esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physicochemical Properties of Chicken Meat
#### 3.2.2 Color Values
Both treated and untreated meat exhibited increasing trends in color intensity (\(C^{*}\)) similar to yellowishness. The \(C^{*}\) values for the control, MIC-1, MIC-2, and MIC-4 on day 0 were 4.84 \(\pm\) 0.58, 5.16 \(\pm\) 0.56, 5.03 \(\pm\) 0.94, and 4.87 \(\pm\) 0.40, respectively; on day 14, the values increased to 6.62 \(\pm\) 0.87, 6.73 \(\pm\) 0.59, 7.54 \(\pm\) 0.45, and 8.97 \(\pm\) 0.53, respectively. A decreased tendency was discernible for MIC-1 and MIC-2 on day 10, before beginning to decline again on day 14, while the increasing trend of control and MIC-4 was maintained throughout the storage. At the end of the storage period, an increase in \(h^{*}\) was found in all meat samples. By elevating the BAC level in meat, the impact of AITC on \(h^{*}\) values could be observed effectively; as AITC concentration rose, \(h^{*}\) values increased likewise. One of the primary quality attributes that consumers look for in poultry meat products is their physical properties, such as shelf life or appearance [8]. [PERSON] et al. [3] found that a release rate of 0.6 ug/h of AITC did not affect the color of the chicken meat. However, our findings showed that a release rate of 1.2 ug/h of AITC results in discoloration of the chicken's meat surface. The authors concluded that low concentrations of AITC have the potential to maintain color values \(L^{*}\), \(a^{*}\), and \(b^{*}\) of ground chicken meat without deteriorating the appearance or attractiveness to consumers.
## 2 Method
### Autoregressive Integrated Moving Average (ARIMA) Model
#### 2.6.2 Parameter and Diagnostic Checking  
After passing the stationarity test of time series data, we fit the ARIMA model by estimating the values of p and q. For a model that can adequately describe the trends of the GPP sequence, the distribution of the residuals should be similar to white noise. Meanwhile, the goodness of fit statistics of the model should be the most accurate among the candidate models. Based on this, we considered the following test statistics: mean absolute error (MAE), root mean squared error (RMSE), relative mean absolute error (rMAE), and relative root mean square error (rRMSE), the specific definitions of these statistics can be found in the Appendix A. Taken together, we used these metrics to assess the fitting effect of the ARIMA model on the GPP series.
## 3 Proposed Tracking Algorithm  
According to Equation (7), the multipath and other factors in the echo signal of the receiver cannot be eliminated, and the characteristics of the reflected signals vary with the material and size of obstacles. Therefore, in an NLOS environment, the dynamic positioning accuracy of the proposed Bluetooth array angular measurement and positioning system decreases sharply. Therefore, if only relying on the positioning accuracy of the system, the high-precision positioning within the coverage area cannot be guaranteed, so the tracking algorithm is particularly important. After comparative analysis, this paper selects strong tracking kalman filtering algorithm as a basic tracking algorithm and optimizes it.
## 2 Establishment of a Method for Evaluating Rock Damage Effects
### Basis of Evaluation
#### 2.1.1 Evaluation of Rock Damage Effects Based on Classical Strength Theory  
For analysis of numerical simulation results based on classical strength theory, the main consideration is the damaging effect of stress wave on the rock, and the damage degree of the rock is evaluated by the Mises damage criterion according to the location of the study area, as in Equation (1):  
\[\sigma_{vm}\geq\begin{cases}\sigma_{cd}&(\text{Crushing zone})\\ \sigma_{td}&(\text{Fracture area})\end{cases} \tag{1}\]  
\[\sigma_{vm}=(1/\sqrt{2})\Big{[}(\sigma_{1}-\sigma_{2})^{2}+(\sigma_{2}-\sigma_ {3})^{2}+(\sigma_{3}-\sigma_{1})^{2}\Big{]}^{1/2} \tag{2}\]  
where \(\sigma_{vm}\) is the Mises effective stress; \(\sigma_{cd}\) is the dynamic compressive strength of the rock; \(\sigma_{td}\) is the dynamic tensile strength; and \(\sigma_{1}\), \(\sigma_{2}\), \(\sigma_{3}\) are the three principal stresses.  
