esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 5 Simulation and Analysis
### False Target Deceptive Jamming  
The distance-angle two-dimensional adaptive beamforming gain of the FDA-MIMO radar is shown in Figure 6. From Figure 6a,b, we can know that the existing deceptive interference EFT1 and EFT2 are located at the sidelobe location of the beam pattern, at which time the beam gain drops sharply, forming a zero notch at this location, while the new deceptive jamming NFT1 and NFT2 are located at the same gain location as the real target, and the beam gain obtained is as high as the target.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parametric** & **Number** & **Parametric** & **Number** \\ \hline Carrier frequency & \(f_{0}=9\ \text{GHz}\) & Frequency offset & \(\Delta f=1\ \text{kHz}\) \\ Array elements & \(M=N=10\) & Snapshot & 2000 \\ Pulse repetition cycle & \(T=0.2\ \text{ms}\) & Range bin & 300 \\ Target position & \((20\ \text{km},5\ ^{\circ})\) & SNR & 10 dB \\ Target delay & \(\tau_{0}=0.05\ \text{ms}\) & JNR & 20 dB \\ EFT1 delay & \(\tau_{1}=0.0733\ \text{ms}\) & EFT2 delay & \(\tau_{2}=0.1233\ \text{ms}\) \\ NFT1 delay & \(\tau_{1}=0.0733\ \text{ms}\) & NFT2 delay & \(\tau_{2}=0.1233\ \text{ms}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The FDA-MIMO simulation parameters.  
Figure 5: The range dimension deceptive interference suppression flow chart.  
The output power of the range-angle two-dimensional adaptive matched filter for existing false targets and new deceptive interference is shown in Figures 7 and 8, respectively. As can be seen from Figures 7a and 8a, the output power of existing deceptive interference EFT1 and EFT2 is greatly weakened, and its power magnitude is not much different from the power of ordinary noise. The new deceptive interference NFT1 and NFT2 have the same large power as the real targets, which is almost unaffected by the adaptive beam.  
The range bin analysis results of existing deceptive interference and new deceptive interference are shown in Figures 7b and 8b, respectively. From Figures 7b and 8b, we also can observe that the output power of the existing deceptive interference EFT1 and EFT2 are greatly weakened, the new deceptive interference NFT1 and NFT2 can effectively influence the range bin of the FDA-MIMO radar, and the interference can appear at any range bin, which can form an effective deceptive jamming effect.  
Figure 6: The distance-angle two-dimensional adaptive beamforming gain of the FDA-MIMO radar. (**a**) Three-dimensional beamforming gain; (**b**) Two-dimensional beamforming gain at the main lobe.  
Figure 7: The range–angle two-dimensional adaptive matched filter output power of the existing false targets. (**a**) Three-dimensional beamforming gain; (**b**) Range bin analysis results.
## 3 Network Slicing Concepts
### Slice Monitoring  
The monitoring module accommodates the real-time state and health monitoring of the deployed slices. It seamlessly communicates this information to the MANO components through the adaptation layer and to the corresponding lifecycle manager.
## 3 Results and Discussions
### Compressive Strength
#### 3.2.4 Influence of SS/SH Ratio  
The AAS is prepared by mixing SH and SS in predetermined ratios. The SS is added to increase the silica content of the geopolymer matrix and SH is essential for dissolution process. In case the geopolymer matrix has a high silica content, more SH is then needed to achieve an affective geopolymerization process. Furthermore, the dissolution process will not be effective if the matrix has not enough of SH [43,44]. It is, therefore, important to find the optimum ratio of SH and SS in the AAS. Figure 6 illustrates the effect of the SS/SH ratios (2, 2.5, and 3.0) on the development of compressive strength of GPC. It can be observed that there is an increase in compressive strength as the SS/SH ratio increases from 2.0 to 2.5, while it decreased from 2.5 to 3.0. The compressive strength of the mixes with 20% and 30% slag content was increased by 15% and 17% respectively by the rise of SS/SH from 2.0 to 2.5. However, when this ratio was further increased to 3.0, a decrease in strength was observed by 7% and 10% respectively for mixes having 20% and 30% slag content. The enhancement in strength can be credited to the modification in microstructure of the geopolymer matrix due to the increased quantity of SS. However, when the ratio is increased to 3.0, the adequate quantity of SH may not be available in the matrix that is required for the completion of dissolution process during the development of geopolymer which resulted in a drop of strength of the GPC mix.
