esa-sceva/satcom-chunk-collection huggingface dataset:
# ISAR Image Quality Assessment Based on Visual Attention Model
## 4 Discussion of the Method  
### Ablation Studies  
The ablation studies presented in Table 2 demonstrate the progressive improvement in performance as different components are added. The Gram-T model shows a significant improvement over traditional methods, with both SRCC and PLCC exceeding 0.8 and obtaining a total score of 1.730. The addition of the CAB module further enhances performance, increasing the total score to 1.744. The inclusion of the IRAB module leads to further improvements in both SRCC and PLCC, with the combined CAB and IRAB modules achieving the highest performance, with a total score of 1.773. These results highlight the effectiveness of the model structure and the attention mechanisms in improving the model's ability to capture discriminative features and enhance image quality assessment.  
Additionally, in Table 3, experiments on the selection of scale factors are conducted. It is obvious that the model performs best when the scale factor is 0.8.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Methods** & **SRCC** & **PLCC** & **Score** & **Inference Time(s)** \\ \hline MQEM [7] & 0.6411 & 0.5652 & 1.206 & 0.30 \\ BRISQUE [19] & 0.6501 & 0.5844 & 1.235 & 0.20 \\ NIQE [20] & 0.5312 & 0.5382 & 1.069 & 0.10 \\ VGG16 [21] & 0.4564 & 0.4148 & 0.871 & 0.15 \\ Resnet18 & 0.7102 & 0.6507 & 1.361 & 0.24 \\ Resnet34 & 0.8697 & 0.8296 & 1.699 & 0.36 \\ Resnet50 [22] & 0.8712 & 0.8499 & 1.721 & 0.47 \\ ViT [12] & 0.8731 & 0.8467 & 1.720 & 0.28 \\ CNN-based Regression [9] & 0.5531 & 0.5437 & 1.097 & 0.43 \\ KNN-based Regression [9] & 0.4626 & 0.4782 & 0.941 & 0.22 \\ VPSL [17] & 0.6916 & 0.6372 & 1.329 & 0.34 \\ EBFF [18] & 0.7941 & 0.7219 & 1.516 & 0.65 \\ Ours & 0.9103 & 0.8627 & 1.773 & 0.31 \\ \hline \end{tabular}
\end{table}
Table 1: Effects of different models on ISAR image quality assessment. Our models are compared with popular networks, including Resnet and Vit. Our models perform better than the other ones we compared them to on the ISRA image dataset.
## 3 Implementation and Experiments
### Data  
We use the following datasets for our experimental evaluations. The first three datasets are taken from the UCI machine learning repository [22].  
**Adult** ([30]) This was extracted from a dataset of the US Census Bureau Data Extraction System. It consists of 32,561 records over the 15 attributes: age, work class, final weight, education, education number, marital status, occupation, relationship, race, sex, capital gain, capital loss, hours per week, native country, and salary class. Since the final weight entry is unique for roughly one half of the records, we removed it from the dataset.  
Following [PERSON] _et al._[23], we also prepared this dataset with the nine attributes, age, work class, education, marital status, occupation, race, sex, native country, and salary class. This second variant is called Adult-2 in the following.  
**Nursery** ([31]) The Nursery dataset was derived from a hierarchical decision model originally developed to rank applications for nursery schools; see [PERSON] _et al._[32] for a detailed description. It contains 12,960 records over the eight attributes: parents, has nurse, form, children, housing, finance, social, and health. All entries are encoded as positive integers.  
**CMC** ([33]) This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. It contains 1,473 records over 10 attributes. The attributes are wife's age, wife's education, husbands education, number of children, wife's religion, wife working, husband occupation, standard of living, exposure, and contraceptive.  