Following the detonation of the explosives, the stress wave reflects and stretches at the free surface, and the rock in this range mainly exhibits tensile damage. Mises damage criterion is adopted to evaluate the damaging effect of the rock. However, the hole spacing of the peripheral holes is small, the depth of the explosive buried is deep relative to its diameter, the explosive surroundings can be considered as an infinite rock body, and the location of the boundary between the crushing zone and the fissure area can not be accurately determined. Furthermore, in numerical simulation calculations, describe the process of detonation and expansion by combining high-energy explosives with the JWL state equation. However, in the fracture area, the tensile stress generated at the crack tip by the explosion gases is neglected, leading to the calculated stress values being lower than the actual stresses. Based on the above analysis, the evaluation of rock damage effects using the stress changes in the classical strength criterion will deviate from the actual damage of the rock mass. This situation is more prominent in models with a poor description of the stretching behavior [22]. Therefore, it is necessary to introduce reliable indicators based on classical strength theory to comprehensively evaluate rock damage effects.
## 9 Results and Discussion
### Overall Latency  
We evaluated the overall latency of our system in comparison to the baseline methods, as shown in Figure 8a. The Least Connection (LC) method demonstrated the lowest overall latency, with approximately an 18% improvement over the hybrid model. Hot Concurrency (HotC) showed comparable performance to the hybrid model, while OpenWhisk (OW) performed the worst due to its inability to handle sudden spikes in workload effectively. A more detailed examination of each workload's latency, presented in Figure 8b, reveals that although HotC achieves lower latency than the hybrid model in the IR and EG workloads, the hybrid model outperforms HotC in the MM and PN workloads. LC maintains the lowest latency across all workloads when compared to both the hybrid model and the other baseline methods. However, these results must be interpreted in the context of container utilization and SLA violation rates. In other words, while LC and HotC (particularly under lighter workloads) offer reduced overall latency, they do so at the expense of higher SLA violations and less efficient use of available containers.
## 2 Calibration Setup Parameters of Multilateration Method
### Machine Tool Setup Parameters  
As mentioned above, kinematic errors are significantly sensitive to thermally induced effects. To avoid the thermal expansion of its structure, the studied machine tool was customized. Three different cooling systems controlled the temperature of the numerical controller card, the drive motors, and the spindle (DMG MORI, Roissy, France). Moreover, a set of thermal probes (ETALON,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Source** & **Parameter** & **Unit** & **Value** \\ \hline \multirow{4}{*}{Machine tool} & Warming cycle & - & Yes \\  & Plate material & - & Steel \\  & Feedrate & mm/min & 1000 \\ \hline \multirow{4}{*}{Tracking laser} & Type of pattern & - & Box \\  & Sampling step & mm & 10 \\  & Acquisition time & s & **Optimized** \\  & Number of offsets & - & 4 \\  & Offset size & mm & **Optimized** \\  & Number of TL positions & - & **Optimized** \\ \hline Environment & Room temperature & \({}^{\circ}\)C & 20 \\ \hline \end{tabular}
\end{table}
Table 1: Setup parameters.  
Braunschweig, Germany) was placed on the machine tool and used to compensate the thermal drift of the linear guides. Before starting the measurement, a warming cycle was applied to ensure that the thermal expansion of these guides is stabilized. This warming up of the linear axes of the machine tool consisted of a CNC program by simultaneously moving the three axes of the machine. The toolpaths can cover the entire working volume of the studied machine. The feed rate of the three axes was set to 5000 mm/min. The thermal sensors (ETALON, Braunschweig, Germany) placed near the axes allowed for the monitoring of the temperatures until thermal stabilization of the mechanical structure. Preheating time lasted about 20 mins before the beginning of the measurements. Due to the excessive weight of the TL, this appliance was bound to the machine bed via a self-made support plate which was also subject to thermal expansions. As the machine was mainly composed of steel, the same material was used for it. Finally, a compromise concerning the feedrate had to be made between the limitation of the dynamic effects of the machine tool and the calibration time. A rate of 1000 mm/min is a wise choice in that it minimizes these two constraints.