## 2 Methods
### Marking Strategies
```
1:\(\mathcal{R}_{k-1}^{(m)}\)\(\triangleright\) Estimator of the last step
2:\(\text{conform\_refinement}(\mathcal{P}_{\text{R}})\)\(\triangleright\) Process of conformation to avoid hanging nodes
3:\(\mathcal{R}_{k}^{(m)}\)\(\triangleright\) Evaluation of (12)
4:\(\mathcal{P}\)\(\triangleright\) Set of original elements (of size \(N\))
5:\(\mathcal{P}_{\text{R}}\)\(\triangleright\) Set of elements marked to be refined (of size \(N_{\text{R}}\))
6:proceduremark_nextstep(\(\mathcal{R}_{k}^{(m)}\),\(\mathcal{P}\),\(\mathcal{P}_{\text{R}}\))
7:\(\theta\gets 1\)
8:\(i\gets 0\)\(\triangleright\) Number of iterations of mark_nextstep
9:if\(k=1\)then\(\triangleright\) To force a first round of uniform refinement
10:\(\mathcal{R}_{2}^{(e)}\gets 0\)\(\forall e\in\mathcal{P}\)
11:else
12:for all\(e\in\mathcal{P}\)do\(\triangleright\) Go through each element
13:\(\mathcal{R}_{k+1}^{(e)}\leftarrow(\mathcal{R}_{k}^{(e)})^{2}/\mathcal{R}_{k -1}^{(e)}\)
14:endfor
15:endif
16:while\(N_{\text{R}}=0\)do
17:\(\theta\leftarrow\theta/10^{i}\)\(\triangleright\) Adjustment of \(\xi_{\text{cut}}\)
18:\(\xi_{\text{cut}}\leftarrow\theta\cdot\text{max}\{\mathcal{R}_{k+1}^{(1)}, \ldots,\mathcal{R}_{k+1}^{(N)}\}\)\(\triangleright\) Threshold to be marked
19:for all\(e\in\mathcal{P}\)do
20:if\(\mathcal{R}_{k}^{(e)}\geq\xi_{\text{cut}}\)then
21:\(e\in\mathcal{P}_{\text{R}}\)\(\triangleright\) Add to the set of marked elements
22:endif
23:endfor
24:\(i\gets i+1\)\(\triangleright\) Increase the iteration number
25:endwhile
26:\(\mathcal{P}_{\text{R}}\leftarrow\text{conform\_refinement}(\mathcal{P}_{ \text{R}})\)\(\triangleright\) Conform marked elements
27:endprocedure
```  
**Algorithm 1** Marking strategy using the next-step estimator inspired by [38]  
We include the algorithm for the quantile marking strategy in Algorithm 2. We adapt the implementation to the presence of the conformation process: we perform an iteration considering fewer and fewer elements until the number of elements to be refined is lower than the given percentage (specified through parameter \(\theta\)) of elements to be refined. The maximum marking strategy does not require special treatment due to the conformation of the mesh, as shown in Algorithm 3, in contrast to the slight modifications introduced in the fixed-energy fraction and SER algorithms, included in Algorithms 4 and 5, respectively.
# Effects of Fineness and Dosage of Fly Ash on the Fracture Properties and Strength of Concrete
## 3 Experimental Details
### Materials  
Materials used for preparing the concrete mixtures included a Type I Portland cement (Table 1), river sand, crushed coarse aggregate with a maximum size of 10 mm, a naphthalene-sulfurate based superplasticizer, and fly ashes with three levels of fineness. Class F fly ash from the Taichung Power Station in Taiwan was used. The fly ash complied with the requirement of ASTM C168. Table 1 lists its chemical and physical properties. The fly ash was classified using the method of sieving and consisted of three lots:  
