esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
###### Abstract  
This article investigates the transmission of downlink control signals for multiple unmanned aerial vehicle (UAV) clusters in collaborative search and rescue operations in mountainous environments. In this scenario, a reconfigurable intelligent surface (RIS) mounted on the UAV is utilized to overcome obstacles between the ground base station (BS) and UAVs. By leveraging the fixed channel of the RIS to the BS, the line-of-sight (LoS) path characteristics of the air-to-air channel, and the position information of the UAV, the RIS forms a directional beam by adjusting the RIS coefficient, which points towards UAVs in the cluster. To ensure low delay in control signaling and UAV state transmission, we adopt semi-persistent scheduling (SPS), which allocates pre-specified periodic intervals to each UAV for the formation of corresponding RIS coefficients. The allocation of time slots is constrained by the transmission intervals required by different UAVs and the number of RISs available. We propose a time slot scheduling scheme for UAVs to reduce inter-cluster interference caused by RIS beams. The time slot allocation problem is formulated as a combinatorial optimization problem. To solve this problem, we first propose an intuitive greedy scheme called local interference minimization (LIM). Building upon the LIM scheme, we propose a rollout-based algorithm called rollout interference minimization (RIM). Through simulation, we compare the LIM and RIM schemes with the benchmark scheduling scheme. The results demonstrate that our proposed scheme significantly reduces interference between UAV clusters while satisfying the conditions of periodic transmission and RIS quantity constraints.  
reconfigurable intelligent surface (RIS); unmanned aerial vehicle (UAV); semi-persistent scheduling (SPS)
## 3 Results and Discussion
### Temperature Uniformity  
In a reliable design of power module heatsink, it is important to cool down all chips uniformly. This will improve the current sharing and loss distribution among the chips and enhance the whole lifetime of the power module. Distribution of temperature in chips is investigated and presented in Figure 8. It can be seen that the temperature distribution is better in the new offset design and temperature in the chip areas (hot spots) is lower compared to the conventional offset heatsink. The back-and-forth path (serpentine passage) in this new design is the main reason of this ability to face hot spots.
# Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
###### Abstract
Regarding the topic of fault diagnosis for rotating machinery, many studies have been conducted; however, the following difficulties and challenges are posed:  
* Selection of fault-feature vectors. Effective fault-feature vectors are crucial for rotating-machinery fault diagnosis, but it is challenging to choose the right ones due to the many types of faults and their corresponding feature vectors. * Machine-learning classification methods. Machine-learning algorithms are used for classification, but it is challenging to choose the appropriate algorithm, adjust the hyperparameters, and handle the problem of data imbalance due to the small amount of fault data and the imbalance of sample sizes for different fault types. * Accurate fault diagnosis. Accurately diagnosing faults is the main goal, but it is challenging due to the need to analyze the fault-feature vectors and machine-learning classification results, select different diagnosis methods for different fault types, and achieve real-time diagnosis during operation. This paper proposes a new fault-diagnosis method combing successive variational-mode decomposition (SVMD) [28] entropy values and machine learning, which allows for continuous extraction of modes when the number of modes is unknown. This method, similar to the VMD method, treats the modes as the most spectrally compact signals and achieves mode decomposition by adding criteria to deal with the VMD-optimization problem. In this paper, the new method is validated using simulation data. The results show that the new method can basically converge to the same pattern of modes as VMD with a known number of modes, in the case where the number of modes is unknown. The sections of this paper are organized as follows: Section 2 introduces the basic principles of signal-decomposition methods such as VMD and SVMD, and illustrates the effects of signal decomposition with simulated signals as examples. Section 3 focuses on the fault-specific feature-extraction methods of energy entropy and fuzzy entropy. Section 4 details the experimental dataset, experimental results, and related discussions. The discussion and open issues for future directions are presented in Section 5. Section 6 provides the conclusions of the whole study.
