-3}  & \multicolumn{1}{c}{**MPFrFT**} & \multicolumn{1}{c}{**FrFT**} \\ \hline \multirow{2}{*}{\begin{tabular}{c} Lena \\ Cameraman \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} \(-0.0011\) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} \(-0.0631\) \\ \end{tabular} } \\ \hline \multirow{2}{*}{\begin{tabular}{c} Peppers \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} \(-0.0033\) \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} \(0.0012\) \\ \end{tabular} } \\ \hline \hline Boat & \(4.11\times 10^{-4}\) & \(0.0083\) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Correlation comparison among the proposed MPFrFT and FrFT-coded OFDM using Cameraman, Peppers and Boat standard gray-scale test images.  
matching source image. Table 13 shows the histogram analysis for the proposed MPFrFT-coded OFDM using Cameraman, Peppers and Boat standard gray-scale test images.
# Rebar Recognition Using Multi-Hyperbolic Attention in Faster R-CNN
## 4 Results  
In Figure 5, images (a) and (d) are sourced from the public simulation dataset, while the remaining images are derived from real detection data collected at the left entrance of the Husa Tunnel on the Tenglong Expressway in Yunnan Province (Tengchong end). Among these, image (a) contains five rebars, images (b) and (c) contain four rebars, and (f) contains nine rebars, while images (d) and (e) did not detect any rebar, indicating that the model can accurately identify rebar in real images with significant interference. In Figure 6, images (a), (b), and (c) represent detection results without the introduction of the DAS and HAT modules, where each image detected one rebar less than the actual count, whereas images (d), (e), and (f) illustrate detection results with the incorporation of the DAS and HAT modules, demonstrating that the inclusion of these modules enhances the model's detection performance for rebar.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss Weights (\(a,b,c,d\))** & **\(F1\) Scores** & **Accuracy** & **Recall** & \(AP\) \\ \hline \((1,1,1,1)\) & 0.83 & 72.21\% & 97.43\% & 94.93\% \\ \((1.5,0.5,1.5,0.5)\) & 0.83 & 72.80\% & 97.43\% & 96.34\% \\ \((0.5,1,0.5,1)\) & 0.78 & 66.15\% & 94.12\% & 91.86\% \\ \((1,0.5,1,0.5)\) & 0.82 & 71.55\% & 95.22\% & 91.77\% \\ \((0.5,1.5,0.5,1.5)\) & 0.81 & 70.46\% & 95.59\% & 92.06\% \\ \((0.5,0.5,1,1)\) & 0.79 & 66.84\% & 96.32\% & 92.18\% \\ \((1,1,0.5,0.5)\) & 0.81 & 70.46\% & 95.59\% & 92.06\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics of loss weight hyperparameter experiments.  
Figure 5: Detection results of rebars in tunnel lining (**a,d**): simulated images; (**b,c,e,f**): real tunnel lining images).  
Finally, to substantiate the efficacy of the model in practical engineering applications, the total amount of rebar was divided by the number of rebars identified by the model in a randomly selected sample of 50 ground-penetrating radar images, establishing the rebar identification rate as a key performance metric for the model's effectiveness in practice. The proposed model demonstrates a rebar detection and identification rate of 93.46%, an improvement of 0.94% compared to the baseline model.
