esa-sceva/satcom-chunk-collection huggingface dataset:
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 4 Simulations and Results  
The simulations were implemented by Aqua-Sim [22], which is an NS-2 based simulator for underwater sensor networks and developed at the Underwater Sensor Network (UWSN) Lab at the University of Connecticut. In the experiment, the nodes are randomly located in a square area \(1.5\times 1.5\) km\({}^{2}\) and 500m deep. All nodes in the cluster are static and can reach the other nodes directly. In the network initialization stage, each node measures the round-trip time with other nodes, and broadcasts this information to the others to obtain an inter-node propagation delay table. The acoustic propagation speed is set to 1500 m/s. Data packet size, control packet size, and data rate are 125 bytes, 10 bytes and 1200 bps, respectively. The powers of sending, receiving, and idle states are set to 20, 1 and 0.5 Watt, respectively. Data packets generation for each node follows a Poisson distribution with parameter \(\lambda\) packets/s, and the destination of each packet is selected randomly with equal probability. Channel-related packet losses are ignored, i.e., packet losses only occur due to packet collisions. In the simulations, the normalized throughput, packet delivery rate, average end-to-enddelay, and average energy consumption of the proposed method are compared with underwater ALOHA (UWALOHA), underwater carrier sense multiple access/collision avoidance (UW-CSMA/CA), slotted floor acquisition multiple access (SFAMA) and ROPA protocols with different network loads and node numbers. The normalized throughput, packet delivery rate, average end-to-end delay, average energy consumption and normalized offered load are defined as [23]:  
* Normalized throughput: The ratio of the average number of data bits successfully received by the intended destinations per second to the transmission bit rate (dimensionless).
* Packet delivery rate: The ratio of the number of data packets successfully delivered at the intended destinations to the total number of data packets generated (dimensionless).
* Average end-to-end delay: The average time interval between generation and successful delivery of data packets at the intended destinations (in ms).
* Average energy consumption: The ratio of the total energy consumption to the number of data packets successfully received by the intended destinations (in Joule/packet or J/pkt).
* Normalized offered load: The ratio of the average number of data bits sent per second to the transmission bit rate (dimensionless).  
UWALOHA, UW-CSMA/CA, slot SFAMA are typical ALOHA, CSMA, and handshaking based MAC protocols respectively. ROPA is an underwater MAC protocol that employs the temporal reuse technique to support multiple data transmissions in a transmission cycle.
## 4 Experiment and Results  
### Data Collection and Experiment Design  
In this section, 10-year daily historical stock prices of 500 large-cap stocks listed on the America Stock Exchange Standard & Poor's 500 (S&P 500), which covers nearly 80 percent of the American equity capitalization, was collected by Quandl API from 1 January 2008 till 1 January 2018, with 2516 total trading days. For each stock, daily _Open-High-Low-Close_ and _trading volume_ was used as the main input values of the dataset. The experiments were conducted on the Ubuntu OS machine containing Intel Core i7-7700 (3.60 GHz) CPU with 64 GB RAM and GeForce GTX 1080 Ti 11176 MB GPU. For model configuration, we used Python 3.6 and Keras library with TensorFlow backend.  
In order to set up the hyperparameters for the LSTM prediction model, we first randomly selected one stock from the S&P500 dataset and then performed different measurements. There are two important hyperparameters that might have a high impact on neural network performance, including the number of hidden layers and the number of neurons. We iteratively tuned the number of hidden layers from 2 to 10, and the number of neurons from 1 to 600, to select the optimal model parameters. Prediction loss values were calculated using mean square error (MSE) by adjusting the values of epochs between 5 and 8000; the least loss error was obtained at 4000 epochs. As evident from Figure 6, the minimum prediction loss error was found at 256 and 512 neurons, respectively. A stacked LSTM architecture comprised of 2 LSTM hidden layers was used. As reported in the work of [33], Adam optimization is more suitable for deep learning problems with larger datasets. Therefore, Adam optimizer with default parameters provided by Keras was employed in our experiments. The train-test split ratio used for the LSTM prediction model is 80:20. The detail selected hyperparameters are summarized in Table 1. For machine learning models, the scikit-learn library was used for training prediction models.  
