esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Effect of HPP and TS Processing on the Quality of Marinated Lotus Root Slices
#### 2.1.1 Volatile Compounds  
Aroma is an important aspect of the quality evaluation of marinated lotus root slices. The volatile compounds in the blank and marinated lotus root slices are listed in Table 1. In total, 18 volatile compounds were identified in the blank marinated lotus root slices, whereas 32 and 26 volatile compounds were identified in the HPP- and TS-processed marinated lotus root slices, respectively. The blank lotus root has a bland and neutral aroma, and the main volatile compounds such as alkyls in the blank lotus root are contributed by the soil and water environments [22]. Most of the volatile compounds in the marinated lotus root slices detected in this study were from the spices, herbs, and seasonings in the brine. Higher concentrations of total volatile compounds were detected in the TS samples than in the HPP samples but not significantly. Phenols (e.g., eugenol), heterocyclic compounds (e.g., ethyl maltol), alcohols (e.g., benzyl alcohol), ethers (e.g., _cis_-anethol), and aldehydes (e.g., benzaldehyde) were the main volatile categories in marinated lotus root slices. These compounds were previously identified as the major contributors to the aromatic components of spices such as clove and fennel [23; 24; 25; 26]. Although all other categories of volatile compounds were significantly (\(p\) < 0.05) higher in the TS samples than in the HPP samples, the volatile phenols with the highest content were not significantly different (\(p\) > 0.05) between the TS and HPP samples. This suggests that HPP can promote the release of volatile compounds at a small amount of brine use to achieve similar aroma quality as TS treatment.
# Drive-by Bridge Damage Detection Using Continuous Wavelet Transform
## 4 Parametric Study
### Boundary Conditions  
To explore the impact of finite support stiffness on the efficacy of the proposed method, we substituted the pin-supports at both ends of the bridge with elastic springs. Commonly available bearings typically exhibit vertical stiffness ranging from \(k_{spr}=1\times 10^{5}\) kN/m and \(k_{spr}=1\times 10^{7}\) kN/m [63]. Hence, we systematically altered the vertical spring stiffness within this range, using equal values for the springs on both sides. Damage is introduced to the model as a 20%, 50%, and 80% reduction in the bending stiffness of the bridge between \(x=10\) m and \(x=11\) m and the analyses were conducted for a vehicle speed of 2 m/s. Road profile A depicted in Figure 2 was used in the analysis.  
Figure 15: Wavelet coefficients at a scale of 600 for different roughness levels.  
Figure 16 presents the wavelet coefficients at a scale of 600 computed from the residual accelerations recorded on a vehicle crossing a bridge with different boundary conditions. Comparing Figure 16a,d, which represent the softest and stiffest boundary conditions, respectively, we can observe that the edge effects associated with the continuous wavelet transform are distributed over a longer section of the bridge when the softest bearing stiffness is used. As a result, the section of the bridge where damage can be identified using the proposed method becomes more limited. More specifically, for bearing stiffness of \(k_{spr}=1\times 10^{6}\) kN/m and higher, it is possible to detect damage between 5 m and 20 m away from the left support. On the other hand, for \(k_{spr}=1\times 10^{5}\) kN/m, the wavelet coefficients become flat only when the vehicle reaches 7.5 m due to the edge effects limiting the possible damage detection to a portion of the bridge between 7.5 m and 17.5 m. When the damage is simulated between 10m and 11m, as is the case in this study, the disturbance in the wavelet coefficients around the damage location can clearly be identified for all three levels of damage leading to successful detection and localization of damage.
## 2 Materials and Methods
### Statistical Analyses  
The General Linear Model (GLM) was used to determine whether the means of tillage methods and soil type, which were used as covariates differed in runoff (mm) generated, surface water retention, and generated \(CN\)s. The GLM equation is given by the following formula:  
\[y_{i}=\theta+\beta_{j}x_{j}+\beta_{k}x_{k}+\beta_{jk}+\varepsilon \tag{4}\]  
where  
* \(\theta\) is the intercept;
* \(y_{i}\) is the dependent variable, and \(i=\) surface water retention, runoff, and \(CN\)s;
* \(\beta_{j}\) and \(\beta_{k}\) are the weights of the independent variables tillage method and soil type, respectively, \(j\) represents the tillage method levels (CP, DP, Ripper, and Basins), whilst \(k\) represents soil type levels (loamy sand and sand);
* \(\beta_{jk}\) is the combined (interaction) weight contribution of the jth level of tillage method and the kth level of soil type;
* \(x_{j}\) and \(x_{k}\) are the independent variables tillage method and soil type, respectively at different levels as specified above;
* \(\varepsilon\) is the regression error term.  