1. F175: fly ash passed sieve no. 175 (90 \(\mathrm{\SIUnitSymbolMicro m}\));
2. F250: fly ash passed sieve no. 250 (63 \(\mathrm{\SIUnitSymbolMicro m}\));
3. F325: fly ash passed sieve no. 325 (45 \(\mathrm{\SIUnitSymbolMicro m}\)).
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 3 Energy-Aware Reed-Solomon Scheme  
As described in Section 1, a solar-powered node should satisfy the ENO requirements in an effort to not exhaust all energy (so as to avoid blackout). In addition, it should minimize the amount of energy that is discarded owing to the limited battery capacity and also reserve energy for use during nighttime when energy cannot be harvested. In this section, we explain the proposed EA-RS scheme to address these problems, which dynamically adjusts the sizes of RS blocks using the prediction of the energy input and output on solar-powered sensor nodes.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions  
The proposed UDAF model is applied to demand forecasting, and its effectiveness is assessed through a comparison with currently prevailing state-of-the-art models.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 5 Evaluation Results
### Effect of \(\theta_{E}\)  
Figure 4 shows the effect of the target energy outage probability \(\theta_{E}\) on the average aggregated data rate \(\zeta_{R}\) and the energy outage probability \(\zeta_{E}\). As observed in Figure 4a, \(\zeta_{R}\) of NA-NOMA increases as \(\theta_{E}\) increases. This is because higher \(\theta_{E}\) means that IoT devices can try to transmit data with higher transmission power without less concern about the energy outage. However, because the other comparison schemes operate based on the fixed policy without consideration of the target energy outage probability \(\theta_{E}\), their average aggregated data rate and the energy outage probability do not change regardless of \(\theta_{E}\) (see Figure 4a,b).
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
###### Abstract  
Dual-component radar-signal modulation recognition is a challenging yet significant technique for electronic reconnaissance systems. To improve the lower recognition performance and the higher computational costs of the conventional methods, this paper presents a randomly overlapping dual-component radar-signal modulation recognition method based on a convolutional neural network-swin transformer (CNN-ST) under different signal-to-noise ratios (SNRs). To enhance the feature representation ability and decrease the loss of the detailed features of dual-component radar signals under different SNRs, the swin transformer is adopted and integrated into the designed CNN model. An inverted residual structure and lightweight depthwise convolutions are used to maintain the powerful representational ability. The results show that the dual-component radar-signal recognition accuracy of the proposed CNN-ST is up to 82.58% at \(-8\) dB, which shows the better recognition performance of the CNN-ST over others. The dual-component radar-signal recognition accuracies under different SNRs are all more than 88%, which verified the fact that the CNN-ST achieves better recognition accuracy under different SNRs. This work offers essential guidance in enhancing dual-component radar signal recognition under different SNRs and in promoting actual applications.  
Keywords:dual-component radar signals; different signal-to-noise ratio; convolutional neural network; swin transformer +
Footnote †: journal: Applied
## 2 Materials and Methods
### Application of BSA as a Protein Factor  
For analysis of the effect of protein addition _E. coli_, _F. necrophorum_ and _P. levii_ were used, due to their significant reductions in the previous experiment after one minute (see Section 3.1). For the experimental setup, the final solution was adjusted to contain a concentration of approx. 5% BSA and a bacteria number of approx. 10\({}^{6}\)-10\({}^{7}\) CFU/mL. Thus, the McFarland turbidity standard was adjusted to 3.0 (_E. coli_), 4.0 (_P. levii_) and 6.0 (_F. necrophorum_). For the samples with 5% BSA, 1.66 mL of a 30% BSA solution (Sigma-Aldrich, St. Louis, MO, USA) and 0.5 mL of the bacterial strains were each added to separate sterile test tubes. After the addition of 7.84 mL Tb-PAW, the samples were vortexed and incubated for one minute at room temperature. Then, 1 mL of the solution was added to 9 mL sterile saline solution containing peptone-buffered water (0.85% NaCl, 0.1% peptone, VWR) and again a dilution series were performed. Subsequently 100 \(\mu\)L of the appropriate dilutions were spread in duplicate on Colic (_E. coli_) and Schaeeller agar (_P. levii_, _F. necrophorum_). The agar plates were incubated and evaluated as described in Section 2.2. and Section 2.6. As controls, the Tb-PAW was replaced with untreated TRIS-buffer. As the 30% BSA solution contained sodium azide (NaN\({}_{3}\)) for preservation, additional control samples were run including 0.1% NaN\({}_{3}\) (as an equivalent for 5% BSA) and Tb-PAW.