## 3 Model Modification
### Focal-GIOU  
In comparison to the classic IOU (Intersection over Union) computation and the original YOLOv5s IOU calculation, GIOU incorporates the Minimum bounding box to tackle the problem of loss equal to zero when the detection frame and the real frame do not overlap. As a result, the GIOU loss function is more stable during training and more resistant to incomplete target detection, which can lower sensitivity to background noise and false detections. This is especially crucial for UAV crack-detection jobs when there is a lot of background noise and interference. Additionally, the GIOU loss function can provide more accurate target detection results, especially in the presence of irregularly shaped targets (e.g., cracks.) GIOU takes into account the geometry of the targets and measures the similarity between the targets by calculating the difference between the intersection and concatenation of the bounding boxes of the targets. Therefore, using the GIOU loss function better adapts to the shape change and scale change in the target and improves the accuracy of crack detection.  
The use of Focal-Loss can efficiently deal with the problem of excessive category imbalance in the dataset; for the crack identification task, the samples in the normal region are typically substantially greater than the samples in the fracture zone. By introducing Focal Loss, the model may be made to pay more attention to the difficult-to-categorize crack region and reduce overfitting to the normal region, hence improving the model's crack-detection performance.  
Equations (7)-(10) show the Focal-GIOU formulas:  
\[IOU=\frac{|A\cap B|}{|A\cup B|} \tag{7}\]  
\[GIOU=IOU-\frac{|C\backslash(A\cup B)|}{|C|} \tag{8}\]  
\[\mathcal{L}_{GIOU}=1-GIOU \tag{9}\]  
\[\mathcal{L}_{Focal-GIOU}=IOU^{\gamma}\mathcal{L}_{GIOU} \tag{10}\]  
where \(A\) is the Ground Truth. \(B\) is the prediction frame. \(C\) is the closed enclosing interval between \(A\) and \(B\). \(IOU\) is the Intersection over Union (IoU) between the Ground Truth and the predicted bounding box. \(GIOU\) is the metric of the Ground Truth and the prediction frame. \(\mathcal{L}_{GIOU}\) is the loss value. \(\mathcal{L}_{Focal-GIOU}\) is the loss value with the Focal-loss introduced. \(\gamma\) is the hyper-parameter for the curvature of the control curve.
## 4 Simulation Verification
This result indicates that there is further room for improvement in the current three-dimensional electromagnetic model. In other words, even if the S-parameter values are acceptable, there still exists potential for enhancement in performance and efficiency in practical applications. At this point, the theoretical validation of the proposed method in this study has been completed. The next step involves replicating the research methodology in a fully anechoic chamber. This requires positioning the DUT, a robotic arm representing the base station, and a rail system representing the satellite in their respective locations. Subsequently, the throughput from the transmitting antenna to the vehicle-mounted antenna will be measured. By comparing the throughput data at the same transmission location with the S-parameters obtained from the simulation, the focus will be on assessing whether the fluctuations in the data nodes align between the two. For instance, if both the S-parameters and throughput data exhibit poor performance at the rear of the vehicle, this would demonstrate the feasibility of the proposed method. Further analysis reveals that controlling the S-parameters can significantly influence the stability and quality of signal transmission. Therefore, targeted optimization of the design and implementation of the three-dimensional electromagnetic model can lead to improved performance. This can be achieved through adjustments such as modifying the antenna's frequency band, selecting different materials, or optimizing the electromagnetic wave propagation environment. Such refinements will contribute to achieving superior performance outcomes. For instance, the simulation data indicate that different types of antennas exhibit varying effectiveness at different frequency points or bands. Hence, large-scale simulations can be employed to determine the optimal frequency points or bands for 4G or GNSS antennas; this represents one potential method for improvement. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** &
\begin{tabular}{c} **Radio Access** \\ **Technology** \\ \end{tabular} & **MCL in dB** \\ \hline
1 & E-GPRS & \textasci{}164 dB \\
2 & LTE & \textasci{}144 dB \\
3 & LTE-M or Cat-M1 & \textasci{}160 dB \\
4 & LTE-NB or NB-IoT & \textasci{}164 dB \\
5 & SIGFOX & \textasci{}162 dB \\
6 & LORA & \textasci{}157 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MCL supported by different wireless access technologies. Additionally, selecting antennas with superior performance based on their characteristics or altering the installation positions of antennas on vehicles can serve as experimental conditions for refining the model. In summary, optimizing S-parameter values aims to further enhance the overall performance of wireless communication systems, ensuring their effectiveness and reliability in practical applications.
