esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Exercise-induced injuries frequently occur in sports. Hamstring injury is regarded as one of the most common and destructive damages due to the high incidence of the initial injury and frequent recurrence. Epidemiological investigations have shown that hamstring injury represents 37% of all muscle injuries [1]. It is common in competitive sports characterized by fast kicking and running [2, 3], especially during non-contact sprinting activities, including gallops in ball games and sprints in track and field [1, 3]. It also occurs in fitness exercises that involve extensive muscle lengthening-type maneuvers, such as yoga, and dancing [3, 4]. Male athletes are 64% more likely to sustain an acute hamstring injury than female athletes [3, 5]. These rates are similar in soccer, baseball, softball, and indoor track [6]. Injuries seriously affect athletes' performance in the arena, resulting in losing playing time, suffering from pain, and even threatening their professional career [7, 8, 9, 10].  
Hamstring injuries mostly occur proximal to the biceps femoris [3, 11]. It has been reported that 57-72% of all hamstring injuries occur during sprinting. Nearly 94% of these injuries appear in the biceps femoris long head (BFLH) [12, 13, 14, 15, 16]. For example, in 17 subjects with single injuries or isolated re-injuries, 12 of these were injured on the proximal part [17].  
This evidence supports the opinion that the biceps femoris is the major area of hamstring injuries and the injuries mostly occur in the proximal part of BFLH.  
Researchers explored the underlying mechanism of hamstring injury through anatomical, kinematic, and kinetic analyses of sprinting [14; 18; 19; 20]. The injury's timing, location and tissue involved are the discussions' foci due to the discordance of muscle-tendon length and force changes in leg swing progression throughout the gait cycle [15; 21; 22; 23; 24]. However, the reasons and mechanisms for why hamstring injuries tend to occur proximally remain unclear. In addition, injury details are equally unclear, e.g., parallel tearing, tissue becoming separated, misalignment of myofibrils, and others [25]. Thus, it is essential to explore further hamstring injuries.  
To understand the reason that hamstring injuries tend to occur in the proximal area and explore the relationship between the part and injury from the current literature, this review focused on discussing proximal structural features of the hamstrings, from morphological and connected pattern diversity to the tissue's mechanical properties under different loadings. Are injuries related to the structural features of BFLH? What is the risk factor of injury to the BFLH when hamstrings perform multi-functional movements? Although the discussion is focused on injuries to the proximal part of hamstrings, we also attempt to provide suggestions to prevent hamstring injury for sports participants.
## 3 Major Routes of Xylose Transport and Metabolism in Bacteria
### Mechanisms of Xylose Transport
sacchari_ can co-metabolize xylose and glucose, but the rate of xylose consumption decreases substantially in the presence of high glucose concentrations [64; 42; 60]. Concerning _Clostridium_ species, a specific repressor XylR and a pleiotropic regulator CcpA have been found [65]. In the strain _C. acetobutylicum_, a mutation in the 14-nucleotide catabolite responsive element (CRE) sequence relieved CCR and increased 7.5-fold xylose consumption in the presence of glucose [66]. Different strategies, such as the inactivation of PTS components and mutagenesis of _CRP_ or _XylR_, were used in _E. coli_ with the aim of relieving CCR, allowing for the co-utilization of xylose and glucose [67; 68; 53; 69].