## 3 Conformity Review According to Fairway Design Standard
### L-Converted Traffic Volume  
Each sea area has different characteristics of passing ships, based on the existence of a water area near the port and the characteristics of the natural environment, such as water area [32]. Considering this, in this study, the concept of L-converted vertebrae was introduced and measured for ships navigating the sea route and not for simple ships. In marine traffic analysis, treating a small vessel of 10 m in length and a vessel of 400 m in length as one traffic volume is inappropriate. Even for a single ship, the size of the sea area occupied by the length and scale of the ship is different, and the degree of risk to the surroundings and the size of the sea area required for safe navigation are different. Converted traffic volume is a quantified relationship that considers the size of the ship, and when converted based on the length of the ship and when converting the number of ships, it is called the L-converted traffic volume. In general, when calculating the L conversion factor, the standard ship length is set as the standard, and the conversion factor is taken and integrated. We set the length of the standard vessel to 82 m, and the amount of traffic was converted [29].  
Figure 5 shows the L-converted traffic volume, converted based on the length of all ships passing the target fairway by time zone for 7 days.  
The maximum L-converted traffic volumes by time of the Wando fairway, the Yeosu area in a Specific Traffic Safety Waters, the Masan fairway, and the Gadeok fairway were 7.9, 19.1, 13.1, and 30.0 ships/h, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Category** & **Wando** & **Yeosu** & **Masan** & **Gadeok** \\ \hline Total Number of Vessels & 779 & 1274 & 957 & 1002 \\ Average Number of Vessels per day & 111.3 & 182.0 & 136.7 & 143.1 \\ Average Number of Vessels per hour & 4.6 & 7.6 & 5.7 & 6.0 \\ Max. Number of Vessels per hour & 17 & 20 & 15 & 18 \\ Length of Largest Vessel (m) & 161 & 400 & 345 & 400 \\ \hline  & Tanker & 27 (3.5\%) & 447 (35.1\%) & 96 (10.0\%) & 193 (19.3\%) \\ Number and & Cargo ship & 45 (5.8\%) & 284 (22.3\%) & 87 (9.1\%) & 301 (30.0\%) \\ Proportion by & Passenger & 103 (13.2\%) & 13 (1.0\%) & - & - \\ Vessel Type & Towing Vessel & 67 (8.6\%) & 32 (2.5\%) & 275 (28.7\%) & 80 (8.0\%) \\ Fishing Vessel & 108 (13.9\%) & 35 (2.7\%) & 119 (12.4\%) & 14 (1.4\%) \\  & ETC & 429 (55.0\%) & 463 (36.4\%) & 380 (39.8\%) & 414 (41.3\%) \\ \hline  & -50 m & 661 (84.9\%) & 642 (50.4\%) & 784 (81.9\%) & 588 (58.7\%) \\  & 51–100 m & 62 (8.0\%) & 273 (21.4\%) & 103 (10.9\%) & 125 (12.4\%) \\ Number and & 101–150 m & 30 (3.9\%) & 152 (11.9\%) & 32 (3.3\%) & 83 (8.3\%) \\ Proportion by & 151–200 m & 26 (3.2\%) & 124 (9.7\%) & 28 (2.9\%) & 50 (5.0\%) \\ Vessel Length & 201–250 m & - & 27 (2.1\%) & - & 5 (0.5\%) \\  & 251–300 m & - & 24 (1.9\%) & 7 (0.7\%) & 59 (5.9\%) \\  & 301–350 m & - & 26 (2.0\%) & 3 (0.3\%) & 58 (5.8\%) \\  & 351 m–\(\cdot\) & - & 6 (0.6\%) & - & 34 (3.4\%) \\ \hline \end{tabular}
\end{table}
Table 4: Results of maritime traffic survey by target fairway.
# Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model
## 2 Methodology
### Fully Active Suspension System Mathematical Modelling  
Figure 7 highlights the quarter car model of the fully active suspension system that was used to derive the time domain equations of the fully active suspension system. As seen in Figure 7, a variable force actuator, \(F_{a}(t)\), was used as opposed to the variable damper, \(b_{1}(t)\), of the semi-active suspension. Note that the passive spring and damper were still maintained in the fully active suspension.  