# SPEEDY Quantum Circuit for Grover's Algorithm
## 4 Evaluation
### Resource Estimation  
Table 1 shows the quantum circuit resource estimation results for SPEEDY encryption. We estimated resources for rounds 6, 14, and 28 other than round 7 to evaluate the strength of security for each round. Based on SPEEDY-7-192, 4224 qubits, 1792 CCCCCX gates, 3584 CCCCX gates, 10752 CCCX gates, 10,304 Toffoli gates, 13,632 CNOT gates, and 2118 X gates were used. The estimated quantum resource is proportional to the number of rounds.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} A & Ampere \\ AI & Artificial Intelligence \\ BMS & Battery Management System \\ BPTT & Back-propagation Through Time \\ CC & Coulomb-Counting \\ CV & Cross-Validation \\ ECM & Equivalent Circuit Model \\ EIM & Electrochemical Impedance Model \\ EIS & Electrochemical Impedance Spectroscopy \\ EKF & Extended Kalman Filter \\ EM & Electrochemical Model \\ EV & Electric Vehicle \\ EWMA & Exponentially Weighted Moving Average \\ EWMS & Exponentially Weighted Moving Standard Deviation \\ FPGA & Field Programmable Gate Arrays \\ GPU & Graphical Processing Unit \\ HWFET & EPA Highway Fuel Economy Test Cycle \\ HPO & Hyperparameter Optimization \\ HPPC & Hybrid Power Pulse Characterisation \\ KF & Kalman Filter \\ LA92 & California Unified Driving Schedule \\ LSTM & Long Short-Term Memory \\ MAE & Mean Absolute Error \\ MAPE & Mean Absolute Percentage Error \\ MASE & Mean Absolute Scaled Error \\ MaxAE & Max Absolute Error \\ ML & Machine Learning \\ MSA & Multi-Step Ahead \\ MPSoC & Multi-Processor System-on-Chip \\ MSE & Mean Squared Error \\ NCA & Nickel Cobalt Aluminum (oxide) \\ OCV & Open Circuit Voltage (V) \\ PHM & Prognostics and Health Management \\ RMSE & Root Mean Squared Error \\ RNN & Recursive Neural Network \\ RUL & Remaining Useful Life \\ SHAP & SHapley Additive exPlanation \\ SOC & State of Charge \\ SoC & System-on-Chip \\ SPKF & Sigma Point Kalman Filter \\ SSE & Sum-squared error \\ UDDS & Urban Dynamometer Driving Schedule \\ UKF & Unscented Kalman Filter \\ US06 & Supplemental Federal Test Procedure driving schedule \\ V & Voltage \\ xAI & Explainable Artificial Intelligence \\ \end{tabular}
## 2 Literature Review
### Transportation Fairness  
The currently recognized concept of transportation fairness originates from the project report of the International Joint Highway Research in 1994: \"Transportation fairness refers to the allocation of costs and benefits generated by a policy, typically considering various demographic groups\" [25; 26]. [PERSON] [27] provided a comprehensive overview of this concept, suggesting that transportation fairness should include horizontal fairness, vertical fairness considering different classes and incomes, and vertical fairness considering differences in transportation abilities and needs.  
Scholars have not only researched the influencing factors of transportation fairness but also explored the fairness issues between different modes of transportation. For instance, [PERSON] [28] et al. analyzed factors such as residents' travel efficiency, road infrastructure quality, travel costs, and the impact of transportation facilities on the environment, summarizing the impact of these factors on transportation fairness. Scholars have also studied various approaches to measuring transportation fairness. For example, [PERSON] and [PERSON] utilized mathematical models such as Lorenz curves and the Gini coefficient to investigate transportation fairness issues in the Melbourne area.  
