esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Relationship Between SLCMJ Height and Maximal and Explosive Hip Strength  
We found no significant relationship between hip maximal or explosive strength and SLCMJ height in our study. Previous research on this topic is conflicting. [PERSON] and [PERSON] (2022) [14] reported weak correlations between maximal hip strength (r = 0.292-0.304) and RTD variables (r = 0.202-0.288) with SLCMJ height in soccer players, explaining only a small variance (r\({}^{2}\) = 0.24). Similarly, [PERSON] et al. (2022) [48] found moderate correlations (r = 0.389-0.469) between isometric hip abduction strength and SLCMJ height, while [PERSON] et al. (2013) [23] found no significant relationships (r = 0.084-0.188, \(p\) > 0.05) using isokinetic testing.  
We speculate that isometric strength may be less relevant to jumping performance due to its limited specificity to dynamic tasks. Strength assessments should align with functional movement demands, including contraction type, timing, and body positioning, making dynamic strength tests more sensitive [42; 49; 50; 51]. Given that our participants performed counter-movement jumps, concentric and eccentric hip adductor strength may be more relevant to predicting SLCMJ height than isometric strength. Future studies should take this into consideration and further investigate the relationship between dynamic hip adductor strength and single-leg jump performance, particularly in contexts that closely replicate sport-specific movements, by prioritizing dynamic strength assessments that provide more functionally relevant data and enhance the applicability of such findings to performance and training contexts. Furthermore, single-leg jump performance is likely influenced by multiple factors beyond hip strength, including balance, neuromuscular control, and intermuscular coordination. Future research should explore how these elements interact with strength to enhance functional performance in tasks like SLCMJs.
# Plantar Loads of Habitual Forefoot Strikers during Running on Different Overground Surfaces
###### Abstract  
The objective of this study is to investigate plantar loads characteristics of habitual forefoot strike runners while running on different surfaces. Twenty-six runners (age: 28.2 \(\pm\) 6.8 y, height: 172.9 \(\pm\) 4.1 cm, weight: 67.7 \(\pm\) 9.6 kg, BMI (body mass index): 22.6 \(\pm\) 2.8 kg/m\({}^{2}\), running age: 5.0 \(\pm\) 4.2 y, running distance per week: 14.6 \(\pm\) 11.7 km) with habitual forefoot strike participated in the study. Runners were instructed to run at 3.3 \(\pm\) 0.2 m/s on three surfaces: grass, synthetic rubber and concrete. An in-shoe pressure measurement system was used to collect and analyze plantar loads data. Running on the synthetic rubber surface produced a lower plantar pressure in the lateral forefoot (256.73 kPa vs. 281.35 kPa, \(p\) = 0.006) than running on concrete. Compared with the concrete surface, lower pressure-time integrals were shown at the central forefoot (46.71 kPa-s vs. 50.73 kPa-s, \(p\) = 0.001) and lateral forefoot (36.13 kPa-s vs. 39.36 kPa-s, \(p\) = 0.004) when running on the synthetic rubber surface. The different surfaces influence plantar loads of habitual forefoot strikers and runners should choose appropriate overground surface to reduce the risk of lower extremity musculoskeletal injuries.  
forefoot strike pattern; plantar loads; sport injury +
Footnote †: journal: Applied Sciences
# Development, Implementation and Evaluation of An Epidemic Communication System
## 3 Implementation Method for Epidemic Communication
### Interference Model Evaluation Method and Selection of Received Power Threshold  
The parameters used in this simulation are the values specified for actual wireless LANs [17]. The conditions under which interference occurs are calculated based on the transmitted and received power using these values and assuming the actual distance based on the drone's flight position, etc. Therefore, we believe that the scenario created by this simulation is reasonable. The calculated results of the available transmission rates based on the distance between the A and B wireless stations when the distance between the B and C wireless stations, which serve as an interference source, is changed for the previous section are shown in Table 2. Figure 6 illustrates the transmission rates obtained from Table 2 and the corresponding received power for distances between the A and B wireless stations set to the minimum and maximum values of 0.3 m and 1.2 m, respectively.  
