esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
A bone age assessment (BAA) is a clinical procedure used to evaluate skeletal maturity in pediatric patients [1]. Discrepancies between bone and chronological ages over two standard deviations (SDs) indicate growth disorders [2, 3]. In clinical practice, left-hand and wrist radiography-based BAAs are widely used, including the Greulich-Pyle (GP) method [4] and the Tanner-Whitehouse 3 (TW3) approach [5]. The GP method is an atlas-based method that compares a patient's radiograph with an atlas of representative age, whereas TW3 is a scoring system based on the osseous stages and events at each level. To overcome some issues with these methods, including inter-/intra-observer variability and dependency on accuracy [6, 7, 8], researchers have attempted to develop an automated BAA approach that demonstrates high accuracy, reproducibility, and time efficiency [9, 10, 11, 12].  
Most artificial intelligence (AI)-based automated BAA approaches are derived from the whole hand [13, 14, 15, 16, 17] or region-based convolutional neural networks (CNNs) that extract the region of interest (ROI) [18] from left-hand radiographs. These automated BAAs are validated as reference data against manual ones by experts analyzing left-hand radiographs using the GP or TW method. Although AI performance has improved and the mean absolute difference (MAD) values have gradually decreased to an acceptable range, the risk of measurement errors persists. These errors can potentially be mitigated through consistent results from repeated measurements [19]. Given the ethical considerations against obtaining a second radiograph from each child, we propose using the right-hand BAA as a validation tool during the initial evaluation.  
Left-handed radiographs are preferred over right-handed ones for detecting bone age [20]. One rationale is that since most people are right-handed, the right hand is more prone to injuries than the left hand [4], making the bone ossification levels of the non-dominant hand a more accurate reflection of true bone maturity [21]. Another reason is that in the early 1900s, in a conference involving physical anthropologists, it was decided that physical measurements should be taken on the left rather than on the right side of the body [4]. [PERSON] [22] observed that the left hand was more advanced than the right hand, whereas [PERSON] and [PERSON] [23] found no such effect.  
Current developments of artificial intelligence have come to the level of recognizing human activity and their physical, motor, and mental statuses while moving and acting in physical spaces [24]. It is time to focus on how to use it efficiently in real practice without missing inevitable errors. As a simple tool for error filtering, we propose right-hand BAA as a validation tool. The aim of this study was to introduce an upgraded automatic hand bone alignment technique and to propose right-hand BAA as a tool for filtering out measurement errors.
# Beyond Correlation: Mutual Information to Detect Damage in Nonlinear Systems
## 6 Discussion of Results  
### Qualitative Description of the Damage Detection Ability of the Three Metrics  
A reduction in MI implies a reduction in the joint probability as estimated from a 2D histogram. This, in turn, implies the following: Imagine a scatter plot (SP1) showing the transmissibility coherence \(TC_{ij}\) vector representing the mean baseline state on the \(x\) axis, and a \(TC_{ij}\) vector from the baseline state is on the \(y\) axis. SP1 is expected to contain \(N_{f}\) points, where \(N_{f}\) is the length of the frequency vector. All of these points are expected to lie close to the line \(y=x\). If we create a second scatter plot (SP2), this time using a \(TC_{ij}\) vector from an unknown state on the y axis, the points on SP2 may or may not be close to the line represented by the equation \(y=x\). We may observe the following three cases:  
* **Case 1:** SP2 appears similar to SP1. All three metrics suggest that there is no damage.
* **Case 2:** SP2 contains \(N_{f}\) points close to a line \(y=a\ x+b\), where \(a\
eq 1\). In this case, ATC and TMAC may fail to detect any change. The correlation coefficient is close to 1, even though the slope may be very different from that in SP1. On the other hand, MI will likely detect the change, as the points in SP1 and SP2 no longer occupy the same grids on the \((x,y)\) plane. The amount of change in MI depends on the number of points, the number of grids in the \(x\) and y directions, and how the number of points in each grid changes between SP1 and SP2.
* **Case 3:** SP2 is very different from SP1; the points in SP2 do not lie close to any particular line. In this case, both ATC and TMAC detect damage since the correlation coefficient has decreased. MI will also detect damage since a large portion of the \(N_{f}\) points occupy different grids in SP1 and SP2.  
A large decrease in MI accompanied by small changes in ATC and TMAC is consistent with the behavior described in Case 2. This suggests that the modal frequencies of the system have changed. As mass and stiffness changes lead to changes in modal frequencies, the observed behavior is indicative of Category 1 damages.
