esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Other studies have also suggested that it is important to consider body fat distribution when assessing the effects of FM on bone mass, as subcutaneous and visceral fat in the abdominal region may have different effects on bone tissue [22; 23]. Furthermore, it can be assumed that not only age category and sex are important determinants of the relationship between bone mass and body composition (BC) parameters, but that obesity also plays an important role. In a cohort of non-obese postmenopausal women over 60 years of age from China, total FM was positively associated with BMD, with an increase in BMD observed with increasing FM [24]. When analyzing obese and non-obese subgroups in the study by [PERSON] et al. [25], the positive correlation between FM and BMD disappeared in obese adults. In addition, FM and BMD showed a negative correlation in premenopausal obese women. The authors concluded that FM is not beneficial in obese individuals and may even be detrimental to bone health. Later, [PERSON] et al. [18] pointed out that the higher the PBF, the lower the BMD in male adolescents with obesity. From this, the study concluded that the decrease in bone mass in overweight, obese, and extremely obese adolescents was influenced by an increase in PBF. We therefore suggest that these different results emphasize the importance of assessing bone quality and bone tissue properties differently in obese and non-obese individuals, depending on the level of FM. There is evidence of a correlation between the amount of bone measured by DXA BMD and BC parameters. However, current research rarely focuses on investigating the effect of BC on bone tissue properties measured by mQUS SOS in young adults, so clear conclusions in this area are lacking. Current data only suggest a lower tibial SOS in female adolescents with higher adiposity [26]. Furthermore, there are currently no studies investigating the differential effects of BC on bone quality measured by SOS, specifically in obese and non-obese young adults. However, existing literature [27] suggests that there may be different associations between bone tissue and BC in adolescents with FM within and above normal limits. Therefore, this study aimed to explore the relationships between SOS (bone tissue parameter from the transaxial mQUS) and specific bioelectrical impedance analysis (BIA) parameters (e.g., FM and LBM), taking into account sex and obesity status. Adjustments for osteoporosis-related behavioral and lifestyle factors were also included in the regression analyses.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
###### Abstract  
Over the past few years, wireless communication has grown dramatically, and the consumer demand for wireless services has seen a significant jump. One of the main challenges for beyond fifth generation (B5G) networks is the increased capacity of the network. The continuously increasing number of network users and the limited radio spectrum in wireless technologies have led to severe congestion in communication channels. This issue leads to traffic congestion at base stations and introduces interference in the network, thereby degrading system capability and quality of service. Interference reduction has thus become a major design challenge in wireless communication systems. This review paper comprehensively explores interference management (IM) strategies in B5G networks. We critically analyze and summarize existing research on interference issues related to device-to-device communication, heterogeneous networks, inter-cell interference, and artificial intelligence (AI)-based frameworks. The paper reviews a wide range of methodologies, highlights the strengths and limitations of state-of-the-art approaches, and discusses standardized techniques such as power control, resource allocation, spectrum separation and mode selection, carrier aggregation, load balancing and cell range expansion, enhanced inter-cell interference coordination, coordinated scheduling and beamforming, coordinated multipoint, and AI-based interference prediction methods. A structured taxonomy and comparative summary are introduced to help categorize these techniques. Several related works based on their methodologies, shortcomings, and future directions have been critically reviewed. In addition, the paper identifies open research challenges and outlines key trends that are shaping future B5G IM systems. A comparative visualization is also provided to highlight dominant and underexplored optimization objectives across IM domains. This review serves as a valuable reference for researchers aiming to understand and evaluate current and emerging solutions for interference mitigation in B5G wireless systems.  
B5G; interference management; D2D communication; inter-cell interference; enhanced inter-cell interference coordination; coordinated multipoint; coordinated scheduling; beamforming; HetNets 2025142237  
1
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 4 Experiments
### Comparison to the State of the Art  
We compare the results of the PMFN with recent state-of-the-art single image SR (SISR) and LFSR methods, which are VDSR [8], EDSR [19], GB [31], resLF [12], LFSSR [32], LF-ATO [13], LF-InterNet [14], LF-DFNet [14], MEG-Net [23], and DPT [24]. The bicubic method is the baseline of comparison. The VDSR and EDSR are the typical methods for SISR. The GB is the traditional method for LFSR. The resLF, LFSSR, LF-ATO, LF-InterNet, LF-DFNet, MEG-Net, and DPT are the LFSR methods by using deep learning. The learning-based methods have been retrained on the same training datasets for consistency. Meanwhile, we used peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) to evaluate the quantitative performance.
