esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Muscle Synergies in Sports So Far
### Muscle Synergy Analysis to Prevent Sports Injury  
Preventing sports injury is a major issue in professional sports. In the literature, it emerged that female athletes suffer a greater number of specific sports-related musculoskeletal injuries than male athletes [12]. For instance, in female basketball players performing the 45\({}^{\circ}\) side-step cutting task, the risks of non-contact anterior cruciate ligament injury are significantly higher than those of male basketball athletes, because female athletes exhibit lower hamstring activation and higher quadriceps activation, and this concurrent effect causes an increased risk of anterior cruciate ligament injury [13]. These injuries can result in reduced athletic performance and may also cause additional injuries in other areas of the body. Therefore, identifying posture control strategies and analyzing muscle synergies recruited during females' movements could help to decrease the potential risk of injuries [13; 14]. Indeed, [PERSON] et al., comparing elite hockey athletes and non-athletes, identified, in the elite athletes, specific muscle synergies exploited to restore balance against unexpected external perturbation, which could be used as an instrument to develop a training strategy for female athletes requiring high postural stability to lower their risk of injuries [14].  
Another factor that may increase the risk of injury is fatigue, as it leads to impairments in functionality and can alter the kinematics of movements. These changes in kinematics could derive from adaptations occurring at the neuromuscular level [4]. To better understand how the central nervous system (CNS) develops neuromuscular strategies in response to fatigue, [PERSON] et al. applied an external rotation (ER) fatigue protocol on competitive baseball players to mimic the fatigue during repetitive overhead throwing. This study aimed to determine the effects of isolated ER fatigue on the adaptation of muscle synergies in the shoulders of baseball players in order to help clinicians develop injury prevention plans limiting fatigue to decrease overuse-related injury rates. This study discovered a reduction in the variance accounted for (VAF) in ER movements and abduction movements, and a change in muscular weights before and after fatigue. These changes may be evidence that the CNS simultaneously seeks a compensatory response to continue the desired movement, maintaining muscle force output, but at the same time, tries to reduce the risk of injury [4]. Therefore, it is needed to delay or avoid the ER fatigue that induces neuromuscular changes to reduce the risk of injury. To achieve this purpose, the optimal treatment strategy is muscular endurance training of the external rotators, as [PERSON] et al. demonstrated that high school-aged baseball players increased their posterior shoulder endurance by completing a 20-week training program [15].  
Thus, in general, the differences found between muscle synergies in several conditions or subjects could be a signal for understanding how to intervene in order to prevent/reduce the risk of injury.
## 2 Vehicle-Road Intelligent Capability Allocation Model
### Model Framework
#### 2.2.1 Quantified Assessment Sub-Model of Perception Scheme
Performance Comparison of Various Perception Schemes  
A comparative analysis of the perception capabilities of 12 schemes across two technical routes--vehicle intelligence and collaborative intelligence--is presented, as depicted in Figure 5. The vehicle intelligence encompasses 3 vehicle-based perception schemes (AVP, AVI, and AVA), while the collaborative intelligence includes 3 vehicle perception schemes (ICVP, ICVI, and ICVA) and 3 roadside perception schemes (IRP, IRI, and IRA), with each pair combining in various ways, resulting in a total of 9 distinct schemes. Under the vehicle intelligence technical route, higher-level intelligent vehicles expand their perception range and enhance perception reliability by adding redundant perception devices around the vehicle. However, vehicle-based perception is prone to obstruction by surrounding vehicles and other obstacles, resulting in significant blind spots and poor perception stability. Consequently, the method of enhancing vehicle perception performance by adding redundant sensors to the vehicle has limited effectiveness. This is precisely the reason why it is challenging to achieve higher levels of autonomous driving within the vehicle intelligence technical route. Under the collaborative intelligence technical route, the deployment of roadside perception devices can achieve vehicle-road collaborative perception, significantly enhancing perception performance. This is especially true for the reliability and stability requirementsof advanced autonomous driving, where the advantages of vehicle-road collaborative perception are particularly pronounced. It is noteworthy that, with the completion of advanced perception deployment on the roadside, the improvement of vehicle perception configurations has a minimal impact on the final comprehensive perception performance and reliability. The perception performance and reliability of the ICVA-IRA scheme, which combines primary vehicle perception with advanced roadside perception, are far superior to the AVA perception schemes of vehicles.