## 4 Discussion
### Differences Between Competition Levels  
Our study found that elite athletes demonstrated significantly longer QE durations when compared to near-elite athletes. This finding suggests that elite athletes possess superior concentration abilities during shooting, thereby optimizing their shooting performance [5; 7]. This aligns with research in soccer players, where professional players were found to sustain longer target-focused QE during penalties when compared to less-experienced athletes [47]. This result is consistent with the studies by [PERSON] [5] and [PERSON] et al. [7], who emphasized that a longer QE duration helps to improve the decision-making ability and cognitive readiness of players [5; 7]. Additionally, research has indicated that the ability of elite athletes to suppress distractions (e.g., defensive pressure, crowd noise) and prioritize task-relevant visual cues likely reflects advanced attentional allocation mechanisms [6; 39]. Such cognitive control is critical in high-pressure environments, where lapses in focus can compromise performance [53]. These results underscore QE duration as a key discriminator of competitive level, offering a quantifiable metric for talent identification and training interventions [11; 20].  
Although FFD has been described as showing a slightly longer duration in elite athletes [5; 8], this trend was not significant in the current study. Variations in FFD may depend on the specific shooting scenario [8]. For instance, during free throws in an open-court setting, FFD tends to be longer, providing the athlete with ample time to adjust their movement [9]. In contrast, during jump shots under high defensive pressure, FFD may be shorter as athletes are required to execute their movements more rapidly to avoid interference [10; 21]. This contextual variability highlights the need for sport-specific analyses of FFD, as its functional role may differ across tasks and environments [10].  
Similarly, FD did not show significant differences between elite and near-elite athletes. Research has suggested that the role of FD lies more in facilitating overall visual search strategies than directly influencing shooting accuracy [9; 13]. For example, FD involves a combination of fixations on key targets (e.g., the basket) and secondary information (e.g., the position of defensive players) [42]. Elite athletes in soccer and tennis often prioritize rapid scanning over prolonged FD in order to process dynamic environments [54; 55]. In basketball, FD may involve alternating between primary targets (e.g., basket) and secondary cues (e.g., defender positioning), with its duration modulated by game tempo, being shorter during fast breaks for rapid decisions [10] and relatively longer to allow for comprehensive scene analysis during half-court offense [50]. These findings emphasize the need to contextualize gaze behavior metrics within the scope of specific tactical demands.
## 3 tRNA Core Modifications and Their Impact on Translation and Stress Adaptation
### tRNA Core Modifications and Their Relation with Stress Adaptation Mechanisms
#### 3.3.3 m\({}^{7}\)G46 Is Implicated in the Response to Oxidative Stress  
The m\({}^{7}\)G46 modification is present in both bacteria and eukaryotes [14]. The genes responsible for introducing m\({}^{7}\)G46 in tRNAs, i.e., _trmB_ in bacteria and _trm8/trm82_ in yeast, are not essential for cell viability in optimal growth conditions [95; 156; 157]. However, they are important in certain growth conditions or together with the deletion of other tRNA modification genes (Tables 1 and 2) [18; 95; 96; 98]. In _Pseudomonas aeruginosa_, the m\({}^{7}\)G46 modification has been shown to be involved in an adaptation mechanism to H\({}_{2}\)O\({}_{2}\) oxidative stress (Table 2) [158]. In a _trmB_ strain, translation of phenylalanine and aspartate codons by tRNA\({}^{\rm Phe(GAA)}\) and tRNA\({}^{\rm Asp(GUC)}\) are less efficient, which has a negative effect on the translation of mRNAs enriched in Phe and Asp. It is worth mentioning that, in humans, the m\({}^{7}\)G46 modification seems important as well for the efficient translation of certain codons [159]. In _P. aeruginosa_, the Phe and Asp codons are particularly enriched and grouped in clusters in the mRNAs of the catalases KatA and KatB, two reductases involved in H\({}_{2}\)O\({}_{2}\) detoxification. Interestingly, the transcription of _trmB_ increases following H\({}_{2}\)O\({}_{2}\) treatment, which results in an increase in the m\({}^{7}\)G46 level in tRNAs and, subsequently, in an increase in KatA and KatB production [158]. This complete regulatory loop is one of the best characterized examples of a tRNA core modification involved in stress response, although some of the underlying mechanisms are still not fully understood. As expected, the _trmB_ mutant strain presents an H\({}_{2}\)O\({}_{2}\)-sensitive phenotype, and the survival rate of _trmB_ cells exposed to 20 mM H\({}_{2}\)O\({}_{2}\) was found to be only 10% of that of the wild-type strain [158]. Interestingly, similar H\({}_{2}\)O\({}_{2}\)-sensitive phenotypes have been reported for _trmB_ or _trm8_ mutants in _E. coli_[160], in _Acinetobacter baumannii_[161] and in the fungus _Colletotrichum logenarium_[162], demonstrating the widespread implication of the m\({}^{7}\)G46 tRNA core modification in the response to oxidative stress.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Organism** & **Modification** & **Type of Stress** & **References** \\ \hline _S. cerevisiae_ & Am4 & MMS, NaOC1 & [121] \\ \hline _E. coli_, _S. typhimurium_ & s\({}^{4}\)U8 & UV & [147; 149; 150] \\ \hline _E. coli_ & s\({}^{4}\)U8/s\({}^{4}\)U9 & heat & [125] \\ \hline _S. cerevisiae_ & m\({}^{2}\)G10 & H\({}_{2}\)O\({}_{2}\), NaOC1, NaAsO\({}_{2}\) & [121] \\ \hline _T. thermophilus_, _E. coli_ & Gm18 & cold/heat & [95; 107; 125; 163] \\ \hline \end{tabular}
\end{table}
Table 2: List of tRNA core modifications linked to stress.
# Digital Health Technologies to Support At-Home Recovery of People with Stroke: A Scoping Review
## 4 Discussion
### Types and Characteristics of Technologies  
The majority of DHTs fell into two categories: wearable devices and smartphone and sensor-based solutions. Wearable technologies, such as SECOSP, SULAM, and GM-IMU, effectively measured physical activity, particularly UE and LE movement [28; 29; 32]. Accelerometer-based devices and wireless physiological monitoring systems further enabled remote feedback and data collection [27; 35].  
Smartphone and sensor-based tools, including SAM [26], SyMPATHy [34], pressure-sensitive mats [30], and the TS [31], also demonstrated strong performance in tracking specific rehabilitation metrics. These technologies were generally found to be reliable and usable, although some limitations such as discomfort, rigid design, and small sample sizes may hinder broader adoption [28; 32; 35].  
Importantly, while wearables dominate the monitoring domain, the field of DHT interventions is increasingly trending toward virtual reality [43] and AI-based approaches in today's healthcare landscape [44]. This divergence highlights the need for convergence between monitoring and intervention tools. More interdisciplinary collaboration between healthcare professionals, engineers, and data scientists is essential to develop holistic, human-centered solutions for patient adherence and daily use [45].
# Sericin for Tissue Engineering
## 2 Sericin as a Degumming Product  
### Sericin Gene  
Sericin proteins are synthesized by alternative splicing of sericin genes, which are regulated as per different phases of larval development [32]. Sericin proteins are synthesized in the middle silk gland (MSG) of the silkworm, B. mori [46]. Until now, four sericin genes have been reported, namely, sericin1, sericin2, sericin3, and sericin4, all of which are located on chromosome 11 [47].  
The first discovered gene was that encoding sericin 1, which is approximately 24 kb long and contains nine axons [48; 49]. This gene can be alternatively spliced as four mRNAs (10.5, 9.0, 4.0, and 2.8 kb) [50; 51; 52]. The sericin2 gene is approximately 16.2 kb long and comprises 13 exons [50; 51]. It can be alternatively spliced as two mRNAs (3.1 and 5.0-6.4 kb) [51]. The organization of sericin2 is similar to that of sericin1 in that the first two exons are similar in size and encode the signal peptide [53]. [PERSON] et al. discovered sericin3 with a size of 6.6 kb and a transcript of 4.9 kb containing 3 exons [54]. The most recently discovered gene, sericin4, is approximately 40 kb in size and contains 34 exons. It was identified to have six alternatively spliced transcripts [47].  
Sericin 1 and 4 proteins are synthesized in the middle and posterior part of the MSG; however, sericin 2 and 3 proteins are synthesized in the anterior part of the MSG (Figure 1) [47; 54; 55; 56]. Sericin 1 is synthesized from the first to the fifth instar, while sericin 2 is synthesized from the first instar to the fifth day of the fifth instar [55; 57]. Sericin 3 is synthesized only after the fifth day of the fifth instar, while sericin 4 is synthesized from the first instar to the fourth instar [47; 57].  