In addition, if the mass moment of inertia (_I_) of the FW and the athlete's mass are known, _GRF_ can be predicted as the sum of \(F\) calculated from the rotational acceleration of the shaft (FW pulling force) and \(F\) calculated from the linear acceleration of the athlete's \(m\). In contrast to the use of force plates and linear encoders (which are the gold standard in the acquisition of _GRF_ and linear \(v\) data), when using rotary encoders, the characteristics of the shaft (shape of cone or shape of cylinder and its radius) should be taken into account as they, together with the \(I\), determine the calculated mechanical output [33]. In view of this, care should be taken when using calculated kinetic measures for research purposes and for generalizing the results [34]. Therefore, it seems important to determine the possible differences between these measurement methods considering the magnitude of the _GRF_ and \(v\) obtained under different inertias, as well as their influence on the _F_-\(v\)-\(P\) relationship. When quantifying neuromuscular performance in athletes, an accurate and credible description of the mechanical variables is of great importance. No study aimed to investigate the concurrent validity of the outcome variables of the _F_-\(v\)-\(P\) relationship in FW squats using different inertias and sensory systems. Therefore, we conducted a validation study using a synchronized dual force plate and a linear position encoder as criterion measures for calculating _GRF_ and linear \(v\), with a shaft-integrated rotary encoder used as a direct measure. In the first step, the aim of our study was to evaluate the inter-set reliability of different sensory systems (force plates, linear encoder, and rotary encoder) for the calculation of _GRF_ and \(v\) variables. In the second step, we aimed to compare the _GRF_ and \(v\) variables calculated from the rotary encoder data with the variables calculated from force plates and linear encoder data. Step one and two led us to the third, the main objective of our study, which was to compare the calculated _F_-\(v\)-\(P\) relationship outcomes between the criterion and direct measures (force plates and linear encoder vs. rotary encoder). We hypothesized that due to the indirect expression of _GRF_ and \(v\) from rotary encoderdata, the latter would provide us with lower inter-set reliability and statistically significant differences in the outcome variables of the _F-v-P_ relationship.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 3 Implementation of the Selected Balancing Methods
### Single Flyback Converters Based Equalization
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline V\({}_{\text{IN}}\) & V\({}_{\text{BAT}}\) & - \\ V\({}_{\text{OUT}}\) & V\({}_{\text{BAT}}\)/4 & - \\ N & 4 & - \\ L\({}_{\text{P}}\) & 300 & \(\upmu\)H \\ L\({}_{\text{leak}}\) & 3 & \(\upmu\)H \\ Frequency & 250 & kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Single flyback characteristics. Figure 3: Secondary peak current for each capacitor at different frequencies. The line associated with _WE 750311625_ is congruent with the line represented by _ZA9670_. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Manufacturer** & \(\mathbf{L_{P}}\) (\(\upmu\)H) & \(\mathbf{L_{leak}}\) (\(\upmu\)H) & **Manufacturer** & **Height (mm)** \\ \hline
750311558 & 300 & 3 & Würth Elektronik & 10.54 \\
750311559 & 175 & 3 & Würth Elektronik & 10.54 \\
750313973 & 40 & 1 & Würth Elektronik & 11.43 \\
750311625 & 9 & 0.5 & Würth Elektronik & 12.7 \\ TA7608 & 2 & 0.06 & Coilcraft & 5.97 \\ ZB1050 & 12.5 & 0.4 & Coilcraft & 10 \\ ZA9670 & 9 & 0.4 & Coilcraft & 10 \\ NAS5743 & 4 & 0.116 & Coilcraft & 11.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: COTS transformer compilation.
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 5 Results and Discussion
### Security Evaluation
#### 5.3.4 Denial of Service  
A DOS attack at the physical level is beyond the scope of protection of the secure domain mechanisms, and protection against this type of threat requires completely different means (e.g., recent developments have resulted in a prototype DM antenna system that applies physical layer encryption on top of existing radio-communication interfaces [23]). Denial of service attacks can also be performed at the data link and network layers. Network layer DOS attacks involve the routing protocol.