The GLM is an analysis of variance (ANOVA) procedure, in which the calculations were performed using a least squares regression approach to describe the statistical relationship between covariates (tillage methods and soil type) and continuous response variables runoff (mm), surface water retention (\(S\)), and curve numbers (\(CN\)). ANOVA was used to test whether the tillage treatments and soil type means differ from each other, and the results were presented using boxplots. The Tukey test was used to test an experimental hypothesis that the effect of tillage treatments and soil types on runoff, surface water retention, and CNs was not significantly different.  
The Pearson's correlation (\(r\)), calculated as \(r=\frac{\sum(x_{i}-\vec{x})(y_{i}-\vec{y})}{\sqrt{\sum_{i}{(x_{i}-\vec{x})}^{ 2}},\sqrt{\sum_{i}{(y_{i}-\vec{y})}^{2}}}\), was presented graphically using scatter plots indicating the \(r\), \(p\)-values, and regression equations.
# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
## 3 TAKEN-Traffic Knowledge-Based Navigation for CAVs
### Traffic Knowledge Generation-Analysis Module
#### 3.1.1 Waypoints and Velocity Generator
Equations (3) and (4) represent the cost function for the heading and the velocity predictor, respectively. \[headingPredictorModel=\frac{1}{2m}\sum_{i=0}^{m}(F_{1}(x_{i},k)-y_{i})^{2} \tag{3}\]  
where:  
\(x_{i}\) = (a,b) represents the map coordinates or latitude and longitude information, \(y_{i}\) represents the heading at that point and  
\[F_{1}(x_{i},k)=((a+k),b),((a-k),b),(a,(b+k)),(a,(b-k))\]  
such that \(F_{1}(x)\) is the output of the function approximator and \(k\) is linearly spaced values between 0 and \(p\), while sampling coordinates perpendicular to the direction of forwarding velocity of the car and linearly spaced values between 0 and \(q\) while sampling along the direction of forwarding velocity of the car. Here, \(p\) and \(q\) are the vehicle offsets such that, \(0\leq p\leq q\leq vehicleLength*2\), and \(m\) is the number of training instances. \[velocityPredictorModel=\frac{1}{2m}\sum_{j=0}^{m}(F_{2}(x_{j})-y_{j})^{2} \tag{4}\]  
where:  
\(x_{j}\) is the current forward velocity at a given point \((a,b)\),  
\(y_{j}\) represents the desired velocity at that point and  
\(F_{2}(x)\) is the output of the function approximator and m is the number of training instances. Computing the velocity is trivial because it can be obtained easily by the speedometer, however, calculating pseudo velocity can act as a bypass during the cases of latency and glitches while computing the throttle positions. To consider safety, we force the model to overfit. The advantage of this is that one could use the same model architecture to trace different paths and, thus, the amount of storage required to store any path information remains the same. As a result, even without using GNSS systems, the autonomous vehicle would be able to identify itself in the environment by just keeping the track of its heading and velocity. The system is implemented using a feedback loop such that at every timestep the current position and the heading of the vehicle are estimated from the previous timestep's output. In other words, the forward velocity of the vehicle inferred from the velocity predictor model or the speedometer is used to estimate the next position and the heading (using the heading predictor model) of the autonomous vehicle which in turn guides its manoeuvring. This process is represented in Figure 2.
