esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks
## 2 Related Work
It is specifically suited to indoor environments, since the synchronization is much more difficult to achieve for larger outdoor distances. This work uses LoRaWAN instead of LoRa and does not control the transceiver transmission power to optimize node range and energy efficiency. In [17], a mechanism to improve the packet delivery ratio is proposed by adapting the utilization of the channels according to estimated interference. The amount of interference in a network is estimated by gateways, from the number of packets received on each channel. From it, the probability of channel usage per node (or geographical area) is computed and this information is broadcast to end nodes, which in turn change normal channel allocation scheme according to the usage probabilities. Simulation results with different geographical distribution of nodes and packet loss scenarios show improvements over a uniform allocation of channels. This work does not consider node energy consumption and range or the medium utilization over time. In [18], the LoRA CAD is used to add controlled access to the medium and distribute communication load between different channels/spreading factors. Three incremental algorithms are proposed, the base one avoiding collisions using an interframe spacing mechanism with a fixed number of detections and random backoff. In the second algorithm, end nodes use the results of the CADs to determine which channels are less occupied and use them for sending messages. The third algorithm is applied by the gateway, which has a complete view of the network and broadcasts the global channels load to end nodes, avoiding local biased load perceptions. Laboratory tests show that those algorithmsimprove goodput and radio energy consumption comparatively to LoRaWAN's ALOHA protocol. This work's priority is to improve goodput and not energy consumption and it does not consider the optimization of node range and the usage of available medium time availability.
# A Stackelberg Game Approach to Model Reference Adaptive Control for Spacecraft Pursuit-Evasion
## Appendix A Parameters and Equations  
The following is additional information on the parameters in the LVLH frame of reference. In Equation (1),  
\[A=3/2\text{sin}^{2}i_{\text{ref}}\text{sin}^{2}(kt)\]  
\[B=(1+3\cos 2i_{\text{ref}})/8\]  
In Equation (2),  
\[\mathbf{X}_{d}=\left[\begin{array}{cc}\mathbf{0}_{3\times 3}&\mathbf{I}_{3\times 3}\\ \mathbf{X}_{21}&\mathbf{X}_{22}\end{array}\right]\mathbf{,}\mathbf{Y}_{p}=\mathbf{Y}_{e}=\left[ \begin{array}{cc}\mathbf{0}_{3\times 3}\\ \mathbf{I}_{3\times 3}\end{array}\right]\]  
\[\mathbf{X}_{21}=\left[\begin{array}{ccc}(5c^{2}-2)n^{2}&0&0\\ 0&0&0\\ 0&0&-q^{2}\end{array}\right]\mathbf{,}\mathbf{X}_{22}=\left[\begin{array}{ccc}0&2 nc&0 \\ -2 nc&0&0\\ 0&0&0\end{array}\right]\]  
Among these, \(i_{\text{ref}}\) is the inclination of the reference orbit, while \(i_{\text{sat1}}\) and \(i_{\text{sat2}}\) represent the inclinations of the two spacecraft orbits. \(X_{0},Y_{0},Z_{0}\) are the initial conditions of the relative state between the two spacecraft. \(c\) represents the orbital eccentricity correction factor, \(q\) represents the frequency shift caused by \(J_{2}\) perturbation, and \(l,k,\phi\) are the corresponding dynamic coupling parameters.  