# Fabrication of Low-Twist and High-Strength Metallic Fibre Hybrid Spun Yarns
## 3 Results and Discussion
### Effect of Ply Twisting on Tensile Properties of Plied Yarns  
It can be seen from the results shown in Figure 4, that with ply twisting, the tenacity of yarn is improved while the elongation is reduced to some extent. This might occur due to the reason that the single yarns were produced with Z twist and then plied together with S twist. This opposite direction of folding twist decreases the twist of component single yarns to an extent corresponding to the amount of folding twist in plied yarns. The twisting angle of fibres in component yarns is also reduced with the reduction in Z twist, and helically arranged fibres could be oriented along the yarn axis. In this way, the contribution of fibre strength towards yarn strength is enhanced under an axial tensile load. The effective supplementary binding of superficial fibres (including hairiness) into the bulk of yarn by ply twisting may contribute positively towards the yarn tenacity. Additionally, the thick/thin places of spun yarns are even out during ply twisting, which may also be the cause of an increase in tenacity [50].  
On the other hand, the axial orientation of fibres in plied yarns along with the better binding of surface fibres in the yarn body may help to reduce the elongation of plied yarns [50]. The linear regression equations for the correlation of tenacity and elongation with plied yarns are given in Table 5. The positive signs of slope coefficient for tenacity and negative for elongation have confirmed their direct and inverse relationship, respectively, with ply twisting of spun yarn. The R\({}^{2}\) values of tenacity and elongation are 0.8154 and 0.7915, respectively, which show that the relationship between tensile properties and ply twisting is good enough but not entirely linear.
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 4 Gold Nanoparticle and Nanorods: Clinical Aspects
### Integrin \(\alpha_{\text{v}}\beta_{3}\)-Based Targeting
#### 4.4.4 Radionuclide \({}^{131}\)I  
\(\gamma\) and \(\beta\)- emitter. Recently, [PERSON] et al. [91] produced \({}^{131}\)I-labeled AuNRs conjugated to the cyclic RGD for targeting integrin \(\alpha_{\gamma}\beta_{3}\)-positive B16F10 cells implanted in mice. Their promising results aim at angiogenesis-targeted SPECT/CT imaging; moreover, the authors suggested a multifunctional theragnostic use in view of this AuNR's intrinsic capacity of photothermal conversion in the near-infrared region.
## 3 Comparison Based on Numerical Simulation
### Comparisons of Results  
In this subsection of the study, we compared the wind loads obtained from full-scale measurements (Section 3.1), numerical simulation (Section 3.2), and passive model tests (Section 3.3) in accordance with the proposed research scheme. As depicted in Figure 10, the mean wind pressure distribution measured in the traditional ABL flow field closely resembles that measured in flow field A, with slight differences from the result measured in flow field B. However, all three distribution patterns are significantly smaller than the full-scale measurement data in the side region of flow separation (60-130 degrees).  
Figure 10: Mean wind pressure distributions from full-scale measurement and model tests.  
Figure 9: Model test scenarios in TJ-3 wind tunnel.  
We used the data presented in Figure 10 to quantify and compare the three adverse effects, as shown in Figure 11. As seen from the figure, the Re effects obtained are extremely significant in the side region of flow separation and cannot be ignored. The non-stationarity effects are not as significant as Re effects but still carry significant weight in influencing wind loads. Finally, the turbulent flow characteristics effects are negligible around the half circle as it can be observed that all differences in the mean wind pressure coefficients are less than 0.4 for turbulent flow characteristics effects, but they are usually greater than 1.0 and 0.6 for Re effects and non-stationarity effects, respectively.  
It should be noted that the uncertainty is an important issue concerning all numerical simulations, and a significant source of uncertainty for CFD analyses is the initial state of calculation. Therefore, a simple quantitative estimate of the uncertainty from the numerical process presented in Section 3.2 is included in this portion of the study. By changing the initial phase (\(\varphi\)) of all components in the original senior sinusoidal function (Equation (3)), three other senior sinusoidal functions are obtained as inlet velocity samples. By inputting these three inlet velocity samples into the numerical program, simulations are redone on the Fluent platform in flow field B, and the results for the three additional cases are added in Figure 10 (the three dashed lines). Comparing the results of the 2 nd-4 th runs of numerical analyses with the initial numerical data, it can be seen that they are basically close together, and the uncertainties in relative errors calculated are in the accepted range [0, 60%], suggesting that the uncertainty from the initial state of calculation is insignificant for the present study.
