esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms
## 2 Fractional Lower-Order Power-Based Algorithms
### Other Algorithms
When \(p=q=2\) and \(\lambda_{0}=0.5\), the algorithm exhibits optimal noise reduction capabilities in pulse environments. They enhanced its convergence speed through convex combination methods and studied the control capability of the FxGMN algorithm for impulsive noise under different parameter combinations. [PERSON] and [PERSON] employed a continuous logarithmic transformation \(p\)-power cost function for active noise control [25]. The algorithm performs well under various secondary path conditions. [PERSON], based on the FxLMP algorithm, introduced a continuous mixed \(p\)-norm cost formula to improve adaptive algorithms in ANC systems [26]. [PERSON] introduced self-\(p\)-normalized algorithms to achieve faster convergence without compromising the stability error characteristics of the system, where \(\odot\) denotes the Hadamard product and \(r\) denotes the intensity of the reference noise [27]. Some algorithm update formulas are shown in Table 3.  
In summarizing various algorithms, the sign algorithm stands out for its ability to handle impulsive noise without the need for prior estimation of the characteristic parameter \(\alpha\). However, its drawback lies in a relatively slow convergence speed, necessitating enhancements through techniques like affine projection, variable step size, and momentum. The mixed norm algorithm combines error \(l_{1}\) and \(l_{2}\) moment algorithms, offering a balanced approach, where the former boosts robustness and the latter improves convergence speed. Meanwhile, the continuous \(p\)-power algorithm introduces a logarithmic transformation for rapid convergence, albeit at the expense of a higher complexity. Choosing the most suitable algorithm depends on specific application requirements and contextual considerations. When exclusively considering the algorithm's effectiveness, the FxGMN algorithm is applicable. Due to the property of convex combination, this approach enhances the robustness of the MN algorithm while keeping up with convergence rate. When the primary concern is complexity, the SA algorithm can be used, which is easy to implement, but the algorithm has poor stability in the mixed noise environment with Gaussian noise or in the impact noise with a high intensity. The continuous mixed \(p\)-norm algorithm can be used when faster convergence is required.
## 3 The Data and Previous Work
### Previous Work  
In our previous work, we computed univariate and bivariate entropy for three features--packet size, inter-packet delay, and packet process time. Our initial results showed that all three features have higher entropy values under DOS and MITM attacks. We concluded that entropy could be a potential indicator to alert the system of some anomaly, although it would require further investigation to detect the actual nature of the anomaly as such.  
Our results from joint entropy analysis with each feature pair showed that bivariate joint entropy analysis with packet process time and inter-packet delay can indicate the presence of malicious activities, while the other feature pairs do not convincingly point toward that direction. Our detailed results and discussion can be found in [24].
###### Abstract  
This study examined the relationship between body composition and on-field, in-game physical performance in female collegiate soccer players. Body composition, including total mass, fat mass, and lean tissue mass for the lower extremities and total body, was measured in 10 starting players using dual energy x-ray absorpitometry (DXA). On-field, in-game physical performance was tracked via a global positioning system (GPS) over 14 regular-season games, measuring total distance and distance covered in six speed zones. Players covered 4544.7 \(\pm\) 495.2 m in the first half of the game and significantly less distance in the second half (3356.5 \(\pm\) 1211.7 m, \(p\) = 0.004). A repeated measures ANOVA revealed decreased distances in jogging, low-, and moderate-intensity running during the second half compared to the first half of the game (\(p\) < 0.001). Lower total-body fat mass, total-body fat percentage, and lower-extremuities fat mass were correlated with greater distances at moderate- and high-intensity running during the second half and entire game (r values from \(-\)0.644 to \(-\)0.745, \(p\) < 0.01 to 0.04). These findings suggest that body composition can influence the distance covered at moderate- and high-intensity running speed during competitive games. Training strategies aimed at reducing fat mass and incorporating high-intensity training may benefit female soccer players and enhance team success.  