## 3 Materials and Methods
### Sensors' Detail  
The UCI Heart Disease dataset acquires patient health vitals using a variety of medical sensors and diagnostic instruments. Standard patient records are utilized to measure gender and age. A sphygmomanometer is used to measure Testbps and biochemical analyzers are used to measure Fbs and Chol. ECG devices record ST depression (Old Peak) and Restecg readings in order to identify irregularities in cardiac activity. During physical activity, heart rate (Thalach) is recorded using heart rate monitors. Stress test devices that monitor the heart's reaction to exertion are used to evaluate Exang. Nuclear imaging scanners are used in that to examine heart blood flow. Arterial blockages are revealed by the number of main vessels colored (Ca), which is determined by coronary angiography and fluoroscopy  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Sr\#** & **Data Type** & **Attributes** & **Description** \\ \hline
1 & Numeric & Age & Represented in years \\ \hline
2 & Binary & Sex & Denotes gender: 1 for male, 0 for female \\ \hline
3 & Categorical & Chest Pain (CP) & Type of chest pain categorized into four levels (0–3) \\ \hline
4 & Numeric & Resting Blood Pressure (Testbps) & Blood pressure recorded in mmHg (range: 94–200) \\ \hline
5 & Numeric & Cholesterol (Chol) & Cholesterol concentration in blood (range: 126–554) \\ \hline
6 & Binary & Fasting Blood Sugar (Fbs) & Blood sugar level during fasting: 0 (normal), 1 (elevated) \\ \hline
7 & Categorical & Resting ECG (Restecg) & ECG classification in resting state: normal, ST segment abnormality, or Ventricular Hypertrophy (0, 1, 2) \\ \hline
8 & Numeric & Thalach & Peak heart rate recorded in beats per minute (range: 71–202) \\ \hline
9 & Binary & Exang & Indicates occurrence of exercise-induced angina: 0 (No), 1 (Yes) \\ \hline
10 & Decimal & Old Peak & ST-segment depression triggered by exercise (range: 0–4.4) \\ \hline
11 & Ordinal & Ca & Number of major blood vessels visible in imaging (0–3) \\ \hline
12 & Categorical & Thal & Thallium stress test result: 0 (unknown), 1 (fixed defect), 2 (normal), 3 (reversible defect) \\ \hline
13 & Categorical & Slope & ST-segment slope classification: upward, flat, or \\  & & downward (0, 1, 2) \\ \hline
14 & Binary & Target & Outcome variable: 1 (heart disease present), 0 (no heart disease) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset description.  
imaging. These cutting-edge medical sensors and diagnostic instruments guarantee precise data gathering for the study and prediction of cardiac disease.
## 2 Experimental Program  
### Specim Design  
To analyze the influence of concrete type, the composite forms of steel frames and RACSWs, and the connecting stiffness of beam-column joints (BCJs) on the hysteretic behavior of SFIRACSWs, this study designed six specimens of one-story and one-bay, at a 1:3 scale, and divided them into three groups. The first and second groups were designed to analyze the effects of the composite forms of steel frames and RACSWs, and the third group was a pure steel frame that was used as a reference specimen.  
The first group comprised specimens of infilled cast-in-place RACSWs, which were labeled SPE1, SPE2, and SPE3. Only the wall of SPE1 was made of ordinary concrete, to illustrate the effect of concrete type. The walls of the first group were poured at the construction site and connected with steel frames through shear stubs. The second group consisted of specimens of infilled prefabricated RACSWs,which were labeled SPE4 and SPE5. The walls were prefabricated in the factory and assembled rapidly with steel frames through T-shape connectors embedded in the walls. Specimen SPE6 belonged to the third group.  
The span and height of the specimens were 1050 and 1200 mm, respectively; the beam section was HN \(150\times 100\times 5\times 8\), and the column section was HW \(150\times 150\times 7\times 10\). Two forms of BCJs, namely, welded-bolted rigid joint and flush end-plate semi-rigid joint, were adopted, as illustrated in Figure 0(a),b, to investigate the influence of the connecting stiffness of BCJs on the seismic performance of the SFIRACSWs. Specimens SPE1, SPE2, SPE4, and SPE6 adopted rigid joints, while the other specimens used semi-rigid joints. The main parameters of the specimens are presented in Table 1, and the dimensions and details of the specimens are illustrated in Figure 1.  
Figure 1: Dimensions and details of specimens.  
The diameter of the recycled coarse aggregate was 10-30 mm, and the replacement rate of the coarse aggregate was 100%. The cast-in-place RACSWs with a thickness of 90 mm were connected with steel frames by M16 shear studs, which were welded on the steel frames at 110-mm intervals, as depicted in Figure 1d. The \(925\times 860\times 90\) mm prefabricated RACSWs were connected with steel frames by ear plates, T-shape connectors, and high-strength bolts. The spacing of the bolts was 100 mm, and the rapid assembly of the walls and steel frames could be achieved as illustrated in Figure 1e. The ear plate connectors were welded on the flange of steel beams and equipped with stiffeners at 200-mm intervals. The T-shape connectors were embedded in the concrete wall and welded with the steel bars of the hidden beams (Figure 1f).  
