esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Feasibility of PAZ as an Opportunity Illuminator in a Passive Bistatic Radar System
### OI Signal Waveform  
Most of the detection capabilities of a passive radar system depend on the OI signal, in the same way that the dedicated transmitter determines the characteristics of active radars.  
Figure 14: PAZ imaging models [8]: ScanSAR (**a**); stripmap (**b**); spotlight (**c**).  
Bistatic range resolution depends on signal bandwidth and system geometry, as shown by expression Equation (3), so other criteria related to system geometry must be studied before facing the calculus of the bistatic range resolution.  
For the waveform analysis, a synthetic signal was generated with the following parameters:  
* Pulse repetition frequency, PRF = 3.03886 kHz
* Duty cycle, \(\tau=19\%\).
* Pulse duration, T = 62.523 \(\upmu\)s.
* Modulation factor, \(k_{r}=2.0383\cdot 10^{12}\) Hz/s
* Bandwidth, \(\Delta B=127.44\) MHz
* Number of Linear Frequency Modulation (LFM) pulses, 50.  
The ambiguity function is a powerful analysis tool for determining the detection capabilities of a waveform. It can be calculated using Equation (1), after substituting \(s_{SURV}[n]\) by \(s_{REF}[n]\).  
The AF of the synthetic signal is depicted in Figures 15 and 16. The ambiguity peak pattern predicted in [34] can be clearly identified:  
* Periodic ambiguity peaks appear along the time delay dimension, with a period equal to \(\frac{1}{PRF}\).
* Periodic ambiguity peaks appear along the Doppler shift dimension, with a period equal to \(PRF\).  
These undesired peaks can mask desired targets or increase the probability of false alarms. To avoid these effects, radar coverage and desired target dynamics must be limited to guarantee the operation in the unambiguous region of the AF: time delay lower than \(\tau=\frac{1}{PRF}=0.32908\) ms and Doppler shift lower than \(\
u=PRF=3.03886\) kHz.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 4 ITGA Design
### Generating Initial Population  
Firstly, the initial population size for the ITGA is set to \(n\). Secondly, for tasks with conflicting data transmission times, a heuristic rule-based conflict resolution method is applied to adjust the transmission schedules, ensuring they become executable for data transmission. Subsequently, several chromosomes are randomly generated, and heuristic rules are used to allocate ground station hardware resources, aiming to improve the fitness values of these chromosomes. Finally, based on their fitness values, the top \(n\) optimal chromosomes are selected to form the initial population.  
The heuristic rule-based ground station hardware resource allocation method is as follows:  
1. Firstly, based on the equipment parameters required for the task, filter out equipment that does not meet the constraint conditions according to Formulas (4)-(9). Secondly, allocate the remaining available equipment to the current task. Finally, repeat the above operations until all task equipment is allocated.
2. During the allocation process, if there is a conflict in equipment usage and no equipment of the same type is available, the following allocation strategy applies: if the priority task that has already been allocated equipment has a longer effective data  
Figure 5: Chromosome: the number indicates the task number, and the location sequence indicates the execution sequence.  
transmission duration than subsequent tasks, it will not be preempted by those subsequent tasks with conflicting equipment requirements.
3. If a data transmission task does not have any time conflicts with other data transmission tasks, it will be directly allocated the most optimal equipment.
4. When multiple devices of the same type on the same side of the switch matrix are capable of serving the same task and are not occupied by other tasks, the device with the fewest allocated tasks will be selected to serve that task, and the allocation plans for the other devices will not be considered.  
The flow chart of the initial population generation is shown in Figure 6.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
###### Abstract  
A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via \(\ell_{1}\) minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.  
GNSS; GPS; compressed Sensing; sparse approximation +
Footnote †: journal: Sensors
# Evaluation of Lower Extremities Power, Movement, Position and Effectiveness in Volleyball
###### Abstract  
Background: This paper presents research on the comparison and evaluation of lower limb power, movement, position on the court, and effectiveness in the players of men's volleyball. The study involved professional players in the first division of a men's volleyball team. The main aim of this paper was to show the correlation between lower limb power, movement, position on the pitch, and volleyball efficiency. Methods: The authors presented basic somatic characteristics of anthropology metrics, advanced research results obtained with the Optojump measuring system, players' results statistics obtained with the Data Volley software (v.4.2024.01), and by applying scientific statistical methods and graphics interpretation. Results: The analyses with the Kruskal-Wallis test showed statistically significant differences in all the variables studied. Flight time (TF, H = 122.967; \(p\) < 0.001.) was the shortest for the players in the libero position, then the setter, attacker, middle and longest flight time was shown by the receiving players. The height of the jumps (JH, H = 123.018; \(p\) < 0.001) was the highest for receiving and mid players, and then attackers and testers, and the lowest jumps were recorded by libero players. The highest power of lower limbs (P, H = 20.392; \(p\) < 0.001) was characteristic for receivers, setters, mid-players and, curiously, libero achieved a better result than attackers. Conclusions: The analysis shows that the power of the lower extremities in volleyball players is associated with the function they perform on the court.  