**Canada** ([34]) This dataset is taken from the Canada Vigilance Adverse Reaction Online Database and contains information about suspected adverse reactions (also known as side effects) to health products. The original dataset was collected in November 2012 and contains 324,489 records over 43 attributes. (See [35] for the list of the attribute names.) Since some values are contained in multiple attributes (as numerical code, in English and in French), other attributes contain unique values for each record and some attributes are empty in most records, we removed all these attributes from the dataset. We ended up with 324,489 records over the nine attributes: type of report, gender, age, report outcome, weight, height, serious adverse reaction, reporter type, and report source.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 3 Exhaust Manifold Analysis
### Results and Discussion
#### 3.6.4 Turbulent Kinetic Energy Comparison  
The diagram shown in Figure 11 represents the evolution of turbulent kinetic energy (TKE) along the length of the exhaust manifold for various turbulence models, progressing from the inlet to the outlet.  
Figure 10: Pressure drops from the inlet of the manifold.  
Figure 9: Velocity distribution across the exhaust manifold (70 s).  
Among the simulations, it is noteworthy that both the turbulence models of transition SST and k-\(\omega\) exhibit distinctive behavior, showcasing the maximum TKE in the range [8; 9; 10; 11; 12] m\({}^{2}\).s\({}^{-2}\), suggesting its contribution to heightened turbulence levels, particularly in the latter part of the manifold. In contrast, the k-kl-\(\omega\), Reynolds stress, and RNG k-epsilon models display lower TKE values, generally ranging between 0 and 4 m\({}^{2}\)/s\({}^{2}\), suggesting a more subdued turbulence prediction. The higher TKE values observed in the k-omega and transition SST models can be attributed to their advanced turbulence modeling approaches, which are designed to capture the complexities of transitional and turbulent flows more accurately. These models include additional mechanisms to account for turbulent energy transfer and dissipation, leading to higher predicted TKE levels. One possible explanation for the notably more significant turbulent kinetic energy (TKE) that is predicted by the transition SST is that it employs a hybrid approach that combines elements of the k-\(\omega\) and k-\(\varepsilon\) turbulence models. This combination allows for a more comprehensive representation of turbulence phenomena, particularly in transitional flow regimes, which may enhance TKE predictions.
## 2 System Model  
### Network Model and Operation  
We consider a CRAN system as illustrated in Figure 1, where all the RRHs are connected to an edge cloud consisting of a baseband unit (BBU) pool via cloud links with high-bandwidth and low latency communication. In the setup, \(N+1\) RRHs, i.e., RRH\({}_{0}\), RRH\({}_{1}\),..., RRH\({}_{N}\) are spatially distributed in a given area\(\mathcal{A}\subseteq\mathbb{R}^{2}\). We assume that all RRHs are using the same spectrum resources (i.e., time and frequency) when transmitting to their corresponding UEs. We consider the link between RRH\({}_{0}\) and UE as a _typical link_. Meanwhile, we assume that link from RRH\({}_{j}\) to UE for all \(j\!\in\!1,\ldots,N\) as (potentially) interfering links to the typical link. We use the notation \(d_{0}\) to denote the distance between the RRH\({}_{0}\) and the UE, while \(d\) denotes the distance from all other RRH\({}_{j}\) to the UE. The UE of interest is served by the closest RRH, while other RRHs are uniformly distributed on the perimeter of a circle (with radius \(d>d_{0}\)) centered on the UE (Notice that in real-world setups, at any instant of time, the UE could be at any random location. For analytical tractability, we have eased this constraint by assuming equal distances to interfering nodes such that all the interference from other interfering RRHs are assumed from the same nodes). We focus on the typical link performance while all the other RRH\({}_{j}\), \(j\!=\!1,\ldots,N\) are using the same channel to transmit data to their corresponding UEs. We carry the analysis for the typical link performance when the remaining RRHs are:not cooperating with the typical link through CRAN, i.e., no cooperation (full interference scenario as shown in Figure 1);
* cooperating with the typical link through the CRAN to serve the UE (BBU at CRAN enables coordinated multi-point transmission and cooperative solutions, similar to scenarios described in [12; 14; 25; 29]). Under the cooperation, we proposed silencing, TAS with silencing and the MRT scheme as shown in Figure 1 which is explained in detail in the following Section 2.2.