Silkworms produce two types of silk: cocoon silk and non-cocoon silk. As the synthesis locations of proteins in the silk gland determine their position in silk, the innermost and outmost sericin layers in cocoon silk comprise of sericin 1 and 3, respectively, whereas the outer and inner sericin layers in non-cocoon silk consist of sericin 2 and 4, respectively [32; 47; 55]. Non-cocoon silk with sericin 2 and 4 may exhibit better adhesion than cocoon silk with sericin 1 and 3, because sericin 2 and 4 have a higher percentage of charged amino acids than others [47; 53]. In addition, sericin 2 and 4 contain fewer serine residues than sericin 1 and 3, suggesting that sericin proteins in non-cocoon silk form fewer \(\beta\)-sheet structures than those in cocoon silk [47; 54; 55; 52]. The structural differences in sericin consistent with the distribution of sericin proteins in these two types of silk are also reflected in functional differences [47]. Sericin 1 and 3, the main components of the cocoon shell, have a rather rigid structure that forms the cocoon. In contrast, sericin 2 and 4 have adhesive properties, necessary  
Figure 1: The middle silk gland of the silkworm and sericin gene expression.  
to anchor the larval body to the surrounding material, such as a tree branch, without the need to form a rigid structure [47; 53; 55].
## 4 Numerical Examples
### A Crucifix Crack in a Square Plate under Bi-Axial Tension  
Last example is a crucifix crack in a square plate under bi-axial tension, as shown in Figure 10. The parameters for this example are taken the same as [42; 43]: \(W=0.5\,\mathrm{m}\), \(\sigma=1\,\mathrm{MPa}\), \(E=1.0\times 10^{3}\,\mathrm{MPa}\), \(\
u=0.3\), and the plane strain condition is assumed. Due to symmetry, the SIF at each crack tip is theoretically the same; therefore, only the results at crack tip A are considered. The normalized SIFs \(F_{I}=K_{I}/\sigma\sqrt{\pi a}\) and present numerical results comparison with references [42; 43] are shown in Table 10.  
The computations of SIFs are under the degree of interpolation polynomials \(p=1,2,...,8\) for all of the examples. Comparisons of the numbers of elements used by different numerical methods are listed in Table 11. In the computations of the first model, the total number of discretizing meshes is 252, and the maximum DOF is 15,678; in the second to the fifth models, the total number of discretizing meshes is 39, 142, 329 and 580, respectively, and the maximum DOF is 2524, 9064, 20,722 and 36,206, respectively. The above examples show that coarse meshes are enough to obtain quite accurate SIFs using the present method. It can be seen that the convergences are very fast with the increasing of degree \(p\) from 1 to 8,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(a\) & **0.10** & **0.15** & **0.20** & **0.25** & **0.30** & **0.35** \\ \hline Present & 0.8824 & 0.9084 & 0.9507 & 1.0190 & 1.1320 & 1.3170 \\ Ref [42] & 0.8800 & 0.9092 & 0.9537 & 1.0223 & 1.1300 & 1.2866 \\ Ref [43] & 0.8831 & 0.9246 & 0.9614 & 1.0040 & 1.1370 & 1.3247 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Normalized SIFs for a crucifix crack.  
Figure 10: (a) A crucifix crack in a square plate under bi-axial tension; (b) Global and local meshes (the total number of elements is 580, DOF = 36,206).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Reference** \\ **Solutions**[39] \\ \end{tabular} & **[PERSON] et al.**[40] & \begin{tabular}{c} **Relative** \\ **Error (\%)** \\ \end{tabular} & **[PERSON] et al.**[41] & \
\begin{tabular}{c} **Relative** \\ **Error (\%)** \\ \end{tabular} & **Present** &
\begin{tabular}{c} **Relative** \\ **Error (\%)** \\ \end{tabular} \\ \hline \(F_{IA}\) & \(1.040\) & \(1.029\) & \(-1.058\) & \(1.029\) & \(-1.058\) & \(1.042\) & \(0.192\) \\ \(F_{IIC}\) & \(0.495\) & \(0.492\) & \(-0.606\) & \(0.489\) & \(-1.212\) & \(0.496\) & \(0.202\) \\ \(F_{IIC}\) & \(0.503\) & \(0.501\) & \(-0.398\) & \(0.501\) & \(-0.398\) & \(0.504\) & \(0.199\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Normalized SIFs for the branched cracks in an infinite plate.  
the accuracy of the SIFs obtained by the present method is also satisfactory, and the SIFs have better stability.