Volleyball; players; factors; power; lower limbs; effectiveness Article
# A Review of the Impacts of Implant Stiffness on Fracture Healing
## 5 Issues in the Clinical Setting  
In actual clinical practice, IFM cannot be verified in each individual case, so the method that is assumed to optimize IFM is selected. Surgical factors linked to increased fusion failure rates align with the growing body of literature indicating that excessively rigid implants can impede fracture fusion in distal femur fractures [37]. Various factors could relate to non-union, such as different metal materials, plate length, and other variables affecting bone fusion post-locking plate fixation. The prevalence of non-union associated with this procedure may vary due to factors like metal material variations, plate length, and other influencing factors. While the optimal surgical setup remains unclear, controlling factors like metal material and plate thickness to prevent overly rigid fixation and ensure optimal stability could help reduce non-union rates in managing distal femoral fractures.  
Surgeons must navigate the delicate balance between preventing excessively rigid fixation constructs and allowing callus formation without compromising fixation stability. The TiNSbSn alloy is a high-performance metal known for its exceptional combination of high strength and low Young's modulus, qualities that have led to its clinical use in femoral stems [61; 62]. It is anticipated that by manipulating variables other than Young's modulus, such as the thickness and length of plates, it will be possible to engineer materials for fracture treatment that possess the ideal stiffness. This approach suggests a tailored strategy for material development, aiming to optimize the biomechanical environment for fracture healing. Further research is necessary to determine the ideal balance of intraoperative variables. Currently, only animal studies are approved to address these concerns, and the clinical application of novel materials is anticipated. Future studies should ideally employ randomized controlled trials to minimize confounding factors. If not possible, it is crucial to analyze homogeneous data adequately powered for analysis purposes to address surgical cohort analysis effectively.
## 4 Application and Discussion of the Methodology
### Application of FFTA
#### 4.2.2 Domain Expert Evaluations  
The evaluation effect of experts is crucial to scientific conclusions. It is generally preferred to select a heterogeneous group with different experiences and various viewpoints as the composition of the expert group. In the present study, a heterogeneous group of experts was selected to calculate the impact rate of BEs in EMAs, and 11 power systems were employed to judge the HOFs. Refer to the expert weight scores listed in Table 1 and Equation (6) to calculate the expert's weighting coefficient, and the results are shown in Table 4.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 4 Discussion  
### Successional Patterns of Root-Tip P Mobilizing Enzymes  
We predicted that as soil P is transformed by the biota during primary succession to progressively more recalctrant organic forms, activities of P-mobilizing enzymes on _Alnus_ root tips would shift  
Figure 4: Spider plot of ectomycorrhizal fungi (EMF) community composition derived from non-metric dimensional scaling ordination and PERMANOVA comparisons (_pairwiseAdonis_ package) based on a dissimilarity matrix of species abundance data which showed that alder root-tip communities were significantly different from both white spruce (\(p<0.05\)) and Salicaceae (\(p<0.10\)). Species clusters identify the centroid (spatial mean) of data points representing combinations of successional stage and horizon for each host.  
Figure 5: Comparison of mean \(\pm\) S.E. activities of acid phosphatase, phosphodiesterase, and phytase of alder root tips dominated by _Tomentella_ sp. 6 versus _Alnicola luteolofibrillosa_ 2 averaged across horizons and stages. Significant differences between species are indicated above bars at \(p<0.05\) (**), and \(0.01\) (***). Marginal (\(R^{2}{}_{m}\)) and conditional (\(R^{2}{}_{c}\)) values generated for linear mixed effects models evaluate variance explained by fixed and fixed + random effects, respectively (see Statistics section above).  
towards increases in phosphodiesterase and phytase [41]. We only found partial support for this prediction in that _Almus_ root tips showed a progressive increase in acid phosphatase (100 uM) and phytase activity across stands. However, phosphodiesterase when measured at low and high substrate concentrations was highest in the early-successful stand, in contrast to the predicted pattern (Figure 1). Recent \({}^{31}\)P-NMR data ([PERSON]) indicate that while the amount of soil organic P increases during succession on an area basis as soil organic C accumulates, concentrations of organic P, and proportions of the dominant forms of inorganic and organic P within organic horizons are actually quite similar across these floodplain successional stages. Thus, the lack of a significant increase in enzymes specifically targeting organic forms of P may not necessarily be unexpected. The fact that rates of P-mobilizing enzymes were positively intercorrelated across all root tips sampled suggests that ectomycorrhizae are using a coordinated suite of enzymes to meet P demands, or perhaps simply that healthier root tips/mycorrhizae have more intact enzymes. Within our early successional stands, organic horizons are poorly developed, with an approximate thickness of 1 cm. Nevertheless, there is a high root density of early successional woody species in surface soils [42], and likely a rapid turnover of organic P from decaying microbes and plant tissues, which may explain why all three enzymes appear to be up-regulated in a coordinated manner to meet plant P demands.