## 3 Results and Discussion  
### Organogelators: Design and Synthesis  
Several new partially fluorinated organogelators were designed via careful manipulation of hydrogen bonding functionalities (urea and amide) and hydrophobic functionalities (fluorocarbon and hydrocarbon) in the molecule to promote gelation. Boc-protected amino acids, _N_-Boc-\(\beta\)-alanine and _N_-Boc-L-aspartic acid were chosen as the building blocks. First _N_-Boc-\(\beta\)-alanine and _N_-Boc-L-aspartic acid were transformed to the corresponding _N_-Boc-octylamides (**1** and **3**) by reacting with octylamine (Scheme 1). The resulting amides were deprotected to obtain the corresponding free amide-amine derivatives (**2** and **4**), the primary building blocks for partially fluorinated amide or urea-amide derivatives.  
**Scheme 1.** Synthesis of 3-amino-_N_-octylpropanamide (**2**) L-asparticdioctylamide (**4**) building blocks from _N_-Boc-\(\beta\)-alanine and _N_-Boc-L-aspartic acid.
## 1 Introduction  
Advances in biological technology are generating an exponential growth in the amount of genomic data available for analysis. Processing this data requires pattern classification and feature selection methods that can identify those features that are significant, in addition to eliminating redundant and irrelevant features. Effective methods are especially needed that function over very large data sets with thousands of features, such as genome databases of living organisms. An important genome data analysis task is to identify subsets of genes that affect a specific phenotypic characteristic, such as infectious and inflammatory diseases [1], economically important traits in agricultural products [2], or in cancer prognosis [3]. Phenotypic characteristics can be binary, such as whether or not an individual has a particular disease, or quantitative and continuous, such as the weight of a species [4] or the number of bristles on Drosophila melanogaster [5].  
Quantitative traits in organisms are formed during development by the interaction of many genes located throughout the genome and distributed over multiple chromosomes. Experiential techniques that study quantitative traits determine the location of each relevant genes, termed loci, rather than the genes directly. So the problem of identifying the set of genes (polygenes) that affect the variation of a quantitative trait is defined in terms of the loci of those genes, and referred to as Quantitative Trait Loci (QTL) problem [4]. Knowing the locations and effects of QTLs enables scientists to understand the biochemical basis of traits and how these traits may have evolved through the time. There is great economic value in solving the QTL problem for specific traits of commercial organisms since these traits may then be manipulated to improve yield.  
[PERSON] in 1866 [6] was the first to introduce the quantitative trait loci concept. He suggested that the color of flowers can be influenced by several genetic factors. Some years later, in 1923, [PERSON] [7] studied the relation of seed weight and seed coat color in beans demonstrating that multiple genes control these bean phenotypes.  
The use of genetic markers was introduced by [PERSON] in 1961 [8] as a means to map different groups of genes that together control a quantitative trait. Biochemical markers characterize and identify QTLs responsible for variation in quantitative traits by identifying similarities and differences over samples of DNA and their corresponding phenotypes. Techniques that utilized dominant markers have the advantage over co-dominant methods since they allow for the analysis of many loci per experiment. An earlier method, Random Amplification of Polymorphic DNA (RAPD) was slow and cumbersome since it requires a large amount of sample DNA along with steps to produce the gene loci present. New techniques such as amplified fragment length polymorphism or AFLP utilize high-throughputsequencing to speedup the process. Many authors have applied these techniques in their research to study economically significant organisms like: maize (see [9; 10; 11]), tomatoes (see [12; 13]), and rice (see [14]).