performance; football; body composition; GPS +
Footnote †: journal: Applied Sciences
# Analysis of Multilayered Ground Amplification Characteristics by Scaled-Down Model Tests
## 1 Introduction
However, due to the possibility of damage to equipment in constructing bedrock and the difficulty in laying and removing accelerometers, it is recommended to change the compaction level of the soil to form the ground, or to create a multilayered ground by mixing gravel and collected soil samples. Thus, it is necessary to utilize materials with different properties and densities to implement more than one layer with different ground properties. Although there are many studies using numerical analysis, most of the analyses have been performed to study a single type of soil, which is not consistent with the actual situation. Furthermore, many studies consider boundary effects but ignore variations and differences in seismic wave amplification through the ground with two different soil properties. In this paper, the dynamic characteristics of multilayered ground under the different seismic waves were analyzed by using the 1 g shaking table test. By creating multilayered ground, different layers have different natural frequencies. Due to the differences in physical properties between layers and the existence of a boundary between layers, the amplification characteristics and dynamic performance differences of each layer can be clearly captured. Combined with the experimental method and analysis method, the dynamic characteristics of various grounds can be explored. Shaking table tests are performed by creating a multilayered ground and verified by numerical analysis, which was not very common in previous studies. This way combined with the similarity law, the dynamic behavior of large-scale models can be easily predicted. Based on previous studies, this study considers in more detail the problems that will be faced in the actual ground.
# Performance of Payement Temperature Prediction Models
## 2 Materials and Methods
### Literature Review
#### 2.1.1 SHRP Superpave Model  
SHRP developed the first Superpave(tm) method for asphalt mix design between 1987 and 1993 that included temperature models as part of the design method [2]. Two models are used to estimate minimum and maximum pavement temperatures, from air temperature. A theoretical model is used to estimate maximum pavement temperature from the maximum air temperature and geographical location (latitude). This model was developedbased on the results of heat-transfer modelling and regression analysis. [PERSON] (1986) further simplified the equations for maximum and minimum pavement temperatures. Pavement and air temperatures collected at 30 test sites throughout North America were used. Thus, the model may require verification and calibration for use at other geographical locations.
# SMS: A Secure Healthcare Model for Smart Cities
## 5 Role of MEC in the Proposed SMS
#### Blockchain in SMS
Consider a situation where a patient is about to get a heart attack, and, based on the constant real-time monitoring and historical data analysis, the doctors are able to predict it well before time. Thus, this predictive healthcare can provide revolutionary measures to know the nature and severity of the event even before it occurs and, thus, can save many valuable human lives. All this is made possible only because of the possibility of processing the data close to its source of origin with the help of edge  
Figure 5: Smart contract in SMS. computing paradigms. In MEC, the \"device relationship management (DRM)\" software is responsible for monitoring and managing edge devices. The DRM is capable of providing the facilities like caching, filtering, optimizing, and buffering of the datasets. Algorithm 1 provides the mechanism for handling requests in the SMS. ```
1: Request\(\rightarrow\)Ri
2: Request Type (RT)\(\rightarrow\)[Urgent, Normal];
3: For \(\forall\) Ri \(\in\) RT
4: If (Ri = Urgent)
5: Forward the Data/Request to Edge Nodes
6: Process the Data/Request
7: Return the results to the User
8: Else
9: Forward the data/Request to the Cloud
10: Process the Data
11: Return the data to the user
```  
**Algorithm 1** Request Handling in SMS
## 4 Experiment and Analysis
### Comparison Experiment of Recognition Distance with QR Code  
Compared with QR codes, one of the prominent advantages of OBC is the long effective distance. In this section, by increasing the distance between the receiver and transmitter, we compared the performance of OBC and QR codes whose area is the same as the LED's luminous area. The recognition rate data of QR code is obtained from the scanning function of QR code in many Applications in the smartphone used in the experiment. Under the condition of no magnification, scanning within 5 s is considered a successful recognition. The result is shown as Figure 18.  
As shown in the Figure 18, when the distance is greater than 3.5 m, the recognition rate of QR code drops sharply and almost loses the recognition ability, but the OBC still maintains a high recognition rate at 6 m.