## 2 Materials and Methods
### Datasets
#### 2.2.2 Weather Data  
Daily gridded rainfall data with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and maximum and minimum temperature datasets with a resolution of 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) were obtained from the Indian Meteorological Department (IMD) website. The average yearly rainfall for the Nagavali and Vamsadhara watersheds for the period of 1901-2018 is 1230 and 1260 mm, respectively.
## 2 Related Study
### Data Preprocessing  
Data often encounter issues in practical scenarios, such as incomplete or missing data, noise, and outliers. These issues can disrupt the proper functioning of models. Hence, data preprocessing is essential for adjusting and manipulating data before applying analytical algorithms and preventing inaccurate judgments owing to flaws in the data. In this context, missing value deletion and statistical analysis were utilized to identify and eliminate values exceeding three standard deviations. This resulted in cleaner data, which facilitated subsequent analyses.
## 4 Mian Features and Challenges of IoMT and Healthcare 4.0
### Specifications and Requirements of IoMT and Healthcare 4.0
High network flexibility: Network flexibility is a key component of Healthcare 4.0, which indicates the ability of healthcare providers to adapt their networks quickly and easily to changing patient needs and technological advancements. This is particularly important as Healthcare 4.0 involves the integration of various technologies, including robotics, AI, the IoT, and big data analytics. The ability to add these technologies quickly and seamlessly to the healthcare network is essential in order to improve patient care, productivity, and overall healthcare quality. Security and privacy: Most countries store and transmit personal health information (PHI) electronically. Due to the vast amount of health information gathered and shared via the Internet, the key concerns of healthcare operators are privacy and security. There is a risk of system-wide attacks on health data due to the open transmission channel. To defend against such attacks, researchers employ a variety of cryptographic strategies. Privacy includes the ability of only authorized users to access health information about specific patients, as well as the retrieval, use, and disclosure of patient information to an interloper. This can be achieved by implementing various schemes and their corresponding rules.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 5 Hardware Architecture of the FPGA-Based System: An Application Example  
### OFDM Technique Characteristics  
The designed architecture for the DFT computation is applied to the OFDM technique, which is used in several digital wireless applications, including digital video broadcasting, digital audio broadcasting, and wireless local-area networks. In fact, the OFDM technique mitigates delay-spread effects and produces high spectral efficiency. It is a multicarrier transmission in which a single data stream is transmitted over several lower-rate subcarriers, which are orthogonal to each other. Each subcarrier is modulated with a conventional digital-modulation scheme, such as quadrature amplitude modulation (QAM). DFT is used to modulate and demodulate the data constellations on the orthogonal subcarriers [26; 27; 28].  
The authors have simulated and implemented the circuit for the DFT evaluation in case of digital video broadcasting--second generation terrestrial (DVB-T2) for the \(1k\) mode using the 16-QAM constellation of the payload data. The finite word length of the input signal is composed of four bits: two for the in-phase component (I) and two for the in-quadrature component (Q). In Figure 5, the scheme for the 16-QAM modulation is illustrated. In particular, \(f_{BR}\) is the bit rate, \(f_{B}\) represents the baud rate, \(T\) is the symbol length, and \(T_{P}\) is the cyclic preamble length. \(T_{C}\) is the sampling step, \(N\) is the number of carriers (samples), and \(A_{i}\) is the \(i\)-th complex amplitude of the OFDM payload subcarrier. The IDFT block generates the symbol to be transmitted while the cyclic preamble enables symbol synchronization and allows dealing with multipath and performing channel equalization at the receiver end.  
Figure 4: Logical scheme of a generic BFT block having samples of a bit slice as serial inputs.
