esa-sceva/satcom-chunk-collection huggingface dataset:
# Aggregatable Subvector Commitment with Efficient Updates
## 1 Introduction
### Our Contributions  
In this paper, we propose a new aggregatable subvector commitment vector based on Newton interpolation, which can efficiently update the commitment and proofs when inserting a new value into the vector. Table 1 provides a brief comparison with previous research work. Our primary contributions can be outlined as follows:  
* This paper proposes a novel aggregatable subvector commitment scheme based on Newton interpolation. When appending a value to the vector, it eliminates the need to fully recompute the original commitment value and proofs. Instead, the client incrementally updates the commitment and proofs for each position, significantly reducing computational overhead.
* The Karatsuba algorithm can efficiently perform large-integer multiplication, which can reduce the computational overhead from \(O(n^{2})\) to \(O(n^{\log_{2}3})\) for the multiplication of two n-digit numbers. By leveraging this algorithm, our scheme improves the efficiency of both aggregation and verification operations.
* We present a formal security analysis to prove that our scheme can achieve the security of position binding. Furthermore, we provide a thorough implementation, and the experiment results demonstrate that our scheme is more efficient at aggregation and verification, and at updating the proofs when appending a new value. Specifically, compared with aSVC, the proposed scheme achieves a 48\(\times\) speedup when adding an element to a 16-length vector, as well as 2.13\(\times\) and 1.73\(\times\) speedups for aggregating eight proofs and performing verification, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scheme** & \(|C|\) & \(|\mathbf{\pi_{t}}|\) & **Aggregate** & **Verify**\(|\mathbf{\pi_{t}}|\) & **Verify**\(|\mathbf{\pi_{t}}|\) & **UpdateAllProofs** & **AddAllProofs** \\ \hline aSVC & \(1|G_{1}|\) & \(1|G_{1}|\) & \(O(k\log^{2}k)\) & \(1\) & \(O(k\log^{2}k)\) & \(O(n)\) & \(O((n+l)\log(n+l))\) \\ \hline Our Scheme & \(1|G_{1}|\) & \(1|G_{1}|\) & \(O(1.39(\frac{k}{2}+1)^{\log_{2}3})\) & \(1\) & \(O(1.39(\frac{k}{2}+1)^{\log_{2}3})\) & \(O(n^{2})\) & \(O(n+l^{2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with previous work using aSVC.
## 2 Materials and Methods
### Development of pHp Programs  
In the present study, the pHp (recursive acronym for hypertext pre-processor) [80] is used to retrieve the data from MariaDB and display the results and graph plotting. The pHp is a widely used open source scripting language for web development and can be embedded into HTML [81]. Compared to Microsoft Excel, pHp is more dynamic and interactive. This study developed three pHp programs. The first pHp program generally presents the comparison of daily satellite estimations and rain gauge observations at the point-to-pixel scale. Figure 5 shows the interface of the program. To run the program, the user is required to select the grid/rain gauge point, key in, or select the starting and end dates for the program to query the result from the database. Once the criteria are selected, the program will display the result responsively based on the user's selected criteria. The comparison of the two datasets will be visualized in the form of a scatter plot and time series graph. The data points in both graphs represent the average rainfall in a grid. The statistical performance will also display at the bottom of the map.  
Simple and straightforward step simulations are important in data modeling [82]. Maps generated for spatio-temporal analysis provide a clearer picture of the regions that required attention under current climate change [83]. The second pHp program to be developed allows the user to visualize the satellite estimations and rain gauge observations in the form of maps, as shown in Figure 6. In this program, the user will need to select the study period, then the rainfall will be plotted according to the satellite grid size. The legend will be dynamically generated. It makes the researcher easier to visualize the distribution of rainfall data from both rain gauge the observation and satellite estimations of the same period.  
The last pHp program was developed to visualize the distribution of every statistic that was used to evaluate the performance of SPEs before and after LSTM deep learning improvement. This pHp program will be visualizing the performance metrics in the IMERG grids in maps by connecting to MariaDB where the results are being stored. The results from this program are presented in Section 3.  
Figure 5: First pHp program that compares the daily satellite estimations and rain gauge observations at the point-to-pixel scale.  
Figure 6: Second pHp program that shows the daily rainfall distribution of satellite estimations and rain gauge observations.
# Inner-Loop Controllers for Grid-Forming Converters
## 2 Materials and Methods
### Proposed Inner-Loop Voltage Controller with Current Limiter  
The proposed inner-loop voltage controller with a current limiter is designed to achieve four primary objectives:  
* Minimize the output voltage tracking error;
* Limit the inverter current during transients and short circuits;
* Implement the desired inverter output impedance characteristics;
* Attenuate the resonance frequency of the inverter's LC filter.  
The circuit of a single-phase inverter is presented in Figure 6.  