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
## 5 Factors Affecting Fermentation Process of Pharmaceutical Terpenoids
### Nitrogen Source  
_S. cerevisiae_ is able to utilize different kinds of nitrogen containing compounds in their natural habitat, such as amino acids, ammonium ions (NH4+), peptides, and urea. When these compounds are present in the growth medium, they are transported into the cells by permeases and used as building blocks in biochemical pathways or catabolized into glutamate and ammonium for nitrogen metabolism [91]. The type and concentration of nitrogen sources affect the growth and gene expression in this yeast. _S. cerevisiae_ uses preferentially good nitrogen sources, such as ammonia, glutamine, and asparagine, over poor ones, such as proline and urea. Growth rate of this yeast is relatively high in good nitrogen sources rather than in poor nitrogen sources [92]. The accumulation of yeast biomass strongly depends on the nitrogen content in the growth medium, since it was reported that the proportion of nitrogenous compounds in yeast cells is about 50% (by weight) [93]. The most used nitrogen sources for terpenoid production are ammonium sulfate ((NH4)2 SO4), ammonium dihydrogen phosphate ((NH4)H2 PO4), ammonium chloride (NH4 Cl), yeast extract, peptone, tryptone, and sometimes combination of those (Table 3). Higher terpene concentrations were achieved when (NH4)2 SO4 was used as nitrogen source (Table 3). On the other hand, nitrogen limited resting cell fermentation enhanced the titer of betulunic acid (from 57 to 182 mg/L) and total triterpen concentration (from 319 to 854 mg/L) [75]. In this study, the NH4 Cl concentration was decreased from 2.8 g/L to 0.939 g/L to allow nitrogen starvation. It was considered that restriction of biomass synthesis by nitrogen limitation might support the flux into terpenoid accumulation, since terpenoid synthesis has to compete for carbon, energy, and redox cofactors throughout yeast growth [94]. However, under this condition, cessation of growth and product formation, and lower specific productivity were observed in extended fermentations [75]. This indicates decrease in cell viability due to accumulation of toxic intermediates, or reactive oxygen species, which was also reported in the production of farnesene and artemisinin acid [27; 95]. Deficiency of nitrogen content in prolonged fermentations can also result in sluggish or stuck fermentation. However, high nitrogen concentrations also carry risks to the process, once it may oversimulate yeast reproduction and increase biomass levels too fast, resulting in shortage of other yeast nutrients, increased fermentation temperatures and, eventually, causing stuck fermentations and productivity losses. Thus, prolonging the release of nitrogen sources under tight control during the fermentation process can be a good strategy to extend the fermentation period, without heat peaks and productivity breaks [96; 97].
# Subpath Queries on Compressed Graphs: A Survey
## 3 Indexing Labeled Graphs and Regular Languages
### p-Sortable Automata
#### 3.8.1 Subpath Queries  
Subpath queries can be answered on \(p\)-sortable automata by generalizing the forward search algorithm of Wheeler graphs. In fact, the following property holds: for any pattern \(\Pi\), the states reached by a path labeled with \(\Pi\) always form one convex set in the partial order. In turn, any convex set can be expressed as \(p\) intervals, one contained in each class of the chain partition [89] (for any chain partition). The generalized forward search algorithm works as follows: start with the interval of \(e\) on the \(p\) chains (the full interval on each chain). For each character \(a\) of the pattern, follow the edges labeled with \(a\) that depart from the current (at most) \(p\) intervals. By the above property, the target nodes will still be contained in (at most) \(p\) intervals on the chains. Consider the example of Figure 12. In order to find all nodes reached by pattern 'aa', the generalized forward search algorithm first finds the nodes reached by 'a': those are nodes \(q_{1}\) (a range on the yellow chain) and \(q_{2}\) (a range on the red chain). Finally, the algorithm follows all edges labeled 'a' from those ranges, thereby finding all nodes reached by 'aa': nodes (again) \(q_{1}\) and \(q_{2}\) which, as seen above, form one range on the yellow chain and one on the red chain. See [89] for a slightly more involved example. Using up-to-date data structures, the search algorithm can be implemented to run in \(O(|\Pi|\cdot\log(\sigma p)\cdot p^{2})\) steps [89]. In the worst case (\(p=n\)), this essentially matches the lower bound of [PERSON] et al. [52; 53; 54] on dense graphs (see Section 3.2). On the other hand, for small values of \(p\) this running time can be significantly smaller than the lower bound: in the extreme case, \(p=1\), and we obtain the familiar class of Wheeler graphs (supporting optimal-time subpath queries).  