## 3 Experimental Validation and Analysis
### Similarity Clustering  
#### 3.3.1 Spectrum Analysis  
After decomposing the GNSS displacement and acceleration sequences using CEEM-DAN, a series of IMF sequences were generated, and frequency domain features were extracted from these sequences. Building upon time domain analysis, clustering based on the frequency domain similarity of the sub-sequences was conducted. Through spectral analysis, the main frequencies of each sub-sequence were determined.  
For the displacement sequence, as the order of IMF increased, the energy distribution decreased from high to low frequencies. As shown in Figure 9, the energy of IMF1 was mainly concentrated in the range of 1 Hz to 1.5 Hz; IMF2 to IMF3 exhibited energy distribution primarily in the range of 0.5 Hz to 1 Hz; IMF4 to IMF7 had energy concentrated near 0.25 Hz, while IMF8 and other components were mainly centered around 0 Hz. Based on these energy distribution characteristics, IMF sub-sequences with concentrated energy distributions were categorized into different groups according to their dominant frequency features.  
As shown in Figure 10, for the acceleration time series, the energy distribution of its IMF sequences also exhibited a similar trend, albeit with different frequency ranges. The energy distribution of IMF1 and IMF2 was primarily concentrated in the range of 1 Hz to 2.25 Hz; IMF3 and IMF4 showed energy distribution mainly in the range of 0.25 Hz to 0.75 Hz; IMF5 to IMF12 had energy concentrated in the range of 0 Hz to 0.25 Hz, while IMF13 and other components were likewise mainly centered around 0 Hz. Similarly, based on these energy distribution characteristics, the IMF sequences with concentrated energy distributions were categorized into different groups according to their dominant frequency features.  
Figure 9: Power Spectral Density of Displacement Decomposition Components.
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
## 4 PIC for Next-Generation PON
### Technology and Impact on Reach  
A major requirement for future PON technologies is the need for coexistence with current deployed PON systems [65; 70]. For example, GEPON has already been deployed on a scale of millions, thus any new PON technology must be capable of coexisting with this large base [65]. A PON framework of three coexisting technologies, i.e., GPON, XGSPON, and NGPON2, is depicted in Figure 3 from Section 3. Here, the same ODN conveys several technologies allowing the migration and evolution of the network without forcing clients to shift technology. This approach leads to reduced CAPEX and supports pay-as-you-go approaches since it allows keeping using the legacy operating technologies untouched, opening an evolution path for other technologies to follow [7].  
The longer the required reach, the more difficult it gets to find the laser technology able to support the demanded distance. As an example, for a 10 Gbit/s with a short-reach like 10 km, almost all standard laser technologies can be used. If addressing the 2.5 Gbit/s market, a medium-reach like 40 km and further are still not a major limiting factor to the laser choice. However, if requiring a common GPON ODN which ranges 20 km including splitters and the handling of dispersion penalty, it poses challenges to most of the 10 Gbit/s solutions. To achieve that target, the laser must be designed to perform a low penalty, typically bellow 1 dB to 2 dB, at the required distance. To meet those specs,  
Figure 4: Diagram describing the evolution of photonic integrated circuits (PIC) for PON, specifically the implementation of a bi-directional optical sub assembly (BOSA) transceiver solution in PON ONU side, i.e., XFP with discrete optical component packaging (current PON products) and with PIC approach technology (next-generation products).  
specially designed direct modulated lasers (DML) are required, or even the migration to external modulated laser (EML) technologies [7; 8].  
Furthermore, in most recent standards, ODN should support 128 users and a 20 km reach. These requirements have the penalty of a 21-dB attenuation stemming from splitting losses plus 4 dB due to fiber losses and around 3 dB insertion loss (IL) of co-existence elements, leading to an ODN total loss greater than 28 dB. If we target 10 Gbit/s with avalanche photodiode (APD) receivers allowing a \(-\)26 dBm to \(-\)30 dBm receiver sensitivity, it leads to a minimum laser output power of 4 dBm. A possible alternative to overcome this requirement is to use an amplification stage in the path which removes the passivity of the ODN, however, it reduces the potential usage of EML-based solutions since its typical power does not exceed the 0 dBm.