# A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
## 2 Literature Review
### The Vehicle Routing Problem with Delivery Options
[3] allowed consumers to provide multiple delivery locations as well as corresponding time window constraints in urban logistics and distribution using a delivery model with delivery options. They solved this problem by using a large neighborhood search algorithm to improve the probability of consumers receiving the goods in time. [PERSON] et al. [37] proposed a vehicle routing problem with time windows and flexible delivery locations. They set the capacity of different service locations based on a real-time therapist scheduling problem. To solve this problem, they developed a hybrid adaptive large neighborhood search algorithm and analyzed it experimentally on real hospital data, which greatly improved the quality of the solution. [PERSON] et al. [2] proposed a vehicle routing problem that considers customer preferences for delivery options. They used a probabilistic model to represent the degree of customer preference for deliveries to different locations and different time windows by analyzing customer data and further used an ALNS approach to solve the problem. Collectively, these studies highlight how delivery options enhance VRP flexibility, enabling logistics providers to reduce operational costs while improving customer satisfaction. However, many of these works focus primarily on cost efficiency and operational feasibility, often treating customer satisfaction as a secondary or implicit outcome. Given the proven effectiveness of ALNS in handling complex VRP variants with time window constraints and delivery flexibility, we adopt an MSALNS algorithm in this work. This allows us to better capture customer satisfaction as a primary optimization objective while maintaining high-quality solutions for cost and efficiency. The MSALSN builds on the ALNS framework by incorporating customer satisfaction into the design of neighborhood structures, which expands the search space and accelerates convergence. In addition, a multi-annealing mechanism is used to prevent the algorithm from getting stuck in local optima too early, thus improving solution quality [38; 39].
## 4 Experimental Results  
In order to verify the effectiveness of DRKMSVM in short text classification, we conducted experiments on 5 public short text datasets and made comparisons with other well-known methods, including DNMSVM, CNN, SVM with radial basis function kernel (RBF-SVM) and NB. To further illustrate the performance of DRKMSVM, we analyzed the influence of the structure of recurrent network on the performance of DRKMSVM. All the experiments are completed under the framework of the TensorFlow [40], with a server of 64-bit windows 7 operating system, Intel(r) Xeon(r) E5-2643 processor and NVIDIA Tesla K40c GPU.
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
## 4 Multi-Physics Numerical Model  
### The Proposed Multi-Domain Co-Simulation Model  
Figure 2 depicts the co-simulation model architecture, the core of the model developed in MATLAB/Simulink(r), which discretizes the time of the simulation interfacing the different physical domains. This communication process between the various environments is discretized for each physical domain through a dedicated sample frequency, in order to coherently represent all the characteristic behaviors of the entire phenomenon and to improve the computation performances, while avoiding the risk of aliasing. Inside the MATLAB/Simulink master ambient, two main functions are also implemented: the high-frequency parameter estimator that computes the needed static and dynamic electro-magnetic parameters based on the Equations introduced in Sections 2 and 3, and the firmware and control logic routine; this last task allows a control routine to be defined in the case that the excitation on the emitter side is governed by a microcontroller.  
The magnetic domain block receives as an input the interface air gap and updates the magnetostatic model on the base of the reached configuration. The solution of the volume-based discretized analysis of the 3D magnetic field at each simulation instant is used to compute the parameters of auto and mutual inductance and the coupling factor.  
At the same integration time step, the above information is used by the analog electronic domain to update the NETLIST and to solve the set of PDE/ODE describing the circuit, as well as to return all the voltage and current waveforms to the leading routine at every node and branch.  
The co-simulation of these magnetic and analog electronic domains is allowed by the implemented Python-based communication algorithms, which, through dedicated API, allow the integration of the ESRF RADIA package and the NGSpice solvers. In detail, Spice, which over the years has been consolidated as the standard tool for the simulation of analog electronic circuits for both industry and research, has greatly improved the design capabilities of the electronics domain. The development of this routine aims to extend this design boost to those circuits, which, due to the application complexity, require a direct interaction between different physical domains, as in the case of mechatronic applications.  
Finally, the reported architecture also proposes the integration of the mechanical domain. This is because, despite the case of the system under analysis, which presents a simple and basic assembly replica of the experimental measurement apparatus, from a broad point of view of a general application, the two coils could be mounted on a real coupling device exhibiting uncontrollable and random gap variation. The possibility of introducing an integration of the mechanical domain through multibody dynamics (MBD) would, thus, allow the model's prediction capabilities to be extended, and enable a better understanding of the behavior of the device in a real case scenario.