Hidden beams and columns were set around the walls. Four \(\Phi\)8 steel bars were in the hidden beams and columns, and the diameter of the stirrup spacing of 50 mm was 6 mm. A double-layer steel mesh was arranged on the wall, and the diameter of the horizontal and vertical steel bars with a spacing of 120 mm was 6 mm. The reinforcement details of the infilled walls are presented in Figure 2.
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
## 4 Case Study
### Sensitivity Analysis  
Given the long development and deployment periods of a mega-constellation, there is much uncertainty. The risk aversion coefficient \(\gamma\) is used for evaluating the degree that decision-makers could accept risk. Let \(ER\) be the expected revenue and \(C_{risk}\) be the cost of risk, then the utility function can be built with the following equation:  
\[U=ER(x)-C_{risk} \tag{18}\]  
When the value of tge utility function increased, the decision would be more satisfactory for investors. The standard deviation is denoted by \(\sigma\), then the following relationship could be obtained with:  
\[C_{risk}=\frac{1}{2}\cdot\gamma\cdot\sigma^{2} \tag{19}\]  
The risk of cost increases with the risk aversion coefficient increasing; the \(\gamma\) can be denoted by:  
\[\gamma(x)=-\frac{U^{\prime\prime}(x)}{U^{\prime}(x)} \tag{20}\]  
Therefore, the general form of the utility function can be built with the following equation:  
\[U(x)=m-n\cdot e^{-\gamma\cdot x} \tag{21}\]  
In this equation, \(m\) and \(n\) are the constant fitting parameters. The relationship between the risk aversion coefficient and net profit is shown in Figure 11.  
Figure 11: The relationship between risk aversion coefficient and net profit.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scenarios** & **10 Satellites Launched** & **15 Satellites Launched** & **20 Satellites Launched** \\ \hline Scenario 1 & 10–19 10–1 & 10–14 15–11 & 10–17 20 \\ Scenario 2 & 20–27 10 & 15–22 15 & 18–36 19–1 \\ Scenario 3 & 28–36 9–1 & 23–36 14–1 & - \\ Scenario 4 & \(\geq\)37 0 & \(\geq\)37 0 & \(\geq\)37 0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimal deployment results under different scenarios.  
The higher the value of the risk aversion coefficient, the higher the degree of uncertainty is during the tge deployment process. This means that there iare more opportunities to make the net profit of the system better.
# High-Resolution Digital-to-Time Converter Implemented in an FPGA Chip
## 4 Experiments
### Average Resolution and Nonlinearity Tests  
The performance of the Virtex-6 DTC is measured within the range of 0-500 ns. When CNT\({}_{A}\) and CNT\({}_{B}\) are both 0, the obtained time interval is 205 ps. When CNT\({}_{A}\) and CNT\({}_{B}\) are both 96, the obtained time interval is 303 ps. Therefore, the obtained average resolution is \((303-205)/96=1.02\) ps. According to Figure 7, a 202 ps fixed error is introduced by the delay difference between the propagation paths of the Start delay line and the Stop delay line. Therefore, calibration work is done by adding the time delay of the Stop delay line by 205 ps.  
The DNL of the delay cells in PDLs are tested and analyzed. The DNL of delay cells in PDL A237-PDL A239 and PDL B237-PDL B239 are shown in Figure 10. The nonlinearity tests are performed in 96 taps. In the tests, we set the number of delay taps within the range of 0-96 and measure the time delay between the input of PDL A237 and the output of PDL A239 through the oscilloscope. The DNL of delay cells can be calculated from the measurement results. The DNL of delay cells in PDL  
Figure 9: Jitter Histogram of CLKA and CLKB. (**a**) Jitter of CLKA; (**b**) Jitter of CLKB.  
A237\(-\)PDL A239 is \(-\)0.074\(-\)+0.079 LSB (least significant bit, 1 LSB = 77 ps). The DNL of delay cells in PDL B237\(-\)PDL B239 is measured with the same method, and the obtained DNL is \(-\)0.065\(-\)+0.075 LSB (1 LSB = 78 ps). The DNL of delay cells in PDL A237\(-\)PDL A239 and PDL B237\(-\)PDL B239 indicates the nonlinearity of the delay cells in two PDL delay lines. The largest DNL error is about 6 ps, which is mainly introduced by the jitter of clock signals and the jitter of the control module of PDL.  