\[n=\sqrt{\frac{\mu}{r_{\text{ref}}^{3}}}\]  
\[c=\sqrt{1+s}\]  
\[i_{\text{sat1}}=\frac{\Delta z_{0}}{kr_{\text{ref}}}+i_{\text{sat2}}\]  
\[k=nc+\frac{3 nI_{2}R_{e}^{2}}{2r_{\text{ref}}^{2}}\text{cos}^{2}i_{\text{ref}}\]  
\[\Delta\Omega_{0}=\frac{\Delta z_{0}}{r_{\text{ref}}\sin i_{\text{ref}}}\]  
\[\Phi_{0}=\text{cos}^{-1}[\text{cos}\,i_{\text{sat1}}\cos i_{\text{sat2}}+ \text{sin}\,i_{\text{sat1}}\sin i_{\text{sat2}}\cos\Delta\Omega_{0}]\]\[\gamma_{0}=\cot^{-1}\left[\frac{\cot i_{\text{sat2}}\sin i_{\text{sat1}}-\cos i_{ \text{sat1}}\cos\Delta\Omega_{0}}{\sin\Delta\Omega_{0}}\right]\]  
\[\hat{\Omega}_{\text{sat1}}=-\frac{3 nJ_{2}R_{e}^{2}}{2r_{\text{ref}}^{2}}\cos i _{\text{sat1}}\]  
\[\hat{\Omega}_{\text{sat2}}=-\frac{3 nJ_{2}R_{e}^{2}}{2r_{\text{ref}}^{2}}\cos i _{\text{sat2}}\]  
\[q=nc-\left(\cos\gamma_{0}\sin\gamma_{0}\cot\Delta\Omega_{0}-\sin^{2}\gamma_{0} \cos i_{\text{sat1}}\right)\left(\hat{\Omega}_{\text{sat1}}-\hat{\Omega}_{ \text{sat2}}\right)-\hat{\Omega}_{\text{sat1}}\cos i_{\text{sat1}}\]  
\[l=-r_{\text{ref}}\frac{\sin i_{\text{sat1}}\sin i_{\text{sat2}}\sin\Delta \Omega_{0}}{\sin\Phi_{0}}\left(\hat{\Omega}_{\text{sat1}}-\hat{\Omega}_{\text {sat2}}\right)\]  
\[m\sin\phi=\Delta z_{0}\]
## 7 Conclusions  
This paper studied the energy efficiency of a full-duplex relay system for RF energy harvesting using two protocols, namely DF protocol and time-switching protocol (TS). According to the number of antennas used in the energy harvesting process, the system was analyzed from two angles. An optimal time and optimal power allocation scheme to maximize system energy efficiency was proposed. By calculating the time-switching threshold, we divided time into two parts and discussed them separately. We proved that energy efficiency was a concave function and a monotone decreasing function respective to time. By using the properties of the Lambert function, we worked out the optimal time allocation factor, then the optimal power distribution scheme was obtained by finding the derivative function of energy efficiency with respect to power. We compared the time and power allocation schemes for maximizing energy efficiency under the three schemes (FD dual-antenna energy collection, FD single antenna energy collection, and HD). The results showed that the energy efficiency of the FD dual-antenna energy harvesting was better than that of the FD single-antenna energy harvesting, and the simulation results also showed that the energy efficiency in FD mode was much better than that of the HD mode; therefore, the system performance was significantly improved.  
Conceptualization, Y.N.; data curation, Y.N.; formal analysis, Y.N.; funding acquisition, X.S.; methodology, Y.N.; project administration, X.S.; software, Y.N.; supervision, X.S.; validation, Y.N. and X.H.; visualization, Y.N.; writing--original draft, Y.N.; writing--review and editing, X.S., Y.N., L.D. and L.Q.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 7 Evaluations
### Prolonging Lifetime  
The two methods (PWR and PDR) that can extend a node's lifetime are also implemented in Spear. We evaluate the performance in a star network where the central node \(u_{c}\) has \(|N_{c}|=20\) neighbors. In PWR, \(m=30\) levels of remaining energy and corresponding duty cycles are generated in advance. In PDR, \(\hat{\theta}_{0}\) is set to 0.3, \(P_{min}=200\), and the node adjusts the duty cycle every \(\hat{T}=100,000\) time slots.  
Figure 5: Sensitivity of \(p_{1}\) in the PPR method. (**a**) \(\theta_{c}=0.3\); (**b**) U-Connect-PPR.  
**Lifetime**. The lifetime of \(u_{c}\) is illustrated in Figure 7 for different algorithms, both PWR and PDR prolong the lifetime significantly. For example, the lifetimes of PWD and PDR are 5.15 and 6.47 times longer than bare Hedis, respectively.  