In a digital control system, electrical variables are sampled using analog-to-digital converters, and the control calculations are executed by a microcontroller. The control signals are then compared with a triangular carrier to generate the PWM signals, introducing a  
Figure 6: Circuit of a single-phase inverter with primary and secondary controls.  
one-sample time delay into the system [6]. Considering the single-phase circuit on Figure 6, a discrete-time state-space model with a sampling time of \(T_{s}\), including the one-sample delay resulting from the digital implementation, is given by (1), and is rewritten in the compact form in (2). The switching effects of the PWM and its nonlinearity are neglected, so the control action \(u_{k}\) is assumed to represent the voltage synthesized by the converter.  
\[\left[\begin{array}{c}i_{Lk+1}\\ v_{ck+1}\\ u_{dk+1}\end{array}\right]=\left[\begin{array}{ccc}1&-\frac{T_{s}}{L}&\frac {T_{s}}{L}\\ \frac{T_{s}}{C}&1&0\\ 0&0&0\end{array}\right]\left[\begin{array}{c}i_{Lk}\\ v_{ck}\\ u_{dk}\end{array}\right]+\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]u_{k}+\left[\begin{array}{c}0\\ -\frac{T_{s}}{C}\\ 0\end{array}\right]i_{ok} \tag{1}\]  
\[\mathbf{x}_{k+1}=\mathbf{G}_{i}\mathbf{x}_{k}+\mathbf{h}_{i}u_{k}+\mathbf{f}_ {i}i_{ok} \tag{2}\]
## 3 Proposed Method  
Our proposed method is utilized during the course of our experiments for both age and gender classification. Our approach is inspired by the research advancement in the computer vision fields, such as image classification [48; 69; 70], object detection [71], age estimation [23; 24], and gender classification [23] fueled by deep learning. At the very beginning of our age and gender estimation process, we ensure a class-wise balance of the image samples for training. To do this task, we down-sample those classes up to a specified threshold where the number of images is huge, then up-sample other classes by importing images from another dataset built on the same setup and perform data augmentation where necessary. Additionally, we manually filter out those images that seem wrongly annotated using human visual perception. In the later stages, before feature extraction, we detect the face from raw faces and prepare the images for network input performing some preprocessing tasks like rescaling and resizing. We use the same CNN structure for the feature extraction and the Softmax layer for classification output regardless of the estimation task, but a further formulation is performed in case of age estimation. We calculate the expected value over the Softmax probabilities for age regression. Each step of the proposed approach shown in Figure 1 is depicted thoroughly in this section.  
In Figure 2, we present the process diagram of the smart store customer relationship management system based on our approach. When a customer approaches the smart shelf, the camera attached with the shelf automatically captures the image of the customer and the installed age and gender estimation model will predict the demographics of the respective customer. During the interaction of the customer and automated system, the customer will get the personalized product recommendation, advertisement, and offer. In a smart store enterprise solution, several outlets are operating simultaneously under the same setup. Therefore, the customer data will be stored in a local server that exists on the outlet and to the central server from the different outlets through a communication network.
## 1 Introduction
### Previous Research and Limitations
To sidestep the disadvantages of the above methods of CE, we propose a variational channel estimation with tempering, and solve the problem of channel estimation in a MIMO scenario, and test it using simulations. Variational tempering (VT) is derived from Markov Chain Monte Carlo (MCMC) methods. By sampling from different temperatures, a MCMC method can increase the mixing time of the Markov chain [31]. In a similar way, we introduce global temperature. On the contrary, VT algorithms can learn a temperature distribution from the data, and update the weight in every iteration, which is similar to treating the transmitted signal of each sensor user as a distribution as well as its channel coefficiency. Additionally, we apply our CEVTI in coded CDMA case and uplink massive MIMO case and analyze its complexity and optimality guarantee. We find that CEVTI has better accuracy compared with state-of-the-art CE algorithms, satisfying computational cost and guarantees local optimality.
# An Integrated Capacity Allocation and Dynamic Pricing Model Designed for Air Cargo Transportation
## 2 Materials, Methods, and Models
### Demand Forecasting
The statistical outcomes of the multiple regression, time series, and artificial neural network (ANN) models applied to the data set are presented in Table 6. Following a thorough evaluation of the results, we determined that the multiple linear regression method would be the most suitable approach for our study. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & **Multiple Linear** & **Time Series** & **Adjusted Time** & \multirow{2}{*}{**ANN Model**} \\  & **Regression** & **Winter’s Model** & **Series Model** & \\ \hline R\({}^{2}\) & 88\% & 15\% & 62\% & 91\% \\ ME & 116.880 & 103.999 & 175.236 & 262.918 \\ MAD & 183.832 & 411.765 & 298.053 & 289.005 \\ RMSE & 229.814 & 482.661 & 341.486 & 338.400 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical comparison of demand forecasting models. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Network Type**} & **Number of Layers** & **Number of Neurons** & **R\({}^{2}\)** \\ \hline NARX & 1 & 11 & 86\% \\ NARX & 2 & 8 & 67\% \\ NARX & 2 & 10 & 91\% \\ NARX & 2 & 11 & 91\% \\ NARX & 2 & 12 & 85\% \\ NARX & 3 & 11 & 87\% \\ NARX & 4 & 11 & 89\% \\ NARX & 5 & 11 & 88\% \\ Feed-Forward Back & 2 & 8 & 86\% \\ Propagation Network & 2 & 10 & 87\% \\ Feed-Forward Back & & & \\ Propagation Network & 2 & 10 & 87\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results produced by different ANN models for demand forecasting.