Figure 6: Model parameters selection. MAE-mean absolute error, MSE-mean square error, MAPE-mean absolute percentage error, RMSE-root mean square error.
## 2 Materials and Methods
### Object Detection Model
#### 2.2.3 Crop Row Fitting  
Based on the sugarcane detected by SugarRow-YOLO, the spatial location of each plant was represented using the coordinates of the center point of its corresponding bounding box [26; 27]. Subsequently, all detected crop points were clustered into distinct crop rows according to their spatial representations. DBSCAN is an efficient density-based clustering algorithm that groups data points into clusters based on density connectivity [28]. It classifies data points into core, border, and noise points using two parameters: the neighborhood radius (eps) and the minimum number of points (min_samples). During the clustering process, DBSCAN iteratively assigns border points within the neighborhood of a core point to the same cluster. This property enables DBSCAN to effectively handle crop rows with varying shapes, sizes, and densities, as well as to manage the uncertainty in the number of crop rows across different images.  
Traditional methods such as Hough transform [29] and LSM [30; 31] are primarily employed for the linear fitting of straight crop rows. However, in practical field conditions, crop rows are often curved, rendering linear fitting methods prone to significant fitting errors or even complete failure. In contrast, the smoothing spline technique offers greater adaptability to irregular and nonlinear crop row patterns by achieving an effective balance between fitting accuracy and curve smoothness. Moreover, the continuous and smooth paths generated through spline fitting contribute to enhancing the stability and reliability of agricultural machinery operations.  
Smoothing spline approximates a target curve by fitting a spline function composed of piecewise polynomials of degree k to a set of given data points, ensuring both smoothness and continuity across the entire curve [32]. In this study, spline orders of k = 1 and k = 3 are employed. When k = 1, the resulting fit is a piecewise linear function composed of connected straight segments, which is suitable for scenarios where crop rows are approximately straight. The corresponding formulation is provided in Equation (4). In contrast, when k = 3, a cubic spline function is constructed to better represent curved crop rows, as detailed  
Figure 6: The network structure of SugarRow-YOLO.
## 5 Conclusions  
Substituting sand in concrete with CBA via different mixture design methods (that can lead to different qualities of concrete mix components) is a sensitive issue:  
1. _ReCiPe2016 midpoint method_. The increased substitution of sand with CBA lead to: (i) Decreases in the impacts of global warming potential, terrestrial ecotoxicity, water consumption, and increased fossil resources scarcity (MIX-fixed-SLUMP method) and (ii) increases in the impacts of global warming potential, terrestrial ecotoxicity, water consumption, and fossil resources scarcity (MIX-fixed-W/C method).
2. _Six methodological options of the ReCiPe2016 single score method_. With increasing substitution of sand with CBA: (i) CBA0 and CBA25 caused the most environmental damage, while CBA50-CBA100 caused the least environmental damage (MIX-fixed-SLUMP method), and (ii) CBA0 and CBA25 caused the least environmental damage, while CBA50, CBA75, and CBA100 caused the most environmental damage (MIX-fixed-W/C method).  
Consequently, according to the LCA evaluated in this study, using CBA as a partial sand replacement in the concrete industry is a very controversial issue. Perhaps the use of certain design methods will make it possible to obtain concrete mixtures with the best environmental properties. The result of the LCA is highly dependent on the design method. For further clarification of the environmental effects of replacing sand in the concrete industry with CBA byproducts from electricity production, additional concrete mixtures should be considered in the future.