**Percentage of Remaining Energy**. We show the percentage of remaining energy (\(y\)-axis) after \(2\hat{T}\) and \(3\hat{T}\) time slots in Figure 8. After \(2\hat{T}\) time slots, PWR and PDR are just a little better than the bare Hedis protocol (see Figure 8a), while the difference becomes much larger after \(3\hat{T}\) time slots (see Figure 8b). By incorporating PWR and PDR, Spear can save power and extend the lifetime significantly.
## 3 Realization of \"Touch-and-Away\" Behavior
### Player Detection and Tracking  
To approach the circle, we need to detect and track the player. We used the person tracking method using an LRF [12; 26]. Using this method, we can recognize human bodies, obstacles, and their center coordinates.  
Figure 3 shows human detection using an LRF [26]. The LRF scans the field from right to left. We let \(D(\theta)\) be the measured distance at the angle step \(\theta\) from the LRF to the obstacle. Then, let \(\Delta D(\theta)\) be the distance difference, such as  
\[\Delta D(\theta)=D(\theta)-D(\theta-1). \tag{1}\]  
Figure 1: The “Touch-and-Away” behavior.  
Figure 2: The robot’s movement to avoid collision.  
When observing a smooth object, \(|\Delta D(\theta)|\) may have relatively large values at the left and right edge of the object. After detecting the edge points of the object, we can decide whether the object is a human body or not based on the object's size. We calculate the body's center point as the center of the left and right edge points.  
As pointed out by [PERSON] et al. [12], it is a problem that the player's arms are misrecognized as bodies. To solve the problem, we employed the robust object boundary detection method described in [12].  
Once a player is detected, the robot continues to track the player. Figure 4 shows how we track the player [16]. Let \(\textbf{{P}}(t)\) be the position of a player at time \(t\), where the unit of time is the frame of the LRF scan. At the next scan, we observe the players' positions again, then calculate the position difference. When the distance between two observations is small enough, the player at \(\textbf{{P}}(t+1)\) is regarded as the same player as \(\textbf{{P}}(t)\), meaning that:  
\[|\textbf{{P}}(t)-\textbf{{P}}(t+1)||<r \tag{2}\]  
where the threshold radius \(r=600\) (mm).
## 4 Experimental Results
### Computational Time  
To further evaluate the performance of the competing algorithms, we recorded the computing times for 30 runs of each algorithm with the three models and presented them in Figure 11. As can be seen from this figure, different times were spent to identify the parameters of each model of the algorithm. Among the 12 algorithms, the GAs took the longest time to solve the three models, while JAYA took the least amount of time for the SDM and DDM. Aside from that, SSA required the least computational time to solve the PVMM, followed by GWO, JAYA, and the ICO. For the SDM, after JAYA, the ICO required the least computing time. The DDM, PSO, GWO, WSO, and SSA had almost the same computing times, and the ICO needed a little more time than them. Compared with the original algorithms such as JAYA, GWO, WSO, and SSA, the time spent by the ICO was comparable. Its superior performance over these algorithms, however, is significant from a statistical perspective. In addition, although the CO, SEDE, and PGJAYA showed significant performance in terms of statistical results, they required more computational time than the ICO. Compared with the CO, the main advantage of the ICO is its ability to reduce the computing time while maintaining or even improving its performance.