As the principle of people-oriented development gains increasing recognition, scholars have also studied the application of fairness in signal timing optimization research. [PERSON] [29] developed a heuristic solving algorithm that combines penalty functions and simulated annealing methods, incorporating both environmental and fairness objectives into traffic signal timing problems by maximizing traffic capacity and minimizing traffic emissions. [PERSON] [30] proposed a heuristic solving algorithm based on harmony search and penalty function methods, optimizing traffic signal timing schemes in urban road networks by considering traffic capacity and fairness constraints. [PERSON] [31] proposed a dual-objective signal timing simulation optimization model based on uncertainty by balancing the Atkinson index (evaluating transportation fairness) and average travel time (evaluating transportation efficiency). With the widespread application of information entropy in engineering, technology, and socioeconomics, explorations in the field of transportation have also been conducted. [PERSON] [32] and others proposed a transportation fairness evaluation method considering regional fairness and fairness of benefit attribution to different groups based on the Wilson entropy model. [PERSON] [33] and others designed a phase difference optimization algorithm for the line control system using information entropy theory and multi-attribute decision-making methods, with travel time, vehicle delay, and queue length as evaluation indicators.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 4 Problem Solution  
To solve the proposed optimization problem (24), we first divide it into two suboptimal problems, then solve the suboptimal problems one-by-one, and finally apply a joint iterative algorithm to obtain the optimal solution.
## 4 Results and Discussion
### Data Quality Improvement and Application of Results
#### 4.3.4 Improvements in the Classification Problem  
While state-of-the-art CNN architectures have been utilized in this study, several enhancements have been implemented to improve the classification of pedestrian crash diagrams. These refinements optimize model performance, data preprocessing, and computational efficiency to ensure more reliable and practical results.  
One key improvement was adopting customized preprocessing techniques to enhance data consistency and model performance. This involved resizing images while preserving their aspect ratios, normalizing pixel values, and structuring the dataset to ensure uniformity. These preprocessing steps minimized noise and improved feature extraction, improving classification outcomes.  
Additionally, feature-specific CNN training was employed. Unlike conventional classification tasks that train models on a broad dataset, this study trained three CNN architectures--AlexNet, VGG-19, and ResNet-50--separately for each binary classification task. Each model was optimized for distinct features, intersection type, road type, intersection angle, and the presence of crosswalks, Michigan left turns, or nearby residential areas. This targeted approach improved classification accuracy by tailoring model hyperparameters to each feature's characteristics.  
To ensure robust model evaluation, a 5-fold cross-validation technique was utilized. This method reduced the risk of overfitting and provided a more reliable assessment of model performance by validating results across different data partitions. Including this evaluation approach strengthens the reproducibility of the findings and ensures that the reported accuracy and F1-scores are not biased by specific data subsets.  
Overfitting is a common challenge in deep learning models, mainly when training with datasets that may have limited diversity. Training for 50 epochs on a potentially homogeneous dataset could result in models that perform well on training data but struggle with generalization. In addition to 5-fold cross-validation, accuracy trends were closely monitored across training and validation sets to mitigate this. Adjustments were made if substantial discrepancies were observed.  
Furthermore, computational efficiency optimization was a key consideration in selecting the most effective model for real-world applications. While deeper architectures like ResNet-50 are generally expected to achieve high accuracy, our findings demonstrate that AlexNet outperformed both VGG-19 and ResNet-50 in classification accuracy while requiring less computational power. This discussion further emphasizes AlexNet's advantages in achieving high classification accuracy and computational efficiency, making it the most effective model for pedestrian crash diagram classification. This optimization is crucial for transportation agencies and researchers working with limited computational resources, where efficiency is crucial for practical implementation.