# The _Ampd1_ Gene's rs17602729 Polymorphism and Athletic Performance in Track and Field Athletes
###### Abstract
This process reduces the buildup of adenosine diphosphate (ADP) and shifts the myokinase reaction toward adenosine triphosphate (ATP) production [15; 16; 17; 18; 19; 20]. AMPD is a key regulator in cellular energy metabolism during intense physical exercise. Nevertheless, single-nucleotide polymorphisms (SNPs) in this gene may result in altered muscle energy metabolism. For instance, in the case of the rs17602729 polymorphism (NM_000036.3c:34C > Tp.Gln12 Ter), in individuals with the TT genotype of the _AMPDI_ gene, the AMPD activity in the skeletal muscles is considerably low. Conversely, those with the CT genotype exhibit moderate levels of AMPD activity, whereas individuals possessing the CC genotype have high levels of AMPD activity. This scenario illustrates the impact of _AMPDI_ gene variations on AMPD activity in the skeletal muscles [21; 22]. The 1000 Genomes project revealed that the _AMPDI_ rs17602729 polymorphism occurs at varying frequencies across different populations: it was observed in European Caucasians at an overall rate of 11%, with specific occurrences of 11% in Finland, 14% in both England and Spain, and 8% in Italy. In contrast, this polymorphism was much less common in Africans, showing a frequency of just 1%. It was found in 8% of the American population surveyed. However, this genetic variation was not detected in any of the Asian groups studied ([[http://www.1000](http://www.1000) genomes.org/]([http://www.1000](http://www.1000) genomes.org/), accessed on 13 January 2024). When examining research related to the mentioned gene, studies have reported that individuals carrying the rs17602729 polymorphism T allele were susceptible to muscle cramps, pain, delayed recovery of muscle strength, and early fatigue during exercise [1; 23; 24; 25]. Moreover, some studies associate this allele with power and sprint disciplines [16; 18; 26] while others link it to endurance activities [27; 28]. The presence of both similar and differing studies across various populations makes this research area particularly intriguing [15; 21; 29]. Considering the aforementioned information, the main aim of this research was to analyze the differences in the genotype and allele frequencies of the _AMPDI_ rs17602729 polymorphism between Turkish elite athletes specializing as sprinters/power athletes and endurance athletes, including a control group for comparison. Additionally, the study aimed to explore the potential link between the rs17602729 polymorphism and competitive performance in elite endurance athletes and sprinters/power athletes. To the best of the authors' knowledge, this investigation is the first pivotal study to analyze the _AMPD1_ rs17602729 polymorphism in elite Turkish track and field athletes.
## 5 Results and Discussion
### Performance Comparison under Different Workloads
#### 5.6.1 Average E2E Delay  
Figure 17 shows the E2E delay for different numbers of normal and malicious nodes, as discussed below.  
1. A total of 50 normal nodes and 1 malicious node. * Healthy network: Figure 17 shows that the E2E delay in the 50-node scenario is higher than the 20-node scenario. This is due to the addition of nodes leading to potentially longer paths and increased contention. * Malicious network: The delay in the 50-node scenario increases when compared to the 20-node case because an increasing number of nodes can amplify the disruption effect of the malicious node, and the malicious node in a more extensive network impacts more routes. It results in increased delay due to rerouting. * The NTRUEncrypt-secured network: The delay in the 50-node scenario increases compared to the 20-node scenario because the protective mechanism prevents  
Figure 17: Average end-to-end delay for different node numbers.  
rerouting delays caused by a malicious node, but the cryptographic overhead from NTRUEncrypt could introduce some additional delay.
2. A total of 50 normal nodes and 5 malicious nodes. * Healthy network: Figure 17 shows that the E2E delay in the 50-node scenario is higher than the 20-node scenario because increasing the number of nodes means more hop counts for a packet to traverse, resulting in more delay. * Malicious network: In high-density scenarios, the delay increases compared to the 20-node case. With five black hole-attacking nodes, there is a higher chance for routes to be compromised, causing packets to reroute and subsequently leading to higher delays. * The NTRUEncrypt-secured network: The 50-node delay increases compared to the 20-node scenario because of the cryptographic processing overhead from NTRUEncrypt; the need for potentially frequent route discoveries due to the presence of black hole nodes can increase delay, even if the protection mechanism avoids some rerouting.
3. A total of 50 normal nodes and 10 malicious nodes. * Healthy network: Figure 17 shows that the E2E delay in the 50-node network is higher compared to the 20-node scenario because the denser network can result in longer paths and more chances of communication congestion, leading to higher E2E delay. * Malicious network: A higher-density delay is potentially much higher than in the 20-node case. This is because the ten black hole nodes will disrupt the network, causing frequent rerouting and more extended path discoveries. It also means higher chances of packets encountering malicious routes, leading to a substantial delay. * In the NTRUEncrypt-secured network, the delay increases in high density compared to the 20-node setup due to the cryptographic overhead from NTRUEncrypt.