## 3 Data and Methods  
The workflow of this study is presented in Figure 5. In the InSAR analysis, the well-known open-source package InSAR Scientific Computing Environment version 2 (ISCE2 version v2.5.0, [[https://winsar.unavco.org/software/isce](https://winsar.unavco.org/software/isce)]([https://winsar.unavco.org/software/isce](https://winsar.unavco.org/software/isce)) (accessed on 12 October 2023)) was used to generate a single interferogram and single look complex (SLC) stack. SBAS analysis was performed with the Stanford Method for Persistent Scatterers (StaMPS version 4.1b1, [[http://homepages.see.leeds.ac.uk/~earahoo/stamps/](http://homepages.see.leeds.ac.uk/~earahoo/stamps/)]([http://homepages.see.leeds.ac.uk/~earahoo/stamps/](http://homepages.see.leeds.ac.uk/~earahoo/stamps/)) (accessed on 12 October 2023)) software [54; 55; 56] to obtain the ground deformation rate, followed by spatial statistical analysis to determine potential landslides.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Epicenter Location** & **Magnitude** &
\begin{tabular}{c} **Distance to Study** \\ **Area (km)** \\ \end{tabular} \\ \hline
18 February 1991 & Xiaojin county & 5.0 & 60 \\
12 May 2008 & Wenchuan county & 8.0 & 108 \\
20 April 2013 & Lushan county & 7.0 & 109 \\
22 November 2014 & Kangding county & 6.3 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Historical earthquakes that impacted the study area (according to the Chinese Earthquake Administration).  
Figure 4: Landslides in the Xiaojin area (data provided by the Department of Natural Resources of Sichuan province). (**a**) Accumulated number of landslides in Xiaojin area and (**b**) the spatial distribution of landslides in the study area determined through a field survey. Note that the accumulated number of landslides dropped in 2014 in (**a**) because some landslides were removed from the inventory as the long-term in situ monitoring indicated no further displacement.
# User Modelling Validation over the Security Awareness of Digital Natives
## 4 Analysis of Results and Discussion
### Management of Credentials  
#### 4.3.1 Question 8  
The security professionals have been asked to estimate how many out of ten digital natives across all SCG groups store personal passwords as plaintext in their mobile devices. It is noticeable from the results presented in Figure 10, which shows that an average of approximately 29% of digital natives follow this practice regardless of their security competence or background, while the security experts overestimated the results in respect to the GSCG/MSCG groups.
## 3 Discussion of the Proposed ZVS ITLDC  
### Design of the Proposed ACAC  
#### 3.1.1 The Auxiliary Current \(i_{\text{A\_peak}}\)  
To avoid the shoot-through of the bridges, a proper dead time \(t_{\text{dead}}\) between the PWM drivers is needed and the dead time \(t_{\text{dead}}\) can be designed according to [22]. The dead time will affect the ZVS operation, as shown in Figure 6a; although the collector-emitter voltage of S\({}_{1}\) reaches zero during the dead time \(t_{\text{dead}}\), the zero-voltage condition will not be maintained till the end of the dead time. When the current \(i_{\text{S1}}\) increases to zero and changes to positive, the equivalent parallel capacitor of S\({}_{1}\) will be charged and \(V_{\text{S1}}\) will increase; then, the zero-voltage condition does not exist. To maintain the zero-voltage condition, the auxiliary current should be large enough. As shown in Figure 6b, with a large auxiliary current, the zero-voltage condition is maintained throughout the dead time interval.  
Due to the large auxiliary inductor, the current \(i_{\text{A1}}\) is equal to \(i_{\text{A\_peak}}\) throughout the dead time interval. When the ITLDC works with no load, the peak value of the auxiliary current \(i_{\text{A\_peak}}\) reaches the minimum. The voltage of the equivalent parallel capacitors should be discharged to zero in the dead time interval; thus, the minimum value of \(i_{\text{A\_peak}}\) can be expressed as (5). When the output current is not zero, the auxiliary current \(i_{\text{A\_peak}}\) should be larger than \(I_{\text{o}}/2n\) (\(n\) is the transformation ratio). When the output current is large enough, the current of the leakage inductance will continue not crossing zero throughout the dead time interval. The voltage applied to the leakage inductance is \(V_{\text{in}}/2\); thus, the critical value of the output current to realize natural ZVS can be expressed as (6). When the converter realizes natural ZVS, the auxiliary circuit does not work, and \(i_{\text{A\_peak}}\) is zero. The minimum value of the auxiliary current's peak value \(i_{\text{A\_peak}}\) can be designed as in Figure 7.  