## 3 Results and Discussion
### Comparison of Retention of the Aroma Compounds during the Different Encapsulation Process  
Since it will be time-consuming to compare the retention of each aroma compound present in terephthal fruit oil using different encapsulation techniques, some critical aroma compounds, \(\alpha\)-pinene, sabinene, \(\beta\)-myrcene, (Z)-\(\beta\)-ocimene, ocimene, and linalool were selected for comparison in this study. These aroma compounds are present in high quantities and were previously addressed as the most powerful aroma-active compounds with their higher flavor dilution factors by [PERSON], [PERSON], [PERSON], and [PERSON] [7]. Table 5 shows the percentage of retention of selected volatiles in encapsulates as a function of different encapsulation methods. Many factors, such as the ability of aroma compounds to form a complex with wall material, physicochemical properties of the volatile compounds, wall material type, and concentration, affect the retention and stability of volatile compounds [64]. In general, volatiles with high molecular weight, low vapor pressure, and low hydrophobicity have better retention in the carbohydrate-based matrix [44; 65]. The target aroma compounds identified in this study were all in the terpene class with the same molecular weight (MW = 136.23) except linalool (MW = 154.25). Additionally, the same wall material (MD:GA of 25:75) has been used in all encapsulation methods. Hence, the hydrophobicity and vapor pressure of the compounds would be the main reasons for the retention of each aroma compound. In SD, the \(\alpha\)-pinene and linalool are the most retained components, with almost 100% among the other ones. This result could be because linalool had the highest molecular weight and lowest vapor pressure among the target volatiles. On the other hand, although \(\alpha\)-pinene had the highest vapor pressure, it showed the highest retention owing to its hydrophobic characteristics. Aroma retention results were also consistent with encapsulation efficiency results. As was expected, aroma retention was quite low for SFD and FD methods due to their low encapsulation efficiency. The lower the encapsulation efficiency, the higher the amount of surface oil. This means they will be susceptible to more volatile loss [66]. In addition, the lower percent retention values of aroma compounds for the SFD method could be caused by the evaporation of volatiles in direct contact with liquid nitrogen in an open vessel. With the exception of \(\beta\)-myrcene, SD-UN resulted in the best aroma retention for all other target aroma compounds. The total retention of selected aroma compounds in SD-UN was the highest (73.52%), subsequently followed by SD-2 FN (62.28%), FD (24.52%), and SFD (14.16%). [PERSON] et al. [67] also reported that spray drying quickly removes water by vaporization from oil-in-water emulsions and provides high retention of volatiles. Similarly, [PERSON] et al. [68] have found better retention of limonene in encapsulates obtained by SD than that obtained by FD. Interestingly, the retention of the \(\beta\)-myrcene was the
## 3 Comparative Analysis of Working Performance of Pile-Raft System
### Numerical Calculation Scheme  
To verify the feasibility of CFG piles in reinforcing the foundations of high-rise buildings, three schemes for the numerical calculation of piled raft foundations were used as follows:  
Scheme 1: Raft + concrete-bored piles. The top of the pile was rigidly connected to the raft; the length of pile was 14 m; the diameter was 0.8 m; and the spacing between piles was 3.2 m (shown in Figure 9a).  
Scheme 2: Raft + cushion + concrete-bored piles. The thickness of the cushion was 200 mm; and the parameters of the pile were the same as above (shown in Figure 9b).  
Scheme 3: Raft + cushion + CFG piles. The length of the pile was 14 m; the diameter was 0.4 m; and the spacing between piles was 1.6 m (shown in Figure 9c).  
Moreover, based on Scheme 3 and the integral equivalent method proposed in Section 2.2.3, Scheme 4 was developed.  
Scheme 4: Raft + equivalent reinforcement zone. The cushion was incorporated into the equivalent reinforcement zone (shown in Figure 9d).  
Figure 8: Comparison of horizontal deformation of the bottom cushion: (**a**) embedded pile model; (**b**) equivalent reinforcement zone model.  
Figure 9: _Cont._The simulated procedure of constructing calculations for the above four schemes was as follows: (1) applying gravity to calculate the initial stress field of soil as initial state; (2) excavating the foundation pit, activating the reinforcement zone of the soil nailing wall, and calculating to convergence; (3) constructing piles below the bottom of the foundation pit, activating the embedded pile element (for Schemes 1, 2, and 3) or improving the equivalent parameters of the reinforcement zone (for Schemes 4), and calculating to convergence; (4) constructing the raft and basement structures, applying the upper load, activating the corresponding structural element, interface element, and surface load, and calculating to convergence; and (5) backfilling around the basement, and calculating to convergence.  
In order to improve the accuracy of the calculation of Scheme 2, a special simulation on the cushion layer was carried out by means of refining the finite element mesh of the cushion, so that the stress concentration on top of the pile could be properly reflected. It can be seen that the total mean stress \(p\) of the cushion layer was uniformly distributed, as shown in the Figure 10, which effectively avoided the excessive concentration of local stress on top of the pile, ensuring the accuracy of the calculation.  