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 4 Analysis of Horizontal Rotational Stability
### Analysis of Spring Consistency and Stiffness Calibration  
Manufacturing processes may introduce errors in the wire diameter, outer diameter, and material stress of the springs, leading to stiffness errors and inconsistency among multiple springs. Such inconsistency causes the correction performance of the six-arm wheelset suspension device to deviate from theoretical calculations. To minimize the inconsistency, an adjustment mechanism was designed at the spring and suspension interface for systematic calibration. In addition, the nonzero correction torque caused by the spring inconsistency was dynamically compensated by using the torque generated by the different frictional forces between the left and right wheels and the ground surface.  
Figure 12: Variation curves of the hydro-pneumatic springs’ rotational torque, tension resistance torque, and resultant torque with respect to the rotation angle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Shear modulus of materials \(G\)/GPa & 79 \\ Wire diameter \(d_{1}/\)mm & 10 \\ Mean diameter of the spring \(D_{2}/\)mm & 54 \\ Number of active coils & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of tension springs.  
An adjustable mechanism was installed at the connection between the springs and the modular wheel-leg stator to fine-tune the initial length of the springs, aligning their stiffness as closely as possible with the theoretical values.  
Using a spring stiffness error of \(\pm\)5% as the maximum error for simulation calculations, when two sets of springs along the diagonal were set at +5% and -5%, the maximum correction torque error was determined. The simulation results are shown in Figure 13. With a spring stiffness error of \(\pm\)5%, the spring correction torque generated a resistance/assistance rotational torque of approximately \(\pm\)50 N\(\cdot\)m. For the dual in-wheel motor wheelset drive mode, the dynamic adjustment of the left and right wheels can feasibly and practically compensate for torque errors smaller than 50 N\(\cdot\)m.
## 4 Results and Discussion
### Acoustic Attenuation along Buried MDPE Pipe  
For the buried MDPE pipe, Figure 10 shows the frequency domain response at 3 m and 5.6 m from the acoustic transmitter (ERM vibration motor). The average SNR recorded at these distances are summarised in Table 5 with the change in acoustic SNR between 3 m and 5.6 m calculated as 12.2 dB. With the sensor spacing of 2.6 m, acoustic attenuation along the buried MDPE pipe was therefore estimated as 4.7 dB/m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pipe inner radius** & **Acoustic attenuation** \\
**(mm)** & **(dB/m)** \\ \hline
15.8 & 2.93 \\
36.1 & 1.48 \\
140.8 & 1.16 \\ \hline
**Pipe wall thickness** & **Acoustic attenuation** \\
**(mm)** & **(dB/m)** \\ \hline
9.2 & 1.48 \\
14.0 & 1.69 \\
17.0 & 1.88 \\ \hline
**Pipe wall elastic modulus** & **Acoustic attenuation** \\
**(N/m\({}^{2}\))** & **(dB/m)** \\ \hline \(1.6\times 10^{9}\) & 1.48 \\ \(7.3\times 10^{8}\) & 1.83 \\ \(4.3\times 10^{8}\) & 2.44 \\ \hline
**Pipe wall Poisson’s ratio** & **Acoustic attenuation** \\
**(dB/m)** & **(dB/m)** \\ \hline
0.16 & 1.38 \\
0.25 & 1.39 \\
0.40 & 1.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerically predicted acoustic attenuation along the exposed MDPE pipe with respect to change in pipe inner radius, wall thickness, elastic modulus of the pipe and Poisson’s ratio of the pipe wall.  
shows the acoustic attenuation prediction for a 6 m long buried MDPE pipe. Please note that the modelling results for the three soils are identical, i.e., the data lie on top of each other.  