## 3 Datasets and Methods
### Random Forest  
An ensemble method where all the individual models are decision trees is referred to as a random forest. Random forest uses bagging or bootstrap aggregating where it takes multiple decision trees, each trained on a random subset of features. to obtain a final prediction which is a mean of the results from all the trees. This ebbs away the variance of a single decision tree thus reducing overfitting [28].  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Ensemble Technique** & **Implementation Steps** \\ \hline Random Forest, XGBoost & **1:** Split the dataset into training and testing sets; **2:** Initialize Random Forest classifier with 100 decision trees and XGBoost classifier with default parameterization of xgboost [27] library in python; **3:** Fit the classifiers on the training sets; **4:** Evaluate accuracy on the testing set using the trained classifiers. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ensemble techniques.  
Figure 5: BRISK keypoints on hand edges.  
XGBoost  
XGBoost or extreme gradient boosting is an ensemble algorithm that uses regression trees as the base learners. XGBoost's boosting nature gives more preference to misclassified instances during the next iteration of the algorithm and is hence robust on unbalanced datasets. The model uses gradient boosting, with each tree improving upon the errors of the previous tree in a sequential manner.
## 1 Introduction
On the basis of the above discussion, many scholars and manufacturing firms should be aware of the importance of energy-efficiency and job-priority concerns, on top of minimizing the makespan. There are a few studies on the parallel-machine-scheduling problem with a concept of energy consumption that consider job priority and makespan control. Therefore, this addressed scheduling is both an objective optimization problem and an NP-hard problem. The differential evolutionary (DE) algorithm is suitable to solve this kind of problem because it can obtain non-dominated solutions in a single run and has been successfully applied to optimization problems [17; 20; 24; 27; 28; 29]. In addition, metaheuristics based on local search methods, such as the variable neighborhood strategies adaptive search (VaNSAS), have been successfully applied to solve many combinatorial optimizations problems [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40], which inspired us to develop a parallel-machine-scheduling model, and to propose VaNSAS and new neighborhood strategies: (1) solution destroy and repair (SDR); (2) track-transition method (TTM); and (3) multiplier factor (MF). Nowadays, the cost of energy consumption is key in terms of production efficiency and environmental sustainability for industrial firms. A significant amount of research has been done on parallel-machine-scheduling problems that are strongly NP-hard. We refer to [PERSON] [41], [PERSON] [42] and [PERSON] [43] for the discussion on the complexity of parallel-machine scheduling. Therefore, this study investigates the parallel-machine-scheduling problem for minimizing energy consumption while considering job priority and makespan control. In order to obtain the near Pareto front, we developed VaNSAS: (1) solution destroy and repair (SDR); (2) the track-transition method (TTM); and (3) multiplier factor (MF). On the basis of the problem characteristic and evolutionary algorithm, the proposed VaNSAS uses a new encode scheme that can convert discrete optimization problems into continuous optimization problems, and applies VaNSAS with three different techniques to further improve quality. The remainder of the study is organized as follows: Section 2 reviews the literature related to parallel scheduling, considering energy consumption, job priority, and makespan control, including DE and VaNSAS applications; Section 3 describes a formal definition of the addressed problem and a mathematical model for the proposed problem; Section 4 describes the proposed VaNSAS, SDR, TTM, and MF with multiple operators in order to handle the scheduling problem; Section 5 presents and analyzes the experiment results of our proposed VaNSAS, SDR, TTM, and MF; Section 6 provides the conclusion and suggestions for future studies.
# Minimizing Interference-to-Signal Ratios in Multi-Cell Telecommunication Networks
## 4 Experiments
### Analysis of the Running Times  
If the order of magnitude of \(M\) is reasonable, i.e., \(b-M=\Omega(b)\), then the running times of neither DP-Similarity Clustering nor Spectral Clustering depend too much on the exact value of \(M\); in fact, the difference in running time between \(M=20\) clusters and \(M=40\) clusters with \(b=200\) BSs and \(20\leq u\leq 500\) users is about \(5\%\). Thus, the running times on Figures 2d and 3d are measured for reasonable values of \(M\).  