As far as locate queries are concerned, the sampling strategy described by [PERSON] et al. [70] Section 5.1 and [PERSON] [49] (see Section 3.5.4 for more details) can be directly applied to the DFS spanning forest of any \(p\)-sortable graph (similarly to Wheeler graphs), thus yielding locate queries in polylogarithmic time and small additional redundancy. This is possible since the index for \(p\)-sortable graphs described in Reference [89] supports navigation primitives, as well, and can thus be used to navigate the DFS spanning forest of the graph.
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
## 7 Conclusions  
In this paper, a modification of the alphabet of the symbols transmitted by the source is proposed. In particular, a zero-energy symbol is mapped directly into the constellation. This makes the zero symbol part of the information sent to the receiver, without any need for additional control bits or strategies. The map criterion, the probability of error, the entropy and the capacity of the proposed zero-energy mapping are derived for binary signalling case (Z-BPSK) and compared to the classic binary signalling (BPSK). The results show that the proposed concept performs as the traditional modulation, while saving energy, in particular when the probability of zero-symbol emission is significant, i.e., when the source experiences many silent periods.  
The proposed solution would be useful to save energy and overhead in all the contexts where bursty traffic of data is produced, like in voice or browsing services. The results show that our solution is able to save significant amount of energy with the same probability of error of a traditional system in the case of bursty traffic.
# Retractable Wireless Charging Windings for Inspection Robots
## 3 Design and Simulation of Retractable Wireless Power Transfer Coils
### Simulation Methodology  
A series of simulations were conducted using IPTVisual to evaluate the electromagnetic performance of different winding geometries in the proposed retractable WPT system. IPTVisual is a specialized software tool developed for efficient modeling and visualization of spatial energy flows in arbitrary-shaped inductive power transfer (IPT) coils [21].  
IPTVisual was selected for its ability to simulate mid-range WPT systems with complex coil geometries using a lightweight, script-based approach. It supports arbitrary winding shapes by allowing users to define trajectories through key spatial coordinates. These coordinates were discretized into segments matching the wire diameter to enable high-resolution modeling of inductive and resistive effects. This segmentation approach provides sufficient accuracy in the calculation of self- and mutual inductances using [PERSON]'s formula, with performance validated against analytical and FEM-based methods.  
In the simulation setup, each winding was treated as a discrete circuit element, which could be driven by a constant voltage. Compensation capacitors were included to enable resonance at a single operational frequency. Parametric modeling was carried out across a range of winding geometries configurations with the corresponding coil parameters such as number of turns, turn radius, and spacing systematically varied.  
The solver engine in IPTVisual employs complex impedance matrix formulations to solve for circuit voltages and currents across all nodes. Both amplitude and phase information are retained, which allows the accurate computation of real and reactive power, as well as visualization of the energy flow using Poynting vectors. These energy flow vectors were sampled across a three-dimensional spatial grid and rendered using WebGL to identify high-coupling regions, losses, and the influence of misalignment.
## 3 The Algorithm of the Initial Design
### Computational Parameters
#### 3.3.5 Computation of Synchronous Reluctance in Each Winding Turn (Initial Synchronous Reluctance)  
To calculate initial synchronous reluctance, the following steps must be taken:  
Step one: in this step, the reluctance observed across the winding must be obtained. For this purpose, the magnetic equivalent circuit given in Figure 7 must be used. The reluctance observed across the winding is represented by \(R_{t}\).  