Applying [PERSON]'s 2 nd Law,  
\[-k_{1}(x_{1}-x_{2})-b_{1}\big{(}\dot{x_{1}}-\dot{x_{2}}\big{)}+F_{a}(t)=m_{1} \big{(}\dot{x_{1}}\big{)} \tag{8}\]  
\[-k_{2}(x_{2}-w)-b_{2}\big{(}\dot{x_{2}}-\ddot{w}\big{)}+k_{1}(x_{1}-x_{2})+b_{1 }(\dot{x_{1}}-\dot{x_{2}})-F_{a}(t)=m_{2}\big{(}\ddot{x_{2}}\big{)} \tag{9}\]  
Equations (8) and (9) are derived by considering the forces acting on the sprung mass, \(m_{1}\), and unsprung mass, \(m_{2}\), respectively.  
With a control input of \(e(t)\), the subsequent control output, \(F_{a}\left(t\right)\), of the PID controller in the fully active suspension system was given by the following:  
\[F_{a}(t)=K_{p}e(t)+K_{i}\int_{0}^{t}e(t)dt+K_{d}\frac{de(t)}{dt} \tag{10}\]  
Figure 6: PID controller design for a semi-active suspension system.  
Figure 7: Quarter car fully active suspension system diagram.  
In Equation (10), \(F_{a}(t)\) is the sum of the proportional control, integral control, and derivative control terms that act on the error signal \(e(t)\). Tuning of the controller gains, \(K_{p},\,K_{i},\,\,K_{d}\), is required to achieve the desired output response for \(F_{a}(t)\).
# A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
## 5 Discussion
#### Limitations and Future Works  
The model is tuned only for two parameters, namely, the learning rate and optimizers. CNN, with only four layers, was employed in this study. Understanding the image quality and using pretrained models is one of the possible future enhancements of the study. Another future work direction is to collect a local dataset and evaluate the model's performance.
## 3 Results Analysis
### Impact Pressure on the Revetment Breakwater  
Safety and stability of the revetment breakwater are directly affected by the wave impact pressure. The results recorded at four typical locations are selected as the representative to analyze the impact pressure induced by the regular wave on structure A and B. The time history of the wave-induced impact recorded in Test 3 is shown in Figure 9. It can be found in Figure 9 that the impact pressure on the revetment breakwater is periodic. In addition, the impact pressure has two peaks in one period as marked in Figure 9. It is indicated that the incident wave is reflected by the revetment breakwater. Linear superposition of the wave impact induced by the incident wave and the reflected wave occurs in front of the revetment breakwater, resulting in the wave impact on the breakwater showing an \"M\" shape at the crests. This phenomenon has the typical characteristics of an impact force. The value of the wave impact recorded at PS20 is the largest one among the four typical positions. One conclusion can be made that the wave impact on structure B is much greater than that on structure A.  
Distribution of the maximum wave impact on the surface of the revetment breakwater under regular wave loading in Test 3 is shown in Figure 10. It can be seen in Figure 10 that the maximum impact force is recorded at the top of structure B, and the value is about 17.27 kPa. From the above analysis, it is known that structure B acts as a vertical reflection boundary. Therefore, structure B is subjected to a great impact under regular wave action.  
Figure 8: Time history of the accumulative volume of overtopping water recorded in Test 3.  
Figure 10: Distribution of the maximum wave impact on the surface of the revetment breakwater under regular wave action in Test 3.  
Figure 9: Time history of the wave impact recorded in Test 3 at the four typical positions (hydrostatic pressure is excluded).  
The time history of the impact force on structure A and B recorded in Test 3 is shown in Figure 11. As can be seen in Figure 11, the maximum value of impact force on structure A is about 0.45 kN per sectional meter in the horizontal direction and 0.65 kN per sectional meter in the vertical direction. The maximum value of impact force on structure B is about 1.8 kN per sectional meter in the horizontal direction. It is found that the wave impact force on structure B is much greater than that on structure A, which is consistent with the analysis above. Based on observation in this test, no failure has been observed. The breakwater finally reaches a stable state after a certain amount of settlement, as shown in Figure 6. It can be deduced that the revetment breakwater can basically keep stable under the impact of this regular wave due to the strong support supplied by the passive pressure of the reclaimed coral sand foundation behind the caisson wall.  
Figure 11: Time history of the impact force on structure A and B applied by regular wave in Test 3.