# Federated Optimization of \(\ell_{0}\)-norm Regularized Sparse Learning
## 5 Experiments
### Simulations  
To generate synthetic data, we follow a similar setup to that in [37]. In simulation I, for each device \(i\in[100]\), we generate samples \((z_{i,j},y_{i,j})\) for \(j\in[100]\) according to \(y_{i,j}=z_{i,j}^{T}x_{i}+b_{i,j}\), where \(z_{i,j}\in\mathbb{R}^{1000}\), \(x_{i}\in\mathbb{R}^{1000}\). The first 100 elements of \(x_{i}\) are drawn from \(\mathcal{N}(u_{i},1)\) and the remaining elements in \(x_{i}\) are zeros, \(b_{i,j}\sim\mathcal{N}(u_{i},1)\), \(u_{i}\sim\mathcal{N}(0.1,\alpha)\), \(z_{i,j}\sim\mathcal{N}(v_{i},\Sigma)\), where \(\Sigma\) is a diagonal matrix with the \(i\)-th diagonal element equal to \(\frac{1}{i!2}\). Each element in the mean vector \(v_{i}\) is drawn from \(\mathcal{N}(B_{i},1)\), \(B_{i}\sim\mathcal{N}(0,\beta)\). Therefore, \(\alpha\) controls how much the local models differ from each other, and \(\beta\) controls how much the local on-device data differ between one another; hence, we have simulated Non-IID federated data. In simulation I, \((\alpha,\beta)\in\{(0.1,0.1),(0.5,0.5),(1,1)\}\). The data generation procedure for simulation II is the same as the procedure of simulation I, except that \(y_{i,j}^{\prime}=\exp(z_{i,j}^{T}x_{i}+b_{i,j})/(1+\exp(z_{i,j}^{T}x_{i}+b_{i, j}))\); then, for the \(i\)-th client, we set \(y_{i,j}=1\) corresponding to the top 100 of \(y_{i,j}^{\prime}\) for \(j\in[1000]\); otherwise, \(y_{i,j}=0\). In simulation II, we also set \((\alpha,\beta)\in\{(0.1,0.1),(0.5,0.5),(1,1)\}\).  
The results in Figure 2 show that, with a higher degree of Non-IID, both Fed-HT and FedIter-HT tend to converge slower. We also compare the proposed methods with the baseline method--Distributed IHT. In Figure 3, we observe that in simulation I, FedIter-HT only needs 20 (\(\sim\)5\(\times\) less) communication rounds to reach the same objective value that the Distributed-IHT obtains with more than 100 communication rounds; in simulation II, the FedIter-HT needs 50 communication rounds (\(\sim\)4\(\times\) less) to achieve the same objective value that the Distributed-IHT obtains with 200 communication rounds.
# A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes
###### Abstract  
A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013-2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age \(<\) 20 years, or missing data. KNHANES 2013-2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care.  
undiagnosed diabetes mellitus; screening model; non-invasive variables; deep neural network +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Results
#### 3.1.2 Establishment and Verification of the MLR Model  
We used all color features (a), principal component factors extracted from all color features (b), all color average features (c), principal component factors extracted from all color average features (d), R_mean, G_mean, B_mean, and Gray_mean (e) as input factors, and chlorophyll content as the output parameter. Five MLR models of chlorophyll content in _Astragalus_ seeds were established by stepwise regression.  
The residual analysis results are shown in Figure 4. It can be seen from the histogram of standardized residuals that the standardized residuals of the five models basically obey the normal distribution with a mean value of 0 and a standard deviation of 1. At the same time, it can be seen from the normal probability diagram (P-P diagram) that the scatter basically walks around the diagonal of the first quadrant, so it is judged that the residual basically obeys the normal distribution. It can be seen from the scatter diagram of standardized residuals and standardized predicted values that the scatter fluctuation range of standardized residuals basically remained stable and did not change with the change of standardized predicted values, so it met the homogeneity of variance.  