\[i_{\text{A\_peak\_min}}=\frac{C_{\text{s}}V_{\text{in}}}{t_{\text{dead}}} \tag{5}\]  
where \(C_{\text{s}}\) is the equivalent parallel capacitor of the main switch, \(V_{\text{in}}\) is the input voltage of the ITLDC, and \(t_{\text{dead}}\) is the dead time.  
\[\frac{I_{\text{o\_naturalZVS}}}{n}=\frac{V_{\text{in}}}{2L_{\text{r}}}I_{ \text{dead}} \tag{6}\]  
where \(L_{\text{r}}\) is the leakage inductance of the transformer, and \(n\) is the transformation ratio.  
Figure 6: The turn-on process of the main switch \(S_{1}\) (**a**) with small auxiliary current, (**b**) with large auxiliary current.
## 5 Ablation Studies
### Hash Adaptive Matching
#### 5.1.2 Hash Rounds  
As mentioned earlier, spherical hashing can sometimes assign related feature vectors to different hash buckets, leading to performance degradation. However, by performing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **DPS** & **LSS** & **PSNR\(\uparrow\)/SSIM\(\uparrow\)** \\ \hline Baseline & & & 26.66/0.789 \\ Baseline + HAM (w/o LSS) & \(\surd\) & & 27.72/0.795 \\ Baseline + HAM (w/ LSS) & \(\surd\) & \(\surd\) & 27.88/0.810 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative Evaluation of LSS.  
Figure 6: Comparison of using and not using LSS. (**a**) HR, (**b**) w/o LSS, (**c**) w/ LSS.  
multiple independent hash rounds and combining the results of each round, we can significantly reduce the probability of such incorrect assignments, thus improving the model's robustness.  
To verify this, we conducted experiments with multiple hash rounds and compared the effects of different hash rounds in ablation studies. The experimental results are shown in Table 5. We found that increasing the number of hash rounds significantly improved super-resolution reconstruction performance. Specifically, the results after four rounds of hashing were the most accurate and reliable. During this process, we also achieved a good balance between computational efficiency and performance. The experimental results show that four rounds of hashing improve reconstruction effects while maintaining relatively high computational efficiency.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods
### The Architecture of STFEformer  
STFEformer is a novel transformer-based architecture designed to predict traffic flow by effectively leveraging spatial-temporal traffic data dependencies. This model aims to enhance Intelligent Transportation Systems by providing accurate traffic flow predictions. Figure 1 illustrates the framework of our proposed STFEformer. STFEformer is composed of a fusion embedding layer, a series of L spatial-temporal attention (STA) layers, and an output layer. The \(T\) time steps' traffic flow tensor is input into the fusion embedding layer, where it undergoes transformation to obtain a combined embedding feature representation and an adaptive similarity matrix, effectively extracting both native and spatial-temporal features in traffic data. Following this, the embedding result is then fed into the STA layers, further exploring dynamic and long-range spatial-temporal dependencies. Finally, traffic flow prediction results of the next \(T^{\prime}\) time steps are directly obtained from the output layer through skip connections.  
Figure 1: The framework of STFEformer.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 4 Simulation Results and Analysis
### Hyperdegree Distribution Results  
To verify the correctness of the hyperdegree distribution, the parameters were set as follows: \(N=5000,\ m_{0}=m_{1}=m_{2}=3\), and \(p=0.5\). The simulation plot in double-logarithmic coordinates is shown in Figure 3. The dispersion points represent the simulation results, and the straight lines are the results of the theoretical analysis. The theoretical analysis is consistent with the simulation results and suggests that the hyperdegree distribution follows a linear trend with scale-free characteristics.  
Figure 3: Hyperdegree distribution of the hypernetwork with \(p=0.5\). The black line is the result of the theoretical analysis and the blue dots are the simulation results.  