Table 2 determines the distance between the A and B wireless stations, where wireless station C does not interfere as an interfering wave, and hence, the achievable transmission rate between the A and B wireless stations, when the distance from wireless station A to wireless station B, where communication begins, varies from 0.3 m to 1.2 m. For example,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transmission Rate [Mbps]** & **Receiver Sensitivity Level [dBm]** & **SINR [dB]** \\ \hline
6 & \(-82\) & \(8.82\) \\
9 & \(-81\) & \(9.82\) \\
12 & \(-79\) & \(11.82\) \\
18 & \(-77\) & \(13.82\) \\
24 & \(-74\) & \(16.82\) \\
36 & \(-70\) & \(20.82\) \\
48 & \(-66\) & \(24.82\) \\
54 & \(-65\) & \(25.82\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transmission rate, receiver sensitivity, and SINR of IEEE802.11g standard.  
Figure 4: Example of simultaneous relay transmissions.  
Figure 5: Interference wave model for desired wave.  
when the distance between the A and B wireless stations is 0.3 m, to achieve a transmission rate of 9 Mbps or more, it is necessary to keep the distance between the B and C wireless stations at 1.0 m or more. Therefore, based on these results, in the development of wireless devices for epidemic communication, the transmit-receive range is set to 0.3 m, and the received power at that distance is calculated using Equation (2). Setting the transmit power to \(P_{\text{TX}}\) = 0 dBm (1 mW) and using \(d\) = 0.3 m, \(f\) = 2.4 GHz, \(G_{\text{TX}}\), and \(G_{\text{RX}}\) = 0 dB, the calculated \(P_{\text{RX}}\) is \(-25\) dBm. Thus, in the design of wireless devices, communication is designed to start within the range where the RSSI (received signal strength indicator) is within \(-25\) dBm. However, it is assumed that the distance between the B and C wireless stations is at least 0.9 m to obtain the minimum transmission rate.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 4 System Model: MLSS Concept
The cones represent the coverage areas of satellites where darker colors are used for LEO connections. The orange links visualize the actual connections in each figure. Gateway failure is marked with red X.  
Figure 2: LEO to ground and LEO to LEO traffic backhaul via GSO. The cones represent the coverage areas of satellites where darker colors are used for LEO connections. The orange links visualize the actual connections in each figure. non-communications type use cases (e.g., scientific research, weather, Earth observation, etc. ), with an optional, but beneficial, support for high-bandwidth LEO-GSO links. To achieve this, the GSO layer may employ a sub-6 GHz frequency such as L-band, S-band, or C-band. Alternatively, (or potentially in combination with) a high-frequency band above 10 GHz, such as a Ka-band or Q/V-band, which could be re-used for both GSO-to-ground gateway feeder links, LEO-to-GSO relay and backhaul links, and user links to fixed and mobile VSAT terminals via the implementation of steerable or full-coverage multi-beam HTS/VHTS capability. The LEO layer shall support at least one sub-6 GHz frequency band, such as an L-, S-, or C-band, for user links, and at least one high-frequency band above 10 GHz, such as a Ku-, Ka-, or Q/V-band, for LEO-to-GSO backhaul and relay capability, as well as LEO gateway steerable feeder links. The LEO high-frequency steerable LEO-to-GSO and LEO-to-ground links could be designed to be sufficiently flexible to be re-pointed to serve as RF LEO-to-LEO inter-satellite links (ISLs) or, in the case of feeder links, as high throughput user links for select high-value mobile and fixed VSAT user terminals.
## 5 Methodology and Development
### Machine Learning Dataset Preprocessing
#### 5.3.1 Multilayer Perceptron Processing  
MLPs are feed-forward neural networks that consist of a large number of connected neurons divided into input units, output units, and hidden units. In general, MLP consists of a linear classifier able to sort the input data into two differentiated categories, by using a feature vector multiplied by some weights and added to a bias [46]. In this sense, the correct implementation of an MLP algorithm is performed by defining the following variables that will make up the underlying neural network:  
* Number and type of features;
* Preprocessing of the chosen data associated with the features;
* Architecture of the neural network;
* Number of hidden layers and input nodes;
* Activation function;
* Loss function;
* Data validation.  