The DNL and INL (integral nonlinearity) of the proposed Virtex-6 FPGA-based DTC are shown in Figure 11. The delay characteristic and the structure of all the PDLs in the delay line are nearly the same. The nonlinearity tests are performed in 200 bins. The DNL is \(-\)0.17\(-\)+0.13 LSB and the INL is \(-\)0.35\(-\)+0.62 LSB. The operation range of the proposed DTC is as large as 590 ns. Time intervals larger than 96 ps are generated by increasing the number of delay taps in Line B and have good delay linearity, which is benefited from the structure of the programmable delay line. Therefore, the nonlinearities are measured within the range of 5 ns and can indicate the nonlinearities of the proposed DTC within the operation range.
# Security Risk Analysis of LoRaWAN and Future Directions
## 6 Security Suggestions for the Improvement of LoRaWAN
### Packet Count Limitation  
This opens a venue for attacks for LoRaWAN, since the communication per \(ED\) is limited and under quota. For instance, each \(ED\) is allowed to transmit at most 14 packets per day (maximum packet payload of 12 Bytes), including the acknowledgments for confirmed up-links [38].
# A Grant-Free Random Access Process for Low-End Distribution System Using Deep Neural Network
## 5 Optimal \(T\)  
The threshold \(T\) plays an important role in enhancing the performance of the system by affecting performance metrics such as the delay and success rate. \(T\) should not be constant across all arrival rates as it may badly affect the performance of the system on some arrival rates. If the arrival rate is low and \(T\) is also low, it means that the traffic is sparse and may not qualify for transmission due to the low value of \(T\). The result is more idle traffic. On the other hand, if the arrival rate is high and \(T\) is also high, the large number of devices may qualify for transmission, and hence collisions will take place. Therefore, the value of \(T\) should vary with the arrival rates and should be optimal such that the idle traffic and collisions are minimized and success is maximized. Hence, we face another subproblem of constrained optimization.  
\[\max_{T,PT,CO}\quad Success \tag{2}\]  
To find out the optimal \(T\), the number of successes is calculated for a value of the arrival rate with changing values of \(T\), \(PT\), and \(CO\). Then, the value of \(T\) and other parameters are chosen that give a maximum value to the number of successes. Then, the optimal value of \(T\) is used afterwards.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 6 Types of Information Transmitted  
Information and knowledge sharing between medical experts, medical students, and patients was carried out using ISDN and VoIP techniques for videoconferencing, video streaming, and podcasting in [45,122].
# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 5 Conclusions  
In the continuous image sequence, the CBAF detection model will have the problem of bounding box jitter in the continuous detection of the object during the deformation process. This paper proposes ODEM-GAN to enhance the training of the CBAF detection model and improve the invariance of deformation for object detection. ODEM-GAN consists of three parts: an online deformable sample creation module, a generator, and a discriminator. The online deformable sample creation module is mainly used to create object deformation samples, the generator is used to generate diverse object deformation samples, and the discriminator is used to compete with the generator to ensure that the samples generated by the generator are sufficiently fake. In addition, based on the adversarial loss function of GAN, a positioning accuracy loss function is proposed, which effectively improves the regression accuracy of the detector. Through comprehensive comparative experiments, it is verified that the proposed ODEM-GAN can enhance the pose and deformation irrelevance of the CBAF detection model for parachute detection and effectively suppress the phenomenon of bounding box jitter. Although our algorithm has obvious advantages for specific tasks, it has certain limitations in the training process, the training complexity is relatively high, and if the specific optimized objects are changed in the training of the process, it needs to be retrained, which is a limitation caused by the fact that GAN networks can only generate one category of specific objects. At the same time, because GAN branches are added to each level of the feature pyramid resulting in a large number of parameters, the training time will be relatively long. To address the above limitations, our future improvement can be based on two aspects: (1) improving the GAN network so that it can optimize multiple categories' objects to improve the robustness of the classifier; and (2) optimizing the network structure so that the output of each layer of the feature pyramid is connected to a unified GAN branch to reduce the complexity of training and shorten the training time.