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 2 Related Work
[22] can better measure the outlier of data through the true positive (TP) and false positive (FP) of the known outlier information. However, the existing methods are not suitable for the large amount of data generated recurrently in the actual production process of the manufacturing industry. At the same time, data timeliness evaluation has not been applied in the manufacturing industry, especially in industrial production process monitoring. To sum up, predecessors have achieved a lot of work on data timeliness. These works include putting forward a general timeliness evaluation model and applying it to many practical scenarios. However, when these methods are directly applied to the manufacturing industry, their applicability will be hindered. First of all, these methods do not consider the time series generated by multiple production lines and products in the manufacturing industry. Secondly, these methods simply input the time of data generation into the model as a parameter. The relative stability of the data generated in the manufacturing industry is not considered. Finally, the timeliness evaluation of data has not been applied in the field of anomaly monitoring.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 4 Problem Formulation
### Average AoI of the Proposed Model  
**Lemma 1**.: _The average AoI observed at the FC is:_  
\[\overline{\Delta}(N)=\frac{3H_{N}}{2v}+\frac{G_{N}}{2 vH_{N}}, \tag{7}\]  
_where \(H_{N}=\sum_{l=1}^{N}\frac{1}{l}\) and \(G_{N}=\sum_{l=1}^{N}\frac{1}{l^{2}}\)._  
Proof of Lemma 1.: Recall that the general expression on average AoI we derive in Equation (4) requires us to figure out \(\mathbb{E}[R_{N}]\) and \(\mathbb{E}[R_{N}^{2}]\). And the transmitting time from sensor nodes to the FC obeys exponential distribution with mean \(1/v\) and is i.i.d. across the sensor nodes. In terms of order statistic [26], the mathematical expectation and variance of the time difference denoted as \(\mathbb{D}[\cdot]\) between the first arriving observation and the start point is given by:  
\[\mathbb{E}[R_{1}-R_{0}]=\frac{1}{Nv},\quad\mathbb{D}[R_{1}-R_{0}]=\frac{1}{N^{ 2}v^{2}}, \tag{8}\]  
as we treat this observation as the first arriving observation among \(N\) sensors. As we set \(\mathbb{E}[R_{0}]\) and \(\mathbb{D}[R_{0}]\) to 0, \(\mathbb{E}[R_{1}]\) and \(\mathbb{D}[R_{1}]\) can be calculated as:  
\[\mathbb{E}[R_{1}]=\frac{1}{Nv},\quad\mathbb{D}[R_{1}]=\frac{1}{N^{2}v^{2}}. \tag{9}\]  
The mathematical expectation and variance of the time difference between the second and first observation is:  
\[\mathbb{E}[R_{2}-R_{1}]=\frac{1}{(N-1)v},\quad\mathbb{D}[R_{2}-R_{1}]=\frac{1 }{(N-1)^{2}v^{2}}, \tag{10}\]  
as we treat this observation as the first arriving observation among the remaining \(N-1\) sensors. Take Equation (9) into Equation (10), \(\mathbb{E}[R_{2}]\) and \(\mathbb{D}[R_{2}]\) can be figured out as:  
\[\mathbb{E}[R_{2}] =\frac{1}{Nv}+\frac{1}{(N-1)v}=\frac{H_{N}-H_{N-2}}{v},\] \[\mathbb{D}[R_{2}] =\frac{1}{N^{2}v^{2}}+\frac{1}{(N-1)^{2}v^{2}}=\frac{G_{N}-G_{N-2 }}{v^{2}}.\]  
By using Mathematical induction, the \(N\)-th arriving observation is given by:  
\[\mathbb{E}[R_{N}]=\frac{H_{N}}{v},\quad\mathbb{D}[R_{N}]=\frac{G_{N}}{v^{2}}. \tag{11}\]  
Note that \(\mathbb{E}[R_{N}^{2}]\) can be expressed as  
\[\mathbb{E}[R_{N}^{2}]=\mathbb{E}^{2}[R_{N}]+\mathbb{D}[R_{N}]. \tag{12}\]By taking Equations (11) and (12) into Equation (4), the proof is completed.