The value for each definite reluctance of Figure 7 is computable using Equations (12)-(20):  
\[R_{fe11}=\frac{b_{u}+5l_{p}}{\mu_{0}\mu_{rf}c_{l}p_{b}b_{p}} \tag{12}\]  
\[R_{fe12}=\frac{b_{u}+2.5l_{p}}{\mu_{0}\mu_{rf}c_{l}p_{b}b_{p}} \tag{13}\]  
\[R_{fe21}=\frac{3.5l_{p}}{2\mu_{0}\mu_{rf}c_{l}p_{b}b_{p}} \tag{14}\]  
\[R_{fe22}=\frac{0.5l_{p}}{2\mu_{0}\mu_{rf}c_{l}p_{b}b_{p}} \tag{15}\]  
\[R_{g1}=\frac{g}{k_{le}\mu_{0}l_{p}b_{p}} \tag{16}\]  
\[R_{g2}=\frac{g}{2k_{le}\mu_{0}l_{p}b_{p}} \tag{17}\]  
\[R_{g12}=\frac{b_{u}}{k_{le}\mu_{0}(2g+1 pm)b_{p}} \tag{18}\]  
Figure 7: The equivalent magnetic circuit related to the winding of each pole.  
\[R_{pm1}=\frac{l_{pm}}{\mu_{rpm}\mu_{0}l_{p}b_{p}} \tag{19}\]  
\[R_{pm2}=\frac{l_{pm}}{2\mu_{rpm}\mu_{0}l_{p}b_{p}} \tag{20}\]  
Step two: in this step, the initial inductance of the winding, and the initial synchronous reactance, can be calculated using the obtained reluctance in the previous step, through Equations (21) and (22):  
\[L_{t1}=\frac{1}{R_{t}} \tag{21}\]  
\[X_{s1}=2\pi fL_{t1} \tag{22}\]  
3.6 Computing Wire Diameter and the Resistance in Each Winding Turn (the Initial Resistance) of the Winding \(R_{a1}\)  
Firstly, considering the current density of the copper conductor, the cross-sectional area of the wire is computed by Equation (23), in order to determine the wire diameter:  
\[A_{cuw}=\frac{I_{msp}}{J} \tag{23}\]  
Now, if the conductor cross-section is considered to be circular, wire diameter will be calculated according to Equation (24):  
\[D_{w}=2\sqrt{\frac{A_{cuw}}{\pi}} \tag{24}\]  
To calculate the winding initial resistance, the resistivity of copper is used (Equation (25)):  
\[R_{a1}=\frac{1.65\rho_{cut}\left(2l_{p}+2b_{p}\right)}{A_{cuw}} \tag{25}\]
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Non Orthogonal Multiple Access
#### 3.4.1 Resource Allocation Issues  
In NOMA, one resource block (RB) is shared by multiple users, and the SIC receiver is used to decode the user information at the receiver end based on the user's channel gains. Interference between users can be avoided by choosing proper power allocation algorithms. Otherwise, resource allocation issues such as user pairing and power allocation (PA) will arise. In user pairing, the users with less power are allocated with more channel gain, and the users with more power are allocated with less channel gain to make the channel fair for all the users at the transmitter end. At the receiver end, the SIC receiver is used to decode the same. In this method, if the number of users increases, then the complexity of decoding also increases at the receiver end. This is one of the major problems with user pairing. Along with this, there is another problem, i.e., if the users with high and low gain are transformed to mid gain, then mid gain users may be paired or may not, which leads to reduced channel capacity.  
To overcome the user pairing issues, optimization techniques, game theory, and machine learning algorithms are proposed in the literature. The authors proposed an optimization method while pairing two users [120; 121; 122; 123]. To optimise the user pairing, the channel gain should not be less than the predefined threshold. A strong channel pairing algorithm (CSS-PA) was proposed to increase the system capacity and fairness in user pairing [120]. In Ref. [121], the authors used a new pairing concept, i.e., the highest channel gain users are paired with the next highest gain users, and so on. Game theory for multiple user pairing and machine learning algorithms for user pairing are proposed to reduce the channel capacity [122; 123]. Different user pairing and resource allocation algorithms have been proposed in recent research to resolve the coverage and capacity issues in 5G networks, as summarised in Table 11.
## 3 Results
### Continuous Lysis of Blood Cells  
Based on the results of particle behavior and concentration due to the ICP phenomenon, we explored blood cell lysis using the developed microfluidic device. Figure 3 shows the results of the continuous lysis of blood cells injected into the developed microfluidic device. Figure 3a,b summarize the behavior of blood cells in the absence of an electric field, whereas Figure 3c,d summarize the lysis of blood cells under ICP. Generally, approximately 45% of blood is composed of blood cells. As an excessive amount of cells would interfere with the flow, we diluted the whole blood sample 500-fold using 1X PBS to ensure a smooth flow and observation of the blood cells.  