Generally, the adjusted R\({}^{2}\) represents the degree that the linear equation can reflect the real data. The Durbin-Watson statistic is usually used to determine whether there is autocorrelation between data. The variance inflation factor (VIF) value is usually used to judge whether there is collinearity between independent variables. If the VIF is \(\leq\)10, it means that there is no collinearity between independent variables, and the model operation is more accurate.  
The prediction results of the model are shown in Table 2. It can be seen from Table 2 that there were no significant differences among the five models, and the adjusted R\({}^{2}\) was about 0.98, which indicates that the linear equation reflects the real data better. In this study, the  
Figure 4: Residual analysis results of five multiple linear regression (MLR) models. (**a**) All color features; (**b**) principal component features extracted from all color features by principal component analysis; (**c**) average features of all colors; (**d**) principal component features extracted from principal component analysis of color average features; (**e**) R_mean, G_mean, B_mean, and Gray_mean.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Simulation-Based Approach  
The simulation-based approach has recently gained traction as a useful method for decision makers to efficiently analyze the resilience of interdependent critical infrastructures (ICIs). In situations such as terrorist attacks on ICI systems, it is crucial to have simulation models that can accurately assess resilience and provide targeted solutions to address vulnerabilities in the infrastructure. A case study demonstrating the use of a simulation-based approach was presented by [58], which investigated the response capabilities of a fire department under various terrorist scenarios. The authors in [58] also proposed a discrete event simulation model, focusing on preparedness as a key factor in pre-event disruption resilience. Two types of simulation models were extensively discussed in their work: the first model deals with the social response or population dynamics following a terrorist attack, while the second model examines resource allocation management in affected areas. For both simulation models, resilience quantification was based on the response time (\(R_{T}\)). Additionally, in [59], the authors suggested employing a simulation-based approach within the supply chain context to assess resilience. Their simulation models were centered on three critical resilience factors: preparedness, responsiveness, and recovery, with the Introduction of Time Absolute Error (ITAE) as an innovative resilience measurement.  
_Limitations:_ One of the primary challenges in this approach is the complexity of modeling interdependent infrastructures to evaluate resilience. Moreover, an accurate simulation requires reliable data related to the infrastructure being modeled, which may not always be readily available. These limitations emphasize the need for better modeling techniques and data collection methods to enhance the effectiveness of the simulation-based approach for assessing the resilience of ICIs.
## 3 Result and Analysis
### Initial Determination of Activator Dosage  
The stepwise regression analysis of the quadratic polynomials refers to a method combining regression analysis and derives the mathematical relationship model between multiple independent and dependent variables [62]. For the DA formula experiment, the amounts of Q, DA, GM, and SH were used as independent variables, which were represented by \(X_{1}\), \(X_{2}\), \(X_{3}\), and \(X_{4}\), respectively. The compressive strength of CTB samples with different curing ages was used as the dependent variable. A quadratic polynomial stepwise regression equation was also established to initially determine the optimal proportion of the activator dosage.  
(1) Results of regression analysis of 14-day cured CTB strength  
Figure 7: Trend line of the mechanical strength acquisition of CTB samples with different excitation agents: (**a**) quicklime (Q); (**b**) desulfurized ash (DA); (**c**) glauberite mineral (GM); and (**d**) sodium hydroxide (SH).  
\[Y_{14d}=-15.13+0.039X_{1}+1.86X_{2}-0.054X_{2}*X_{2}-0.014X_{3}*X_{3}-0.64X_{4}*X_{4}\] \[+0.016X_{1}*X_{4}+0.38X_{3}*X_{4}\] The optimum ratio was Q 5.5%, DA 17%, GM 2.5%, \(SH\) 1.0%; \(Y_{14d}\) is 1.404 MPa.
2. Results of regression analysis of 28-day CTB strength \[Y_{28d}=-38.27+10.37X_{1}+1.68X_{2}-0.45X_{1}*X_{1}-0.0027X_{2}*X_{2}+0.32X_{3}*X_ {3}\] \[-0.31X_{1}*X_{2}-0.27X_{1}*X_{3}\] The optimum ratio was: Q 4.5%, DA 19%, GM 2.5%, \(SH\) 1.0%; \(Y_{28d}\) is 2.43 MPa.  