Recall Lemma 2. Theoretically, the running time of DP-Similarity Clustering is dominated by the matrix multiplication \(W\cdot W^{T}\) (computing the similarity measure), but this operation takes less than 0.5 ms even for a \(200\times 500\) matrix (thanks to the accelerated vector operations in the x86-64 instruction set). In this regime of \(b\leq 200\), most of the running time of DPH-clustering (Algorithm 1) is spent after the matrix multiplication, which is log-quadratic in the number of base stations, but the constant factor of the main term is probably quite large. Thus, to improve the efficiency of the DP-Similarity Clustering algorithm, further development should be focused on DPH-clustering.  
The running time of Spectral Clustering is a combination of computing the eigenvectors of a matrix of order \(b+u\) and subsequently clustering the normalized eigenvectors via the \(k\)-means algorithm. Considering the large base matrix and the complex operations performed on it, it is not surprising that Spectral Clustering is an order of magnitude slower than DP-Similarity Clustering.  
Let us refer back to our considerations after Problem 1. In real-world applications, users are constantly entering and leaving the network, and some of them may move out of the range of their cells. For this reason, the clustering needs to be frequently updated, but the time complexity may be prohibitive if the number of BSs is very large. However, if the overall changes to \(W\) are not large, it is possible to reuse \(\mathcal{I}_{|I|-M}\), and thus DPH-clusteringneed not be called upon every time \(G\) or \(W\) is updated. Practically, DPH-clustering is called upon only if the tinf value of the solution increases beyond a preset threshold. Since users are joined to the respective best cluster by Algorithm 2, if the users know the current \(\mathcal{I}_{||-M}\), they can decide for themselves individually (locally) when to leave and join another cell.
# Bus Travel Time Prediction Based on the Similarity in Drivers' Driving Styles
## 3 Methodology  
### Prediction Framework  
We provide an approach to predicting bus travel times based on similarities in drivers' driving styles. It consists of two main parts: driving style classification and travel time prediction. Figure 2 shows an overview of our proposed approach.  
The first part is the hierarchical clustering driver driving style classification model, which is used to classify the driver's driving style according to the bus GPS data. The second part is the bus travel time prediction model. First, the corresponding training dataset was selected according to the classification results of the first part. Then, the corresponding travel time prediction was made for buses driven by drivers with different driving styles. These two parts are described in detail in the following two sections: Sections 3.2 and 3.3.
## 3 Algorithmic Approaches used in the Methodology
### Particle Swarm Optimization (PSO)  
The PSO was first proposed by [PERSON] and [PERSON] in 1995 [48]. It is a population-based optimization algorithm, which became very famous because of its continuous optimization process towards the best solution.  
It is derived from the concepts of swarming habits of animals, e.g., fish or birds and also from genetic algorithms. At a given time \(t\), PSO upholds multiple possible solutions, each represented by a particle. The fitness of these solutions is calculated during each iteration by using an optimization function. There is a certain velocity which each particle has to move with, in order to reach the maximum value, returned by the objective function. At each iteration, the particle's position and velocity are updated according to the following Equations (1) and (2):  
\[\mathrm{V}\left(t+1\right)=\mathrm{W}*\mathrm{V}\left(t\right)+\mathrm{c1}* \mathrm{r1}\mathrm{x}\left[\mathrm{Y}(t)-\mathrm{X}(t)\right]+\mathrm{c2}* \mathrm{r2}\times\left[\mathrm{G}(t)-\mathrm{X}(t)\right] \tag{1}\]  
\[\mathrm{X}(t+1)=\mathrm{X}(t)+\mathrm{V}\left(t+1\right) \tag{2}\]  
Figure 4: Basic deep neural network architecture.  
where V is the particle's velocity, X(\(t\)) is particle's current position at time \(t\). Y(\(t\)) is the individually best solution of the particle at time \(t\), and G(\(t\)) is the global best solution of the swarm at time \(t\). W is the coefficient of inertia, usually ranges between 0.8 to 1.2. r1 and r2 are the random numbers generated in the range [0,1], and c1 and c2 are the cognitive and social coefficients, respectively. These coefficients are also known as learning factors, and their value is usually kept as 2.