# Defense and Security Mechanisms in the Internet of Things: A Review
## 3 Materials and Methods
### Encryption and Cryptographic Mechanisms
Integrity ensures that data are accurate, complete, and not altered in an unauthorised manner. Availability ensures that data are readily available to the authorised users [70]. Also, the security protocols may achieve the following security features:  
* Mutual authentication--both parties to a communication, such as a client and a server, mutually confirm their identity to prevent fraud. * Message or data integrity--verifying that data or communications remain unaltered or untampered during transmission. * Anonymity--protecting the identities of communication participants so that their actions or data are not linked to their real identities. * Untraceability--makes it impossible to link actions or messages to a specific user, even after interception. * Authenticity--confirming that the sender of a message or data is authentic and comes from a trusted source. * Perfect forward secrecy--mechanism that ensures that even if a session key is compromised, previous sessions cannot be decrypted. * Session key establishment--the process of securely establishing a unique encryption key for each communication session between two parties. There are various ways through which we can ensure that security protocols are effectively tested and are in line with the security goals that have been put forward for them by using both formal and informal methods and automated tools. For the formal method, we can employ the Random Oracle Model (ROM) [71], BAN (Burrows, Abadi, Needham) logic [72], or the Real-or-Random (ROR) model [73]. As for the automated tools, we can employ Scyther, AVISPA, or ProVerif tools [74].
## 3 Results
### Binomial Logistic Regression Analysis  
To assess the efficacy of voice treatment based on VHI scores, a binomial logistic regression analysis was carried out. The analysis considered independent variables such as gender, smoking habits, alcohol use, voice usage status, coffee consumption, the presence of comorbidities, surgical history, and voice training status. Gender was coded as 0 for males and 1 for females; smoking status, alcohol consumption, voice user status, and other variables were coded similarly (0 for no, 1 for yes). The dependent variable, representing VHI scores after speech therapy, was coded as 1 if improvement occurred and 0 if not.  
Table 4 summarizes the findings from the binomial logistic regression analysis. The analysis shows that gender has a statistically significant effect on VHI score improvement (\(p=0.012\)), with a corresponding odds ratio (exp (\(B\))) of 25.033, indicating that differences in gender are associated with a 25-fold increase in the odds of an improved VHI score. Other variables, such as smoking status, alcohol consumption, and voice user status, do not have significant impacts. The model's Nagelkerke R2 is 0.422, suggesting moderate explanatory power, and the Hosmer-Lemeshow test indicates good model fit (\(p=0.967\)). The classification accuracy of the model is 91.2%, reflecting strong predictive performance.  
Figure 5: (**a**) Duration of voice treatment and (**b**) number of voice training sessions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(p\)**-Value** & \(p\)**-Value** \\ \hline gender & \textless{}0.001 ** & coffee consumption & 0.125 \\ smoking status & 0.372 & comorbidity & 0.894 \\ alcohol consumption & 0.104 & surgery status & 0.038 * \\ voice user status & 0.061 & voice training status & 0.021 * \\ \hline \hline \end{tabular}  
* and ** indicate statistical significance at the 0.05 and 0.01 levels, respectively.  
\end{table}
Table 3: \(p\)-values calculated for various factors.
# Seismic Study of An Isolated Cable-Stayed Bridge under Near-Fault Ground Motions
## 2 Longitudinal Seismic-Reduction System  
### Friction Pendulum Bearing  
The friction pendulum seismic isolation system is installed between the superstructure and substructure of the bridge. It reduces the transverse forces and horizontal vibrations transmitted to the structure, protecting the bridge from earthquake damage [21]. Its components are shown in Figure 1a. During a strong earthquake, the friction pendulum base isolator induces a pendulum motion in the superstructure via the movement of its spherical sliding surface. The restoring force provided by the pendulum device allows the base isolator to return to its central position, relying on the force of gravity. Since the stiffness center of the friction pendulum seismic isolation system automatically aligns with the center of mass of the isolated structure, it effectively eliminates the twisting motion of the structure within the broadest range possible. This reduces the transfer of seismic energy from the substructure to the superstructure.  