## 6 Experimental Results and Discussions
Signal shown in figure is captured when input to the digital receiver is \(1\)-bit and the clock frequency is \(2\) Msps. The power consumption of the receiver is less when the receiver processes such low resolution (time and amplitude) signal. Figure 18(a) and 18(b) shows the power break-up of the synchronization and data-path sections. The power is averaged over the maximum packet length. The power break up shown is obtained for \(Q_{dig}\) equal to 8-bit. As can be seen that power consumption by the synchronization unit is much smaller than the units in the data path as they are \"**ON**\" for much shorter duration. Among the synchronization units, the fine time tracking unit consumes the most power as it contains many correlators for estimating the fine timing. In data path FIR filters consume the largest power due to many multiply and accumulate units in it. Figure 19(a) shows the measured PER _vs._ SNR\({}_{FE}\) for the receiver working at \(8\)-bit and \(30\) Msps. From the figure it is seen that the SNR\({}_{FE}\) required to meet 1% packet error is around \(-3.25\) dB. Whereas, from Table 1 it is seen that the minimum SNR\({}_{FE}\) required is around \(-6\) dB. As discussed earlier, non-idealities of the RF front end and the experimental setup might be the reason for this difference. The Figure 19(b) shows the PER _vs._ SNR\({}_{FE}\) when receiver works on its lowest configuration, \(1\)-bit and \(2\) Msps. It is seen from this figure that the lowest SNR\({}_{FE}\) meeting the error criteria is around \(6\) dB. Table 1 suggests that it requires around 5 dB of SNR\({}_{FE}\) for \(1\)-bit \(2\) Msps setting to meet the error specification. The difference can be attributed to the factors discussed above. The packet loss is nearly same in both Figure 19(a) and 19(b). This is because the synchronization section in both cases runs at same settings of \(Q_{dig}\) and \(f_{s}\). Though the experimental SNR\({}_{FE}\) values differ from the values obtained through simulation, the difference is not very significant from the point of verifying the idea of the power scalable receiver. The experimental results verify the claim that for different signal conditions different setting (\(Q_{dig}\), \(f_{s}\)) of the receiver can be used to minimize power while meeting the error criteria. The design of the receiver proves to be working well to receive the packets with different \(Q_{dig}\) and \(f_{s}\) settings. Figure 18: Power consumption of synchronization and data-path units, averaged over maximum length packet, for \(Q_{dig}\) = 8 bit.
# Study on Multi-Scale Coral Reef Pore Structure Identification and Characterization
## 4 Multi-Scale Pore Structure Characterization Method
### Fractal Characterization  
Fractal geometry takes a large number of non-smooth and irregular geometric shapes in nature and nonlinear systems as the research object, and aims to quantitatively describe complex shapes that are not suitable to be described by classical Euclidean geometry. Coral reef pores have a certain degree of self-similarity and fine structure, and have fractal characteristics. The shape of the pore section boundary can describe the regular characteristics of the pore. The pore perimeter of a certain area can reflect the pore geometry. The longer the peripheral curve length of the pore section, the greater the tortuosity of the pore boundary, the more irregular the pore geometry and the more complex the structure. Pore size distribution can characterize the homogeneous characteristics of the pore structure. The larger the range of pore size distribution, the more uneven the pore structure and the more obvious the heterogeneity of pores. In the calculation of a single scale fractal dimension, the island method is used to measure and calculate the fractal dimension of pore interface boundary [15]. The island method is a method to calculate the fractal dimension according to the measurement relationship. The existing relationship is:  
\[\text{lg}L(\varepsilon)=D_{F}\text{lg}\alpha_{D}(\varepsilon)+\frac{D_{F}}{2} \text{lg}A(\varepsilon)=C+\frac{D_{F}}{2}\text{lg}A(\varepsilon) \tag{11}\]  
where, \(C\) is the constant, \(L\) is the pore perimeter, \(A\) is the pore area, \(D_{F}\) is the fractal dimension, \(\varepsilon=\frac{\eta}{L_{0}}\), where n is the absolute measurement scale and \(L_{0}\) is the perimeter of the initial figure. In the case of fixed scale n, \(\alpha_{D}(\varepsilon)\) is a constant, and \(\alpha_{D}(\varepsilon)\) is only related to the selected scale, independent of the size of the graph. In the image, the perimeter and area of each pore are measured, respectively. The double logarithm plot of area and perimeter is twice the slope, which is the fractal dimension \(D_{F}\) value. According to the fractal description, the relationship between pore size distribution fractal and fractal dimension is as follows:  
\[\text{lg}N(r)=\text{lg}a-D_{F}\text{lg}r \tag{12}\]where \(r\) is the equivalent radius of pores, \(N(r)\) is the number of pores with pore size greater than \(r\), and a is a constant.
## 4 Applications in Optimization
### Differential Evolution (DE)  
Meta-heuristic optimization methods have seen incredible advancements over the two last decades. More efficient computational algorithms have enabled solving of complex problems. One of these modern meta-heuristics is Differential Evolution (DE), an evolutionary computation method which has become widely used in engineering optimization due to its simple structure, ease of use, fast convergence speed, versatility, and robustness.  
The main difference between genetic algorithms and DE is that in genetic algorithms mutation is the result of small perturbations to the genes of an individual (potential solution) while in DE mutation is the result of arithmetical combinations of individuals.