The machines that process the algorithm use their graphics processing units (GPUs) and CPUs to increase its efficiency. The number and type of features and their preprocessing was addressed in Section 5.1. Additionally, in that same section, the architecture of the neural network was settled in 10 input values. The output was defined by a binary result, where 0 means no attack and 1 means attack. Between the input and output layer, two hidden layers were added. A hidden layer helps to represent different decisions that could not be directly related to the linear results of the features. The first layer helps to approximate functions that contain a continuous mapping from one finite space to another, and the second layer could represent an arbitrary decision boundary to arbitrary accuracy. For this reason, the neural network that is created has two hidden layers, this makes it capable of identifying arbitrary solutions.  
In addition, some other important parameters that the neural network must have are the activation function and the kernel initializer function or the optimizer. In this case, the activation function selected was ReLU; this decision was made following the previous work carried out by the authors and presented in Reference [18], where a comparative analysis of different activation functions for MLP is shown.  
Thus, the model proposed in this research was developed by using Keras (Alphabet Inc., Mountain View, CA, USA) and defined by four layers: one input layer, two hidden layers, and one output layer. The input layer was designed by including one node for each input feature. On the other hand, the number of hidden layers was determined using Equation (1), selected after evaluating the four previously proposed equations [18]. Furthermore, in every layer except in the output, the \"softmax\" function was used, as it facilitates the probability distribution among a different number of categories [47]. The kernel initializer function selected was the \"normal\" and the optimizer was implemented by an \"adam\" optimizer [48, 49]. EarlyStopping [50] was used in the proposed model in order to prevent the overfitting with a min_delta of \(10^{-3}\), which is the minimum value of the loss in order to determine an improvement of the number of epochs in the model.
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 4 Experiment
### Experiment Platform and Parameter Setting  
The computing hardware platform used in this article is Baidu AI Studio [27] from where we access free available computing resource. The hardware is configured with 16 GB memory, equipped with NVIDIA Tesla V100 graphics card. Additionally, we deploy the deep learning framework named PaddlePaddle to conduct all experiments in this paper. PaddlePaddle is an open-access industrial Python library that covers core deep learning frameworks, basic model libraries and end-to-end development kits in favor of both dynamic and static graphs. The version of CUDA is 10.1, and the version of cuDNN is 7.6. Lastly, in order to verify the practical performance, the proposed model is later transplanted to the embedded system device of NVIDIA Jetson Nano.  
As shown in Table 1, in the training section, the total number of the frames is 6400 which includes a total of 4775 frames with totally 11,205 target objects and 1625 frames without any object. All frames are shuffled before feeding into the network. Afterwards, we split the first 80% of them into a training set, and the last 20% into 1 evaluation set. Additionally, augmentation methods such as horizontal flipping and random rotation are used to create new frames and address the issue of bias for unbalanced number of samples in the two \"spot\" and \"background\" classes. Whereas, in the test section, the total number of the test images is 25,600, which includes a total of 44,550 space objects in the test section. There is no overlap between the training and test samples.  
To ensure the fairness of the experimental results, all initial algorithm parameters of the experiments are maintained the same. The training phase ran over 400 epochs and the learning rate for the Momentum optimizer is set to \(1.25\times 10^{-4}\) which remains fixed during the training phase. Additionally, the batch size of 8 is used to fit the model in the training phase.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 4 Discussion
### Post-Fire Energy Fluxes  
As shown in Figure 6, the temporal profiles of the NPP and GPP values in the control area illustrated that the NPP and GPP in mature boreal forest communities are 0.58 kg C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\)  
Figure 8: Annual seasonal profiles of the EVI, LST, LSWI and GPP for the control and focal pixels after normalization processing: (**a**) focal pixels; and (**b**) control pixels. Remote sensing indices (after normalization) were used to perform a comparative analysis of the annual changes from fire disturbances and seasonality.  