Figure 10: Principal stress nephogram in the cushion layer.  
Figure 9: Pile–raft basement models for different simulation schemes: (**a**) Raft + concrete-bored piles (pile simulated by embedded pile element); (**b**) raft + cushion+ concrete-bored piles (pile simulated by embedded pile element); (**c**) raft + cushion + cement/fly ash/ gravel (CFG) piles (pile simulated by embedded pile element); (**d**) raft + equivalent reinforcement zone.
One of Ap4A's central roles lies in the regulation of the cell cycle. By interacting with key proteins, it influences transitions through various cell cycle phases, ensuring controlled proliferation. Disruptions in this regulatory function could contribute to pathological conditions characterized by unregulated cell division, including cancer [5].  
In the context of tumor biology, Ap4A's role becomes even more intriguing. Tumorgenesis is driven by a multitude of factors--genetic mutations, aberrant signaling pathways, metabolic reprogramming, and interactions with the tumor microenvironment (TME) [6]. Tumor cells must navigate and adapt to these pressures, and Ap4A appears to play a critical role in this process. Though its mechanisms in cancer are still being unravelled, emerging evidence points to Ap4A as a key player in tumor cell adaptation and survival.  
Beyond cancer, its role has been widely studied in other systems. For instance, it is co-stored with ATP in the dense granules of platelets and released upon activation in the vascular system. Alongside Ap2A and Ap3A, Ap4A contributes to cardiovascular regulation by inducing vasodilation through the stimulation of nitric oxide (NO) and prostacyclin (PGI\({}_{2}\)) production in endothelial cells [7]. These findings highlight Ap4A's broader signaling functions in inflammation, vascular tone, and endothelial homeostasis. Notably, Ap4A interacts with purinergic receptors, including several P2X and P2Y subtypes. Although often a partial agonist compared to ATP, Ap4A modulates purinergic signaling pathways, including calcium flux, and may temper excessive purinergic activation [8]. This modulation becomes particularly relevant in cancer, where Ap4A levels rise in response to oxidative and thermal stress--suggesting a role in tumor progression, immune evasion, and resistance to therapy.  
This review delves into the multifaceted roles of Ap4A in both normal and cancerous contexts, with a particular emphasis on its involvement in key hallmarks of cancer such as apoptosis evasion, metabolic reprogramming, stress adaptation, and proliferative signaling. We explore its interactions with purinergic receptors--most notably P2X7--and examine how these relationships influence tumor biology and may serve as targets for therapeutic intervention. Finally, we address current challenges and future perspectives in leveraging Ap4A's biology for cancer treatment.
## 1 Introduction
The SAFY crop model was originally developed by [13] and the model was initially evaluated on WW fields in dry land areas in Morocco. The basic version of the model simulated crop growth as green area index (GAI), dry above-ground matter (biomass) and dry grain matter (yield). Due to the simple model architecture, which is modular in design, the SAFY model has been widely adapted by the research community and applied in different environments and crops [24; 27; 28; 29; 30; 31]. [PERSON]. [14; 24] added a soil-water balance module to simulate the effect of the water stress on the crop growth and production. [PERSON]. [25; 32] added a module to simulate the carbon budgets in addition to a soil-water balance module. Additional modifications have focussed on GAI, an important biophysical variable that can describe the crop growth and a state variable in the SAFY model. GAI can be measured on the ground by different sensors [33], collected by destructive sampling methods [33] or, can be retrieved from remote sensing data using empirical or process-based/hybrid methods [21; 34; 35]. For example, Refs. [29; 31] integrated remote sensing data into the SAFY model to update the crop-state variable GAI during the assimilation process, leading to improved model estimates. For the research presented here, we sought to evaluate the use of the SAFY model [13], with modified soil-water balance module [24; 31] and carbon fluxes module [25; 32] to simulate crop growth, biomass, grain yield and components of the water and carbon fluxes for the main crop types grown in Ireland and for which we had access to published or available in situ data. The decision to employ the specific model was based around the model architecture, which can be readily modified and adapted, and the limited number of parameters required to run the model. Given the model's simplistic modelling approach, it has been successfully applied for estimation of crop growth, above ground biomass and grain yield using limited computational resources which is usually a challenge at spatial level application of complex crop models and makes it even more attractive for practical applications. In addition, the model offers the potential to use and deploy in an operational capacity, with limited user intervention. We also sought to contribute to the model development, through the addition of a new water stress function and modifications of components in the energy and carbon flux modules. The soil-water balance module, specifically the water stress function, is detailed in the methods section and latent heat is derived from the actual evapotranspiration in the context of the Irish soils that have been comparatively less studied. The modified model was then evaluated on three maincrop types in Ireland, namely WW, SB and WOSR. Thus, the objective of the work was to adapt a previously developed model, enhancing its capacity to be deployed in a broader range of applications, and evaluate the model for use in a maritime climate regime.
# Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect
## 3 Experiments
### Skeleton Tracking Precision  
The list of all body joints used throughout the rest of the paper is found in Figure 7. In Figures 8-12, there is the precision for particular joints and positions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Kinect 1** & **Kinect 1 No Smooth** & **Kinect 2** & **Azure NFOV** & **Azure WFOV** \\ \hline Position 1 & 15.85 & 25 & 0 & 6.18 (1.09) & 0 \\ \hline Position 2 & 0 & 0 & 0 & 5.63 (10) & 6.52 (0.43) \\ \hline Position 3 & 30 & 0 & 0 & 18.75 (10) & 19.01 (10) \\ \hline Position 4 & 30 & 0 & 0 & 18.75 (10) & 37.32 (28.13) \\ \hline Position 5 & 29.98 & 15.55 & 0 & 18.75 (10) & 32.79 (11.23) \\ \hline Position 6 & 29.96 & 30 & 0.1 & 18.75 (10) & 29.78 (11.3) \\ \hline Position 7 & 26.65 & 35.03 & 0 & 18.75 (10) & 54.43 (99.22) \\ \hline Position 8 & & & & 21.84 (12.1) & 41.41 (21.11) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage of undetected joints for each sensor and position.  
Figure 7: List of skeleton body joints detected by corresponding Kinect sensor (the presented colors are used in all figures throughout the paper).  
Figure 8: Body joint precision measurements of Kinect v1.  
Figure 9: Body joint precision measurements of Kinect v1 with skeleton smoothing turned off.  
Figure 11: Body joint precision measurements of Azure Kinect in the NFOV mode.  
Figure 10: Body joint precision measurements of Kinect v2.  
As can be seen, for certain body joints, the standard deviation varies considerably, even within one sensor datum. However, there is a correlation with the measured distance (Figure 4). Furthermore, the Azure Kinect in WFOV mode is quite unusable with distances higher than 2.5 m; this correlates with the amount of unreliable body joint data. In the NFOV mode, the data are stable and reliable up to a 3.6 m distance.
###### Abstract  
The effects of harmonics, interharmonics, and subharmonics on low-voltage distribution networks, leading to a deterioration in electrical power quality, have become more evident in recent years. The main harmonic sources are power electronic devices due to their implicit nonlinearity. Interharmonic and subharmonic components are mainly caused by a lack of synchronization between the grid frequency and the switching frequency of the power converters. This can be caused by asynchronous modulated devices, or more commonly by fluctuations in the fundamental grid frequency. Interharmonic currents cause interharmonic voltage distortions that affect grid-synchronized or frequency-dependent systems. The IEC-61000-4-7 proposes a general guide on harmonics, interharmonic measurements, and instrumentation in current supply systems. However, the techniques proposed in the standard are intended for measurement and do not enable a precise identification of the interharmonic components in a signal. This work proposes new definitions for the spectral energy aggraputon to improve signal component detection for the IEC standard. Furthermore, an adaptive Kalman filter algorithm is developed that enables the exact identification in real time of the frequency, amplitude, and phase of these components. The proposed system will become the basis for the implementation of a new range of measurement systems that provide improved accuracy and real-time operation. The work is supported by simulated results analysing various scenarios (including transients after changes in harmonic content in the grid voltage) that demonstrate the effectiveness of the proposed method.  
harmonics; interharmonics; subharmonics; power quality; energy efficiency; adaptive Kalman filter