## 5 Stability Analysis of TSWs
### Analysis of Influencing Factors
#### 5.2.1 Influence of Lateral Load Center Position on TSW Stability  
To investigate the influence of LP on TSW stability, the average TSW size was set to 50 m \(\times\) 50 m \(\times\) 3 m with a fixed LW of 4 m. MPS and yield zone data were computed for LP values ranging from 2 m to 25 m. Yield zones were categorized into three areas for statistical analysis: the height bounding zone (HBZ), the length bounding zone (LBZ), and the center zone (CZ). The variation in MPS and yield zones under different LPs is shown in Figure 7.  
As the LP increases, the MPS at points (0, h/2) and (LP, h/2) first increase rapidly and then stabilize. The MPS at point (LP, 0) initially increases and later decreases, with a more pronounced rise than fall. When LP > 13 m, MPS at (0, h/2) exceeds that at (LP, 0); when LP \(\geq\) 22 m, MPS at (LP, h/2) also exceeds (LP, 0). The yield zones in the HBZ increase and then decrease, while those in the LBZ start forming at LP \(\geq\) 7 m and continue increasing. CZ yield zones begin forming at LP \(\geq\) 17 m, with a decreasing rate of growth. The total yield zone count increases with the LP, with the rate slowing over time. When LP \(\geq\) 17 m, both boundary and center zones are in a yield state.  
Figure 7: Variation in the TSW’s MPS and yield zones under the different LP.
## 3 Results
### LiDAR Relationship with Field-Measured Understory Height and Biomass  
Before the fire, LiDAR estimated the average (\(\pm\) stdev) understory vegetation height to be 0.98 \(\pm\) 0.32 m. The average height of the understory changed to 0.84 \(\pm\) 0.35 m five months after the fire. The t-test did not show any significant difference in mean understory height between the two time periods (\(p\) = 0.201).  
Mean understory height estimates calculated from LiDAR had a significant positive correlation with field-based mean height measurements for both pre-fire (May 2018, \(R^{2}\) = 0.617, \(p\)\(\leq\) 0.001, \(n\) = 20) and 5 months post-fire (November 2018, \(R^{2}\) = 0.495, \(p\)\(\leq\) 0.001, \(n\) = 20) (Figure 6A). No significant differences were found in slope (\(p\) = 0.766) or intercept (\(p\) = 0.911) between the two time periods (slope: May 2018 = 0.877, November 2018 = 0.957). Thus, we combined the two datasets from two different months for an examination of the LiDAR height vs. biomass relationship. The values obtained from LiDAR had an average understory height of 0.91 \(\pm\) 0.34 m. The field measured understory height had an average of 0.58 \(\pm\) 0.30 m. The combined LiDAR height estimates regressed against the corresponding field height measurements showed a significant positive relationship with \(R^{2}\) = 0.576 (\(p\)\(\leq\) 0.001, \(n\) = 40) (Figure 6A), a correlation coefficient (r) of 0.770, and an absolute RMSE of 0.216 m (relative RMSE of 18.61%).  
Figure 5: Relationship between field height (m) and understory biomass (Mg ha\({}^{-1}\)) along with the confidence interval of the regression line.  
Figure 6: (**A**) Comparison between field understory height (m) and LiDAR understory height (m) for two sample dates that fell before (May 2018) and five months after (November 2018) a prescribed fire. (**B**) Relationship between LiDAR height (m) and understory biomass (Mg ha\({}^{-1}\)) along with the confidence interval of the regression lines.  