As no difference can be obtained for the three different soils in Table 6, it suggests that changes in the bulk modulus of the surrounding soil, within the range evaluated, did not affect the acoustic attenuation along the buried pipe. From Figure 11a, the numerically predicted acoustic attenuation along the infinitely long buried MDPE pipe is 1.5 dB/m. Compared to the results from the field trials, the numerical prediction of acoustic attenuation is lower than the measured acoustic attenuation (4.7 dB/m). Figure 11b shows that the numerically predicted acoustic attenuation for the 6 m long buried MDPE pipe used in the field trials is 2.1 dB/m, which is closer to (although still lower than) the value of 4.7 dB/m estimated from the field trial. The acoustic attenuation is thus impacted by two processes: acoustic scattering along the discontinuities of the pipe wall as well as acoustic radiation into the soil. The shorter pipe (finite length) causes more acoustic scattering due to pipe wall reflections at both ends of the pipe, which explains the large attenuation in Figure 11b. One of the limitations of the numerical model is that it only considers acoustic radiation from the pipe into the soil through bulk waves, but [23] have shown that the acoustic radiation into the soil is dominated by shear waves at the pipe-soil boundary. Thus, the estimated acoustic attenuation obtained from the numerical model of a finite pipe is still lower than the value measured in the field.
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 3 Network of Networks in Virtualized Networks
### Influencers in a Network of Networks with Network Slicing  
In addition to modeling the availability state of an NoN with network virtualization, we aim to design NoN influencers to improve reliability. Consequently, we develop a method for detecting influencers in an NoN by applying the PV-NoN model based on the CI algorithm described in Section 2. As can be seen from the definition of the PV-NoN model described in the previous section, a failure of P-nodes occurring on the PN spreads to all interdependent VNs in any type of PV-NoN model. We therefore express the CI of P-node \(i\) as CI\((r_{i}^{P})\), and its definition is given based on Equation (7), corresponding to the sum of the influence on the PN centered around P-node \(i\) and the influence on each VN centered around V-node \(i\). We define CI\((r_{i}^{P})\) as  
\[\text{CI}(r_{i}^{P})=(k_{i}^{P}-1)\sum_{j\in\partial\text{Ball}(r_{i}^{P},l)}(k _{j}^{P}-1)+\sum_{k\in\mathcal{V}}\bigg{[}(k_{i}^{V_{k}}-1)\sum_{h\in\partial \text{Ball}(r_{i}^{V_{k}},l)}(k_{h}^{V_{k}}-1)\bigg{]}, \tag{13}\]  
where \(k_{i}^{P}\) and \(k_{i}^{V_{k}}\) are the node degrees of \(r_{i}^{P}\) and \(r_{i}^{V_{k}}\), respectively. The definition of \(\partial\text{Ball}\) is the same as that described in Figure 2. The first term accounts for the CI of P-node \(i\) within the PN, and the second term accounts for the sum of the CIs of all the V-nodes that are interdependent with P-node \(i\) because of resource virtualization.  
Regarding the CIs of the V-nodes, in the case of type-SD and type-DD, a specific V-node influences neither the V-nodes on the external VN nor the P-node on the PN, and thus CI\((r_{i}^{V_{k}})\) can be defined considering the influence on the VN to which the V-node belongs:  
\[\text{CI}(r_{i}^{V_{k}})=\ (k_{i}^{V_{k}}-1)\sum_{j\in\partial\text{Ball}(r_{i}^{V_ {k}},l)}(k_{j}^{V_{k}}-1). \tag{14}\]  
On the other hand, in the case of type-UD, it is possible for a V-node to occupy all of the resources shared among the VNs, and eventually the interdependent P-node runs out of capacity. Therefore, CI\((r_{i}^{V_{k}})\) can be defined as the sum of the CIs of all the interdependent V-nodes including the CI of the interdependent P-node as given by Equation (13):  
\[\text{CI}(r_{i}^{V_{k}})=\text{CI}(r_{i}^{P})=(k_{i}^{P}-1)\sum_{j\in\partial \text{Ball}(r_{i}^{P},l)}(k_{j}^{P}-1)+\sum_{k\in\mathcal{V}}\bigg{[}(k_{i}^{ V_{k}}-1)\sum_{h\in\partial\text{Ball}(r_{i}^{V_{k}},l)}(k_{h}^{V_{k}}-1) \bigg{]}. \tag{15}\]