and 58 g C\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\), which are similar to the flux-tower determination results of 0.6 kg C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\)[52]. Post-fire succession significantly affected the canopy structure and LAI, reflecting an alteration of the energy balance and evapotranspiration and modifying the latent heat flux (LE). In this study, the GPP and NPP decreased by approximately 26-58 g C\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\) and 0.23-0.45 kg C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\), respectively, following a fire (Figure 6). Based on fire-regeneration studies, most tree seedlings are established during the 3-10 year period following a fire, while shrub regeneration occurs in 6-7 years [53]. However, the EVI was the result of shrub and ground vegetation growth in the early post-fire years (Figure 10). The GPP and NPP recovered faster during the first three years, with a second turning point during Years 6-7, which matches the vegetation succession process. The conceptual model showed that the NPP and GPP remained low during the early post-fire succession stage; the recovery peaked in the early and mid-succession stages and then decreased or remained steady at the end of the succession, when the vegetation reached maturity or an older stage. The NPP and GPP reached a steady state in the control area, but the NPP and GPP values in the focal areas continued to rise, even at the end of the entire 17-year study period. These results confirmed prior reports that post-fire GPP and NPP reach relatively steady states only after 20 years or longer. In reality, achieving a steady state could require 20-60 years, and the required time increases with increasing latitude and correspondingly colder temperatures [53], such as in the taiga boreal forest eco-zone. Without a longer dataset for these areas, we could not reliably predict when a steady state might be reached for the study area, but the NPP and GPP values were close to zero in the sixteenth year, suggesting that a steady state should be attained within a few more years. In addition, the NPP and GPP post-fire recovery trajectories were well illustrated by this method, which showed that the steady state period and degradation process could be inverted and were feasible beyond the limited local information from ground measurements.
## Appendix A General Solution to the Paraxial Equation (5)  
The integral (11) is the general solution to the paraxial Equation (5), as it was mentioned in Section 3. To prove this, let us consider without loss of generality the one-dimensional case, and then we shall proceed with the generalization to two dimensions. In such a case, Equation (5) can be recast as  
\[i\ \frac{\partial\psi(x;z)}{\partial z}=-\frac{1}{2k}\frac{\partial^{2}\psi(x; z)}{\partial x^{2}}. \tag{13}\]  
The solution to \(\psi(x;z)\) can be expressed as a linear combination of plane waves, as  
\[\psi(x;z)=\frac{1}{\sqrt{2\pi}}\int e^{ixx}\tilde{\psi}(\kappa;z)dx. \tag{14}\]  
If this ansatz is substituted into Equation (13), then we obtain the following simpler equation:  
\[i\ \frac{\partial\tilde{\psi}(x;z)}{\partial z}=-\frac{\kappa^{2}}{2k}\tilde{ \psi}(\kappa;z), \tag{15}\]  
which solution  
\[\tilde{\psi}(\kappa;z)=e^{-ix^{2}z/2k}\tilde{\psi}(\kappa;0). \tag{16}\]  
Now, this latter expression is substituted into the integrand of the ansatz (14), rendering  
\[\psi(x;z)=\frac{1}{\sqrt{2\pi}}\int e^{ixx-i\kappa^{2}z/2k}\tilde{\psi}(\kappa ;0)dx. \tag{17}\]Since the amplitude \(\psi(x;0)\) at \(z=0\) is assumed to be known, \(\tilde{\psi}(\kappa;0)\) will also be known according to the inverse of the transformation (A2) for \(z=0\):  
\[\tilde{\psi}(\kappa;0)=\frac{1}{\sqrt{2\pi}}\int e^{-i\kappa x}\,\psi(x;0)dx.\] (A6)  
Substituting this expression into (A5) renders  
\[\psi(x;z)=\frac{1}{2\pi}\int e^{i\kappa(x-x^{\prime})-i\kappa^{2}z/2k}\psi(x^{ \prime};0)dxdx^{\prime}.\] (A7)  
The integral over \(\kappa\) can be easily done to yield  
\[\psi(x;z)=\frac{e^{-i\pi/4}}{\sqrt{\lambda z}}\int e^{ik(x-x^{\prime})/2z}\, \psi(x^{\prime};0)dx^{\prime},\] (A8)  
which accounts for the transverse propagation of the amplitude \(\psi\) as a function of the (longitudinal) \(z\) coordinate.  