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Model Training  
After the preprocessing stage, the dataset was divided into training and testing sets at an 80%:20% ratio to measure model performance. ML models and MLP network were trained on 80% of the data to ensure that the classification algorithm's training can be generalized well to new data.
## 2 Feature Identification of Intelligent Driving Technical Routes
### Sensor Combination  
Intelligent driving environment sensors mainly include cameras, millimeter wave (MMW) radars and lidar. Two technical routes have been developed around sensor combinations in the industry; one is \"multi-source fusion\", adopted by most automotive enterprises, and the other is \"vision-only\", represented by Tesla. Recently, Toyota also announced that it will adopt \"vision-only\" to develop high-level intelligent driving, which adds another uncertainty to the technical route dispute.  
The multi-source fusion route is based on the combination of heterogeneous sensors to form a strong perception ability to handle complex scenarios. MMW radars provide stable ranging capability, the camera provides accurate recognition capability, and lidar provides rich 3D details. The combination of the three makes the system have better security redundancy. Some researchers believe that multi-source fusion is the best perception solution because it physically breaks through the limitations of human perception and gives the system the ability to perceive the environment from more dimensions [26].  
The vision-only route is only equipped with cameras on the vehicle. Tesla researchers believe that the logic of visual perception fits the human eye and brain, and it is suitable for realizing high-level artificial intelligence through convolution neural networks and deep learning. The features of low cost, mature technology and uniform data format also make vision-only easier to realize productization. However, some researchers believe that cameras are easily interfered with by the environment, so their reliability is not ideal, and there are extreme cases that are difficult to handle [27].
# Inspection of Coated Hydrogen Transportation Pipelines
## 3 Pipeline Inspection
### Microwaves NDE (MW)  
With advances in technology dielectric materials with superior physical properties are replacing or being used with metals as coatings [75]. Microwave NDT works well for testing specimens with such coatings because, unlike traditional NDT methods, it has little absorption in dielectric materials [75]. Yet, it still interacts well enough with dielectric materials to respond to \"structures and defects\" under the dielectric coating [75]. The use of an \"X-band rectangular waveguide with a vector network analyser\" for NDE has also been posited [75]. Defects can then be imaged by analysing the reflection coefficient at the \"waveguide aperture\". Recent developments in the use of microwave NDE also involve the use of machine learning and artificial intelligence to mitigate the drawbacks of MW methods [76]. In the context of pipeline integrity management, apart from using the \"Microwave Open-Ended Waveguide\" technique for the detection of defects, \"Guided Microwave Testing (GMT)\" was shown to be capable of detecting corrosion under insulation (CUI), while \"Chipless Radio-Frequency Identification Sensor System\" was said to be applicable to SHM [76].
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 3 Overview of Deep Learning-Based Visual Multi-Object Tracking
### MOT Evaluating Indicator
MT (Mostly Tracked): the number of objects that are successfully tracked 80% of the time as a percentage of all tracked objects. ML (Mostly Lost): the percentage of the number of objects that satisfy the tracking success 20% of the time out of all the objects tracked. FM (Fragmentation): evaluate tracking integrity, defined as FM, counted whenever a trajectory changes its state from tracked to untracked, and the same trajectory is tracked at a later point in time. HOTA (Higher Order Metric): A higher order metric for evaluating MOT proposed by [34]. Previous metrics overemphasized the importance of detection or association. This evaluation metric explicitly balances the effects of performing accurate detection, association, and localization into a unified metric for comparing trackers. HOTA scores are more consistent with human visual evaluations. \[HOTA=\int_{0}^{1}HOTA_{\alpha}d\alpha \tag{5}\]  
\[HOTA_{\alpha}=\sqrt{\frac{\sum_{c}A(c)}{|TP|+|FN|+|FP|}} \tag{6}\]  
\[A(c)=\frac{|TPA(c)|}{|TPA(c)|+|FNA(c)|+|FPA(c)|} \tag{7}\]  
where \(\alpha\) is the IoU threshold, and \(c\) is the number of positive sample trajectories. In the object tracking experiment, there are predicted detection trajectories and ground truth trajectories. The intersection between the two trajectories is called true positive association (_TPA_), and the trajectory outside the intersection in the predicted trajectory is called false positive association (_FPA_). Detections outside the intersection in ground truth trajectories are false negative associations (_FNA_).