Moreover, the hyperdegree distribution shows that the power law index is not influenced by the clustering coefficient \(p\). Therefore, we set \(p=0,\ 0.2,\ 0.4,\ 0.6,\ 0.8\), and \(1\) for the simulation. The simulation plot in double-logarithmic coordinates is shown in Figure 4. The simulation results of the hyperdegree with different parameters \(p\) overlap each other. It can be seen that the power-law exponent of our theoretical prediction of the hyperdegree distribution agrees well with the simulation results. This result shows that the aggregation phenomenon does not affect the scale-free property of the social hypernetworks and supports the argument of Section 3.2 that the hyperdegree distribution is independent of the clustering coefficient \(p\).
## 2 Q1: What Are the Recent Trends of UIoT System?  
This section discusses the recent trends and applications developed in the UIoT system along with the communication technologies of the current UIoT system.
## 3 Results
### Noise Level Evaluation Results  
To evaluate the noise level of the reconstructed images for different FDK algorithm parameters, we acquired reconstructed images with various projection angles and filters. The projection angle was increased from 0.4\({}^{\circ}\) to 6.0\({}^{\circ}\) in increments of 0.4\({}^{\circ}\), and reconstructed images were acquired using Ram-Lak, Shepp-Logan, Cosine, Hamming, and Hann filters to evaluate the noise level in the reconstructed images according to the filter used.  
Figure 3: Magnified region of the CBCT image outlined in the red box in Figure 2A reconstructed using various filters of the Feldkamp–David–Kress (FDK) algorithm with various projection angles.  
For noise evaluation of the head phantom, we measured the COV and SNR in ROIs with a matrix size of \(20\times 20\) at the 170 th slice along the z-axis. The ROIs were located in the air, brain, and bone regions. On average, the lowest COV was observed in the brain region among the three ROIs, followed by the bone and air regions (Figure 4). Superior values were found in the following order based on the filter applied in the three ROIs: Hann, Hamming, Cosine, Shepp-Logan, and Ram-Lak. The performance of the most improved Hann filter was measured as a minimum of 0.49 and a maximum of 0.53 in the air region, a minimum of 0.16 and a maximum of 0.21 in the brain region, and a minimum of 0.28 and a maximum of 0.33 in the bone region. On average, the brain exhibited the highest SNR among the three ROIs (Figure 5). In addition, similar to the COV, the image quality improved in the order of brain, bone, and air, and all three ROIs showed superior values in the order of Hann, Hamming, Cosine, Shepp-Logan, and Ram-Lak. The Hann filter exhibited the best performance in the air region, with a minimum of 1.97 and a maximum of 2.04; followed by the brain region, with a minimum of 5.23 and a maximum of 6.08; and, finally, the bone region, with a minimum of 2.95 and a maximum of 3.53. The COV and SNR results for various filters and projection angles are shown in Figures 4 and 5, respectively.
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 4 Discussion
### Reasons for the Difficulty in Estimating Phenology from Remote Sensing
[67], that phenology, and in particular EOS, is more difficult to estimate using remote sensing in heterogeneous ecosystems with a low fractional cover of forests.
# A Compact Monopole Wideband Antenna Based on DGS
## 2 Antenna Design Methodology and Optimization Process
### Parametric Analysis
#### 2.5.2 Effect of Width of L-Slot Vertical (W\({}_{\text{VS}}\))  
The Adjusting W\({}_{\text{VS}}\) to 3.87, 8.87, and 10.87 mm showed that W\({}_{\text{VS}}\) = 8.87 mm provides better impedance matching and a bandwidth of 14.84 GHz. The simulated S\({}_{11}\) for various Wvs values is shown in Figure 7b, while other values resulted in reduced bandwidth. It offers reduced bandwidth and impedance matching with a change in resonance frequency.
# Kinematic Analysis and Verification of a New 5-DOF Parallel Mechanism
## 2 Design and Verification of the Scheme
### Verification of the Primary Scheme
#### 2.2.2 Verification of Degree of Freedom by Screw Theory
That is, the moving platform can achieve the movement of five degrees of freedom--moving along the \(x\)-axis, \(y\)-axis, and \(z\)-axis and rotating around the \(x\)-axis and \(y\)-axis.