The generalization of the integral (A8) to two dimensions (the case considered in Section 3) is straightforward. Given there is no correlation between the \(x\) and \(y\) coordinates, the full solution is just the direct product of (A8) evaluated for \(x\) and \(y\), respectively:  
\[\psi(\mathbf{r}_{\perp};z)=\frac{1}{i\lambda z}\int e^{ik\|\mathbf{r}_{\perp} -\mathbf{r}_{\perp}^{\prime}\|^{2}/2z}\,\psi(\mathbf{r}_{\perp}^{\prime};0)d \mathbf{r}_{\perp}^{\prime}.\] (A9)  
As for the full solution, \(\Psi(\mathbf{r}_{\perp};z)\), it can be readily obtained for the latter by including the corresponding translational exponential factor:  
\[\Psi(\mathbf{r})=\psi(\mathbf{r}_{\perp};z)e^{ikz}=\frac{e^{ikz}}{i\lambda z} \int e^{ik\|\mathbf{r}_{\perp}-\mathbf{r}_{\perp}^{\prime}\|^{2}/2z}\,\psi( \mathbf{r}_{\perp}^{\prime};0)d\mathbf{r}_{\perp}^{\prime}.\] (A10)
## 3 Metrics for Jamming Attack Detection
### Signal-to-Noise Ratio (SNR) or Signal-to Jammer Power Ratio (SJR)  
Although there is a subtle difference between SNR and SJR, we have considered these to be the same because, in our model jammer is the predominant noise source, and have used these terms interchangeably. SNR is calculated as the ratio of the received signal power at a node to the received noise power (or jammer power) at the node. It is almost an effective metric to identify a jamming attack at the physical layer as there can be no jamming at the physical layer without the SNR dropping low. However, some other metrics like PDR, BPR, or BER which can identify a data-link/MAC layer attack should be used with SNR for making it almost full- proof to detect jamming.
# Cycle-to-Cycle Variation of a Diesel Engine Fueled with Fischer-Tropsch Fuel Synthesized from Coal
###### Abstract  
Cycle-to-cycle variations during the combustion phase should be comprehensively investigated because these variations are among the most serious causes of higher emissions and lower efficiency. The main objective of this study was to evaluate the relationship between cyclic variations and combustion parameters. The combustion and cyclic variation characteristics were investigated using a diesel engine operating on Fischer-Tropsch (F-T) fuel synthesized from coal. Experiments were conducted under full load conditions at three engine speeds of 1200, 2000, and 2800 rpm. The results revealed that cyclic variations of F-T diesel were lower than those of 0# diesel, acquired the minimum value at the speed of 2000 rpm, and reached the maximum at the speed of 2800 rpm. The mean fluctuation intensity of F-T diesel was 0.185, 0.189, and 0.205 at speeds of 1200, 2000, and 2800 rpm, respectively, smaller than that of 0# diesel under the corresponding conditions. The relationships between cyclic variations and combustion parameters were analyzed by correlation methods. Maximum in-cylinder pressure (P\({}_{\text{max}}\)) increased linearly with increased ignition delay, while it decreased linearly with increased combustion duration. The Pearson's correlations between P\({}_{\text{max}}\) and ignition delay were 0.75, 0.78, and 0.73; however, the corresponding values between P\({}_{\text{max}}\) and combustion duration were 0.61, 067, and 0.65 when fueled with F-T diesel at speeds of 1200, 2000, and 2800 rpm, respectively. Moreover, the Pearson's correlations of 0# diesel were higher than those of F-T diesel at the same operating loads. Compared with combustion duration, the ignition delay had more important effects on cyclic variations with a higher Pearson's correlation. Furthermore, the ignition delay significantly influenced cyclic variation under a high speed load, while the combustion duration had a marked effect under low speed conditions. Overall, the results revealed the importance of combustion parameters on cyclic variation, which has great significance for controlled cyclic variation in diesel engines.  
cycle-to-cycle variation; fluctuation intensity; diesel engine; combustion parameters; F-T diesel +
Footnote †: journal: Applied Soft. 2019, 9, 2032; doi:10.3390/APP9102032