When the shear force on the base isolator due to an earthquake is less than the frictional force, the friction pendulum seismic isolation system remains in a non-sliding state. The isolated structure supported by it behaves similarly to a conventional structure. However, when the seismic shear force exceeds the frictional force, the friction pendulum seismic isolation system undergoes displacement. The base isolator's liner exhibits ideal friction-velocity characteristics, accommodating both temperature-induced deformations and seismic motion in bridges. A friction coefficient of less than or equal to 4% is sufficient to meet the temperature deformation requirements of bridge structures. A higher dynamic friction coefficient is required to resist seismic motion in bridges. Figure 1b illustrates the hysteresis model of the friction pendulum base isolator. To ensure the accuracy of the numerical simulation, a comparison was made between the numerical simulation and the experimental results [23], showing good agreement.  
In the Figure 1, \(\mu\) represents the dynamic friction coefficient; \(R\) is the radius of the sliding surface; \(dy\) is the yield displacement; \(K\) is the post-sliding yield stiffness of the friction pendulum base isolator; \(N\) is the axial compressive force.
# Method for Improving the Reliability of SRAM-Based PUF Using Convolution Operation
## 3 The Proposed Scheme  
### Common Convolution Filtering  
Common convolution uses filtering to reduce noise in digital-image processing, such as by using mean filtering and median filtering. For example, it can filter the large matrix to a small matrix by one or two layers of convolution through the equal-stride convolution kernel. The step stride of the convolution is the same as the length of the convolution kernel. The mean filtering and median filtering approaches were tested for our SRAM data to determine the feasibility of common convolution. However, the output results were unstable. The common convolution instability rate was higher than that for the original SRAM.  
The outputs of digital-image processing are 8-bit pixels. In contrast, SRAM PUF binarizes the value of the output. A value greater than 0.5 is regarded as 1, and a value less than or equal to 0.5 is regarded as 0. Convolution does not remove noise--it only reduces the weight of the noise in pictures. The mean value of an area tends to be 0.5 for a matrix which has equal probability random distributions of 0 and 1. Unstable cells cause the mean value of an area to fluctuate around 0.5. If the noise changes the results of binarization, the convolution output becomes unstable.  
Figure 1: Difference between mean filtering and median filtering.  
Convolution filtering avoids the issue of the need for additional hardware resources associated with traditional SRAM PUF. All the computing work can be undertaken by the server side of the verifier. When receiving the verification instruction, the MCU system only needs to provide the SRAM power-on initial value for a certain area. When the authentication is completed, this area can still undertake other functions of MCU, as with normal SRAMs. Convolution filtering represents a novel way to generate SRAM PUF. This paper presents a new scheme to improve the reliability of convolution.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 3 ETC Data-Based Traffic Sensing and Feature Extraction
### ETC Data Structure and Characteristic Analysis  
#### 3.1.1 Data Types and Structures  
Data generated by ETC systems are characterized by high structuralization and rich information content. These data not only record vehicle passage information but also contain deep-level traffic operation patterns and user travel characteristics.  
The deep value mining of user card data has become an important direction for ETC data applications. User card data contains multi-dimensional information, including basic cardholder information (name, identification number, contact information), vehicle information (license plate number, vehicle type, color), and account information (balance and credit limit). These data provide the foundation for vehicle identity recognition and user behavior analysis while creating conditions for personalized traffic services and precise traffic management. From the perspective of data structure evolution, user data in modern ETC systems has evolved from simple identity identification to a foundation for user profiles containing diverse information such as travel preferences, consumption habits, and credit status.  
The spatiotemporal characteristic analysis of transaction record data reflects the core value of ETC data in traffic sensing. Transaction records detail information for each passage, including entry time, exit time, entry number, exit number, travel path, toll amount, and other key elements. [PERSON] et al. developed systematic information extraction technologies based on ETC electronic toll data, successfully achieving road segment travel speed calculation and traffic flow distribution analysis [16]. The significance of this research lies in validating the reliability and effectiveness of ETC transaction data in traffic parameter estimation.  
Vehicle trajectory data reconstruction and application represent advanced forms of ETC data applications. By linking identification records from multiple ETC gantries, vehicle travel trajectories can be accurately reconstructed, providing valuable data resources for traffic flow analysis, route choice research, and travel behavior analysis. This not only demonstrates the technical feasibility of ETC data in large-scale traffic monitoring but more importantly showcases the enormous potential of trajectory data in discovering urban traffic patterns.  
From the perspective of data structure development trends, ETC data is undergoing transformation from single-dimensional to multi-dimensional, from static recording to dynamic sensing, and from passive collection to active interaction. These transformations not only enrich the content and value of data but also provide a greater scope for the innovative development of traffic sensing technologies.