LiDAR-derived understory heights (May 2018 and November 2018) were directly compared with field measured dry understory biomass, collected at the same field plots used for height measurements (Figure 6B). The goodness-of-fit analysis showed the best model form was a linear regression, with biomass versus combined height providing the highest \(R^{2}\) value (\(R^{2}\) = 0.507, \(p\)\(\leq\) 0.001, \(n\) = 40). Similar to the equation for field height, the intercept was above zero but was closer to zero than for field measurements (Equations (3) and (4)).  
\[\textit{Undersory biomass}\ =0.616\ +\ 2.750\times\textit{LiDAR height} \tag{4}\]  
Slope and intercept comparisons performed to compare understory biomass with the field- and LiDAR- derived understory height estimates (Equations (3) and (4)) showed no significant difference between the two methods (slope: field = 3.461, LiDAR = 2.750, \(p\) = 0.278, and intercept: \(p\) = 0.272).
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 2 Related Work
### Analysis of S-MAC Protocol's Backoff Mechanism  
#### 2.2.1 A Description of S-MAC Protocol's Backoff Algorithm  
The discrete backoff algorithm is adopted in S-MAC protocol and it is adopted in IEEE 802.11 MAC protocol as well [11]. The basic idea is the use of a random backoff time in the range of 0 to \(CW\). \(CW\) refers to the value of current contention window. Each time the node fails to send out data, the value of current \(CW\) is doubled. If the transmission is successful, the value of current \(CW\) is set to the minimum [15]. The random backoff time \(BackoffTime\) is obtained based on Equation (1).  
\[BackoffTime=Random(0,CW)\times aSlotTime \tag{1}\]  
In Equation (1), \(aSlotTime\) refers to the size of time slot that is determined by the physical layer. \(Random(0,CW)\) represents the random integers that uniformly distribute in \((0,CW)\). \(CW\) is a random integer in \([CW_{\min},CW_{\max}]\). When the first transmission starts, \(CW\)is set to \(CW_{\min}\) (the minimum value of \(CW\)). If the transmission fails, \(CW\) is doubled until it increases by \(CW_{\max}\) (the maximum value of \(CW\)). \(CW\) will be reset to \(CW_{\min}\) after a successful transmitting.
## 3 Results  
The light transmission values measured for each examined photosensitizer and its respective reference without the product (control reference sample), expressed in arbitrary units, were further analysed using Equation (1) to generate the attenuation percentage of each photosensizer with the above specified concentrations (mg/mL) applied. The data extracted are presented in Figures 2 and 3.  
Figure 3: Plot of means with associated 95% confidence intervals for these results following arcsine transformation. Abbreviations: CUR, Curcumin; MB, Methylene blue; PL, Photolase (methylene blue derivative); TB, Toliudine blue; ICG, Indocyanine green.  
Figure 2: Raw mean attenuation percentages with associated 95% confidence intervals, of all photosensitive applied at different concentrations, and with the associated applied photonic wavelength (nm). Abbreviations: CUR, Curcumin; MB, Methylene blue; PL, Photolase (methylene blue derivative); TB, Toliudine blue; ICG, Indocyanine green.  
The methylene blue derivative activated by a 660 nm laser source and methylene blue itself at the highest added concentration of 10 mg/mL (_w/v_) gave rise to the highest attenuation percentages observed, i.e., 99.10% and 94.50%, respectively. Conversely, the lowest percentage attenuation observed was that with toluidine blue activated by an 808 nm laser.  
Regarding methylene blue and indocyanine green, which are the two photosensizers applied at different concentrations, it is evident that intensity losses are elevated based on increasing its concentration.  
In view of the very high level of reproducibility between the replicate attenuation values determined, ANOVA performed on the arcsine-transformed dataset revealed that there were extremely highly significant differences between all photosensitizer/wavelength treatment groups applied (_p_ = 5.73 \(\times\) 10\({}^{-54}\)). Moreover, the post hoc ANOVA analysis using the Tukey's (hsd) test confirmed that all possible comparisons between these treatment groups were all very highly significant, the largest one observed being that between the PL + 660 nm and the TB + 808 nm combinations. Indeed, with the exception of the PL + 808 vs. MB0.1 + 660 comparison, which had a \(p\) value of 2.51 \(\times\) 10\({}^{-4}\), all these two-sample comparisons had \(p\) values of <10\({}^{-6}\). Since the 95% confidence intervals (CIs) for these mean values were very narrow and not visible when displayed in Figure 3, they are listed below in Table 2 for reference purposes.