According to the results of the influence of each activator on the compressive strength, as well as the stepwise regression analysis of the quadratic polynomials, the dosages of activator were determined to be 2.5% \(GM\) and 1% \(SH\).
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 3 Results
### Evaluation of Performance under Different \(\eta_{b}\) and \(\eta_{m}\) Conditions
Applicability of \(\iota_{\mathrm{{}_{\mathrm{{}_{\mathrm{{}_{\mathrm{{}_{\mathrm{{max}}}}}}}}}}}\) and \(\iota_{\mathrm{{}_{\mathrm{{}_{\mathrm{{}_{\mathrm{{max}}}}}}}}}\) in Dipole Source Localization Using Real EEG Data  
We implemented the dipole source localization process using (13) and (15) in the data of two different subjects. For the purpose of this test, we consider the search of \(L=3\) sources as in the case of Section 2.6.1. Since the trials were presented three times for the five selected frequencies, we were left with a 15 s period of stimulation for each frequency which we divided into shorter processing windows of 1 s. Since the data provided no anatomical information of the subjects, we approximated them by using the same head model as in Section 2.6.1. This process required the sensor locations to be adjusted (morphed) to our head model. Finally we evaluated our neural indices \(\  
\(t_{\text{{R}}_{22}}(\theta_{\text{{cmd}}})\), i.e., the maps show the average activity index over 15 processing windows for each candidate position in the ROI. Since a single \(\mathbf{r}_{cl}\) can be part of multiple combinations of \(\theta_{\text{{cmd}}}\), those index values were also accounted and averaged. Under those conditions, our results show very distinctive activation over primary visual cortex V1 (right for Subject 5 and left for Subject 10) in the case of \(t_{\text{{R}}_{1}}\), while activation in secondary visual cortex V2 are more noticeable in the case of \(t_{\text{{R}}_{22}}\) and in the same hemispheres as in the case of the activation of V1 for both subjects. Given the results of Section 3.1 in which \(t_{\text{{R}}_{22}}\) showed less bias and variability than \(t_{\text{{R}}_{1}}\), we believe that activation of V2 could be unnoticeable for \(t_{\text{{R}}_{1}}\) due to estimation errors. Figure 8: Results of the source localization process through our reduced-rank beamforming approach in EEG SSVEP data of Subject 5 in [27] and for a specific frequency. Minimum values of \(t_{\text{{R}}_{(i)}}\) (shown in darker colors) correspond to zones of main brain activation. (**a**) \(t_{\text{{R}}_{22}}\), (**b**) \(t_{\text{{R}}_{22}}\).
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 4 Results  
The primary objective of this research is to assess how well LEO satellite signals penetrate indoor environments. To this end, we performed multiple measurements in a variety of indoor environments. The \(C/N_{0}\) was employed as the key performance metric to compare different environments with each other.
## 2 Related Work
### Physical Attacks  
Analyzing previously recorded CAN frames can reveal information about the system. [PERSON] et al. (2018) developed CarShark, a program designed to intercept CAN frames, revealing their limited range of validity and potential vulnerabilities in specific ECUs. This study was groundbreaking in exposing the vulnerabilities within CAN bus systems. However, its primary limitation lies in the focus on isolated CAN bus attacks without addressing broader vehicle network architectures or the increasing complexity of modern vehicle systems. Additionally, it primarily targets older vehicle models, which may not reflect the current state of automotive technology.  
Exploiting the OBD port, attackers can collect diagnostic data, access the in-vehicle network, and install malware ([PERSON] et al., 2018). [PERSON] et al. demonstrated backdoor creation in BMW entertainment systems via USB port exploitation ([PERSON] et al., 2018). While [PERSON] et al. effectively highlight specific vulnerabilities in BMW systems, their approach may not be generalizable to all vehicle makes and models. Moreover, the study focuses on infotainment systems, potentially overlooking other critical ECU functions. These vulnerabilities allow for the manipulation of fuel levels, speedometer values, or false failure notifications ([PERSON] et al., 2018).