# Optimizing CNT Loading in Antimicrobial Composites for Urinary Tract Application
###### Abstract  
Several methodologies have been implemented with the intent of preventing or reducing the formation of biofilms on indwelling urinary devices. The use of carbon nanotubes (CNTs) in the biomedical field has been increasing, particularly in the production of antimicrobial and antifouling coatings. Despite their proven antimicrobial properties, their use as coating materials for urinary tract devices (UTDs) is still poorly documented. In the present work, CNT/poly(dimethylsiloxane) (PDMS) composite materials containing different CNT loadings were prepared and further tested against _Escherichia coli_ under conditions prevailing in UTDs. Besides CNT loading optimization, textural modifications were also introduced on the surface of CNTs to improve the antibioticfilm properties of the final composites. Material characterization included the textural evaluation of CNTs and the assessment of surface morphology by scanning electron microscopy, while the surface hydrophobicity was determined by contact angle measurements. Biofilm analysis was performed by determining the number of culturable and total cells and by confocal laser scanning microscopy. Results revealed that, by filling the PDMS matrix with 3 wt% CNT loading, a significant reduction in cell culturability (39%) can be achieved compared to PDMS. Additionally, the textural modifications induced by ball-milling treatment proved to be effective on the inhibition of biofilm formation, reducing the amount of biofilm per surface area, biofilm thickness and surface coverage in 31, 47 and 27%, respectively (compared to surfaces where CNTs were not ball-milled).  
carbon nanotube composites; poly(dimethylsiloxane); _Escherichia coli_; antibioticfilm activity; urinary tract devices +
Footnote †: journal: Applied Sciences
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Extended Finite Element Method (XFEM)  
The XFEM is a robust method for simulating crack growth without requiring a direct mesh representation of the crack shape. By incorporating enrichment functions into the standard finite element model, XFEM effectively models complex crack patterns and eliminates the necessity for mesh regeneration as the crack extends. These additional functions, tailored to the crack's geometry, enhance the finite element approximation near the crack tip, enabling XFEM to accurately depict stress singularities and model crack propagation with precision, all while maintaining the original mesh structure. XFEM is particularly suitable for problems involving crack nucleation, crack branching, and crack coalescence. Numerous software programs have been developed for modeling crack growth using the XFEM. These programs offer various features and capabilities to simulate and analyze crack growth in different materials and structures. The following are some of the notable software programs that utilize the XFEM method for crack growth simulation:
## 3 Numerical Results
### An Arbitrary Radiator in the Half-Space Domain
#### 3.2.1 Internal Eigenfrequency Problem  
The arbitrary symmetric object shown in Figure 1 pulsating at the second internal resonance frequency (148.5 Hz) in an infinite air domain was also calculated using the present method with \(F\) = 0.35. The normal velocity amplitude was assumed to be 1. The convergence history illustrated in Figure 9 is robust.  
The amplitudes of the radiating pressures are shown in Figure 10. The amplitudes in Figure 10a,b are almost the same because the geometry of this radiator is bilaterally symmetrical. As the elements are closer to the XY plane, the radiating amplitudes are stronger due to the positive symmetry. Face 5 is a symmetrical cone; thus, the amplitude distribution on Face 5 plotted in Figure 10c is also symmetrical, and the center of the symmetry is the center of Face 5.  
Figure 9: Convergence history of the arbitrary symmetric object pulsating at the internal resonance frequency of 148.5 Hz (\(F\) = 0.35 in Equation (4)).
# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 3 System Model  
In this paper, we propose an adaptive data collection scheme for homogeneous WSNs, where a set of sensor nodes \(S\) are deployed randomly in the sensing area. A single MS will move in the network area to collect the sensed data. We assume that the MS moves at a constant speed and it stops at RPs to collect the data. The stopping duration at each RP is assumed to be fixed and enough to collect the data from all corresponding sensors. For energy consumption, the energy model proposed in [20] is assumed. Using this model, the energy consumed (\(E_{T}\)) to transmit a \(k_{b}\)-bit packet between two sensor nodes \(\mathrm{s}_{1}\) at location (\(x_{1}\), \(y_{1}\)) and sensor node \(\mathrm{s}_{2}\) at location at (\(x_{2}\), \(y_{2}\)) is calculated as:  
\[E_{T}=\left\{\begin{array}{cc}k_{b}E_{elec}+k_{b}\varepsilon_{f}sd^{2},&d<d_ {0}\\ k_{b}E_{elec}+k_{b}\varepsilon_{amp}d^{4},&d\geq d_{0}\end{array}\right\} \tag{1}\]and the energy consumed by sensor node \(s_{2}\) to receive the \(k_{b}\)-bit packet (\(E_{R}\)) is calculated as:  
\[E_{R}=k_{b}\ E_{dec} \tag{2}\]  
where \(E_{dec}\) is the electronics energy; \(\varepsilon_{f}\) and \(\varepsilon_{amp}\) denote the amplifier energy that depends on the required receiver sensitivity and the receiver noise figure, respectively; and \(d\) is the distance between sensor nodes s\({}_{1}\) and s\({}_{2}\) and is given as:  
\[d=\sqrt{\left(x_{1}-x_{2}\right)^{2}-\left(y_{1}-y_{2}\right)^{2}} \tag{3}\]  
The notations that are used in this paper are shown in Table 1.
## 2 Materials and Methods
### Method  
#### 2.3.1 Method Framework  
The process framework of this article is shown in Figure 2. The research is based on PlanetScope satellite remote-sensing images. After correction, seamless mosaic, clipping, resampling, and other preliminary data-processing operations on the original images, we build NDVI of the study area in 12 continuous months by using the red band and the near-infrared band. Then, through band-stacking of 12-month NDVI images, the data input in the subsequent classification can include the spatial position, spectral characteristics, and time series information. Image classification is realized by MLC and the deep-learning method in pixel-based direct classification. Finally, we analyzed the specific classification effect based on the overall classification results of the study area.  
Figure 2: Method framework of LUCC research based on NDVI from PlanetScope satellite images.
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 2 Review of Related Studies
### Distinctions from Prior Research  
Prior research on traffic volume prediction has predominantly utilized historical data and conventional statistical methods. These approaches typically require substantial time  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Methodology** \\ \hline [PERSON] and [PERSON] [12] & \\ [PERSON] and [PERSON] [13] & Linear and nonlinear regression \\ Korea Institute for Health and Social Affairs [10] & \\ \hline \hline \multirow{3}{*}{[PERSON] et al. [14]} & Historical average algorithms \\  & Moving average \\ \cline{1-1}  & Smoothing techniques \\ \hline [PERSON] and [PERSON] [12] & \\ [PERSON] and [PERSON] [13] & Autoregressive linear processes \\ \hline [PERSON] et al. [15] & ARMA \\ [PERSON] et al. [17] & ARIMA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Traffic volume prediction studies based on statistical learning algorithms.  
for generating predictions and may fail to forecast volumes for specific traffic segments. With recent advances in computational technology, deep learning techniques have been introduced for predicting traffic volumes. However, to date, no studies have employed LSTM models for this purpose. This study distinguishes itself by enhancing the time series deep learning framework, specifically through modifications to the LSTM forget gate, thereby improving the accuracy of traffic volume predictions.