# 6G Network Architecture Using FSO-PDM/PV-OCDMA System with Weather Performance Analysis
## 5 Simulation Results  
The proposed FSO-PDM/PV-OCDMA system is evaluated and simulated using Matlab and Optisystem software version 18 with the parameters mentioned in Table 2[27; 28; 29; 30; 31]. Optisystem is a commercial tool that offers designing, simulation, and optimization of optical components, links, systems, and networks. By using Optisystem software, the product time introduction to the market can be shortened and product quality can be improved.  
In this section, the simulation results presented were obtained from simulating our suggested FSO-PDM/PV-OCDMA system. Effects of diverse weather conditions on our proposed system performance are discussed in terms of maximum FSO range, log (BER), Q-factors, and eye diagrams. The conducted results are divided according to different weather conditions into the following parts:
## 3 Pharmacological Properties of GP
### Hepatoprotective Effects
#### 3.6.1 CCl4-Induced Liver Injury  
Researchers reported that GPP possessed a protective effect against hepatic injury in rats caused by carbon tetrachloride (CCI4). GPP (40 and 80 g/kg for 30 days, i.g.) could stabilize the liver cell membrane, effectively prevent the leakage of substance in cytoplasm, and reduce the level of serum transaminase. In addition, the up-regulated expression of iNOS mRNA in liver tissues of a rat model, which led to the production of nitric oxide (NO) and further induced liver injury, was attenuated following GPP treatment [103]. CCl4 was metabolized by cytochrome P-450 in the liver endoplasmic reticulum to produce a free radical that could lead to lipid peroxidation, liver damage, and necrosis. GPS exhibited a potent protective effect on CCl4-induced acute and chronic liver damage, which was attributed to its free radical scavenging capacity [104; 105]. In addition, GPS was reported to also possess anti-fibrotic potential on CCl4-induced liver fibrosis in vivo and vitro studies, which supported its clinical use in alleviating the progression of hepatic fibrosis. GPS could improve the collagenolytic activity, decrease the deposition of collagen in the liver, promote the proliferation of liver cells, and thus suppress the onset and enhance the recovery of liver fibrosis induced by CCl4 in rats [104]. [PERSON] et al. [106] investigated the effects and mechanisms of GPS on ameliorating liver fibrosis induced by CCl4 and found that the transforming growth factor-\(\beta\) (TGF-\(\beta\)) signaling was inhibited by treatment of GPS, consequently suppressing the proliferation of hepatic progenitor cells and inhibiting their differentiation into myofibroblasts.
# Optimal Transport in Multilayer Networks for Traffic Flow Optimization
## 2 Materials and Methods
### The Algorithmic Implementation  
The numerical implementation consists of initializing the \(\mu_{e}>0\) at random. Then, one iterates between (i) extracting the pressure potentials (or the fluxes) using Equations (1) and (2), and (ii) using these to recompute the \(\mu_{e}\) by means of Equation (3), which can be solved numerically with finite difference discretization. The iteration is repeated until convergence. In our experiments, we terminate a run of the algorithm when the difference \(J_{\beta}^{(t+1)}-J_{\beta}^{(t)}\) between two successive updates is lower than a threshold (the superscript \((t)\) is the iteration step). The cost \(J_{\beta}\) in Equation (4) is not strictly convex in general, hence the solution of Algorithm 1 may converge to a local optima. One should then run the algorithm several times, each time initializing to a different random initial realization of \(\mu_{e}>0\). A possible choice for a final optimal solution is the one that has lower \(J_{\beta}\). We give the pseudocode for this in Algorithm 1; this is complemented with the block diagram in Figure 3. Most of the computational effort required by Algorithm 1 is in the solution of \(M\) linear systems as in Equation (2). In our implementation, this is performed by a sparse direct solver (UMFPACK), performing a LU decomposition of each column of the right hand side of Equation (2), and having complexity scaling as \(\mathcal{O}(M\,N^{2})\).  
Figure 3: Block diagram of Algorithm 1. We give a pictorial representation of the pseudocode in Algorithm 1. Here, rectangular blocks are _action_ blocks, corresponding to the update of a variable, to an input initialization, or to the output of the fluxes at convergence. _Conditional_ blocks are diamond-shaped; elliptical blocks denote the _start_ and _stop_ points.  
Figure 2: Example of optimal paths. We show an example of optimal paths obtained with: \(p=0.2\) and (**top**) \(w_{1}=0.2\), (**bottom**) \(w_{1}=0.8\). Values of \(\beta_{1},\beta_{2}\) are those reported on top of each network. The statistics \(Gini_{1}\) and \(f_{2}\) are those defined in Section 3.1. The width of edges is proportional to the optimal \(||F_{\varepsilon}||_{2}\). Blue and red edges are for layers 1 and 2, respectively. The two layers are plotted individually on the rightmost column.  
The resulting \(\{F_{e}\}\) capture how passengers travel along the network via optimal trajectories. The norms \(||F_{e}||_{2}\) measure the total number of passengers along an edge \(e\).