Figure 2: Fluorescence visualization images of the ICP phenomenon. Configuration of the direct electric field application without passing through a Nafion membrane when an electric field (**a**) was not applied and (**b**) when it was applied (applied voltage: 75 V). Configuration of the electric field application passing through a Nafion membrane for the ICP phenomenon when an electric field (**c**) was not applied and (**d**) when it was applied (applied voltage: 75 V). (**e**) Fluorescence intensities along the microchannel: direct electric connection before (blue line) and after (red line) applying 75 V, ICP electric connection through the Nafion membrane before (black line) and after applying 75 V (flow rate: 400 nL/min; exposure time: 100 ms).  
Figure 3a shows blood cells flowing around the Nafion membrane in the absence of an electric field. The detailed behavior of blood cells in the highlighted blue rectangle shown in Figure 3a is summarized in Figure 3b as a timelapse. Over a period of 264 ms, blood cells passed through the observation area without being lysed. Because we diluted the blood samples while maintaining the salt concentration of blood, the blood cells were also not lysed due to osmotic pressure. Conversely, when the ICP was generated, most of the blood cells lysed quickly, as shown in Figure 3c,d. Figure 3c shows the behavior of blood cells in the same region as that in Figure 3a when the ICP was generated. The blood cells were lysed near the Nafion membrane, and the blood cell lysate was concentrated near the Nafion membrane as a pale red liquid. To observe the lysis of blood cells in detail, a timelapse sequence similar to that of Figure 3b is depicted in Figure 3d. Blood cells that arrived near the Nafion membrane at 33 ms were completely lysed at 264 ms. The blood cell lysate blended with the surrounding concentrated lysate, making it impossible to distinguish the cells. Thus, to clearly demonstrate the lysis process of the blood cells, we created a video of the cell lysis process (Supplementary Video S1). In conclusion, we demonstrated that blood cells approaching the ion-depleted region were lysed within a short time (less than 1 s). As a result, they could not pass through the Nafion membrane as shown in Figure 3c,d.
## 3 Results and Discussion
### Simulations for Hierarchical Tree Structured Network
#### 3.2.2 Evaluation of Complexity of Information Fusion
The number of CPU cycles of the microprocessor and the corresponding complexity \(T_{A}\) for each fusion function considering two children per aggregating node as argued above, are given in Figure 16:0 We use Atmel Studio 7 to evaluate the complexity. When considering the results obtained in Table 4, it can be seen that the average complexity of the proposed method is 3.25 times that of average, whereas the complexity of EBDKF has been 5.25 times that of average fusion complexity. When we compare the above result with the results obtained for complexity in information fusion functions for chain-based networks in Table 2, it can be seen that the average complexity of all fusion functions except EBDKF have been increased by 33.33%, 21.875% for average, proposed method respectively. Therefore, the increment of complexity of information fusion is least for Diffusion Kalman Filtering. In average, the AVG method will require three additions and one division, whereas the proposed statistical information fusion requires four additions, seven divisions, and one multiplication whereas the EBDKF fusion will require in average, 14 additions, four divisions and eight multiplications. Thus, still the complexity of diffusion technique is highest even though the topology is changed from chain to tree topology.
## 3 Path Planning for Mobile Robots
### Path Planning Combined with Improved Ant Colony Algorithm and Dynamic Window Algorithm  
The flow chart of the fusion algorithm in this paper is shown in Figure 5:  
Figure 5: Flow chart of the fusion algorithm.  
Use on-board sensors to obtain environmental information to realize the vehicle's own positioning and build a raster map.
* Use the improved ant colony algorithm to complete the global path planning of the intelligent vehicle and obtain the optimal path.
* According to the new obstacle information obtained by the vehicle sensor in real time, integrate the information into the grid map, and use the dynamic window method to complete the local obstacle avoidance.
* Combine the global planning path, plan the local moving target points in real time, and use the preview tracking method to track the target points according to the control parameters in the path planning process to achieve real-time obstacle avoidance and obtain the local optimal path.
* Determine whether the local moving target point is the final target point; if not, jump to Step 1. If yes, the intelligent vehicle reaches the target point, and the algorithm ends.