# Lifting Activities Assessment Using Lumbosacral Compression and Shear Forces
## 2 Materials and Methods
### Kinematic and Kinetic Recordings  
The movements of 37 spherical markers (15 mm in diameter) covered with aluminum powder reflective material were detected using eight infrared cameras (sampling frequency 340 Hz) optoelectronic motion analysis system (SMART-DX 6000 System, BTS, Milan, Italy). Thirty-three markers were placed over specific anatomical points: the cutaneous projections of the spinous processes of the seventh (C7) and tenth cervical vertebrae, suprasternal notch (between the clavicular notches), sternum, sacrum and, bilaterally, over the temple, posterior-superior parietal bone, acromion, olecranon, ulnar styloid and radial processes, head of the third metacarpal bone, anterior superior iliac spine, great trochanter, lateral femoral condyle, fibula head, lateral malleolus, metatarsal head and heel (Figure 1, [19; 20; 21; 22; 23]). Furthermore, four markers were placed over the four vertexes of a load consisting of a plastic crate. Ground reaction forces were acquired by using four dynamometric platforms at a sampling rate of 680 Hz (P 6000, BTS, Milan, Italy) embedded in the floor. Data acquisition from the infrared cameras and force platforms was integrated and synchronized.  
Figure 1: Kinematic (right side) and kinetic protocols (left side), an example of load displacement and velocity and lifting cycle definition; the values of NIOSH protocol for each lifting task are reported.  
In green, the values of the task with lifting index (LI) = 1; in yellow, the values for LI = 2; and in red, the values for LI = 3. L: load weight; H and HM: horizontal locations and corresponding values of the multiplier; V and VM: vertical locations and corresponding values of the multiplier; D and DM: vertical travel distances and corresponding values of the multiplier. Red * on the body (right side) represent the center of mass of each body segment.
# Dependence of the Staphylococcal Volatilome Composition on Microbial Nutrition
###### Abstract  
In vitro cultivation of staphylococci is fundamental to both clinical and research microbiology, but few studies, to-date, have investigated how the differences in rich media can influence the volatilome of cultivated bacteria. The objective of this study was to determine the influence of rich media composition on the chemical characteristics of the volatilomes of _Staphylococcus aureus_ and _Staphylococcus epidermidis_. _S. aureus_ (ATCC 12600) and _S. epidermidis_ (ATCC 12228) were cultured in triplicate in four rich complex media (brain heart infusion (BHI), lysogeny broth (LB), Mueller Hinton broth (MHB), and tryptic soy broth (TSB)), and the volatile metabolites produced by each culture were analyzed using headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography--time-of-flight mass spectrometry (HS-SPME-GC\(\times\)GC-TOFMS). When comparing the chemical compositions of the staph volatilomes by the presence versus absence of volatiles produced in each medium, we observed few differences. However, when the relative abundances of volatiles were included in the analyses, we observed that culturing staph in media containing free glucose (BHI and TSB) resulted in volatilomes dominated by acids and esters (67%). The low-glucose media (LB and MHB) produced ketones in greatest relative abundances, but the volatilome compositions in these two media were highly dissimilar. We conclude that the staphylococcal volatilome is strongly influenced by the nutritional composition of the growth medium, especially the availability of free glucose, which is much more evident when the relative abundances of the volatiles are analyzed, compared to the presence versus absence.  
Staphylococcus aureus; Staphylococcus epidermidis; volatile organic compounds; GC\(\times\)GC-TOFMS; catabolite repression control Article
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 1 Introduction
As a result, it is critical to develop a separation mechanism with a low mass ratio and a large enough volume to accommodate satellites with complex functions and numerous protrusions on their surface. In this study, we present and validate the design of the separation mechanism for the MF satellite through FEAs and prototype tests. The separation mechanism's design serves as a model for a variety of small and micro-nano satellite separation mechanisms. Especially, the separation mechanism has two distinct advantages over the conventional mechanism. First, the separation mechanism proposed in this paper is low-cost, lightweight, modular, and adaptable. It makes use of an integrated guide rail and a main bearing structure, which is simple to process and ensures the accuracy of the guide rail. The separation mechanism weighs less than 6 kg and is capable of supporting a satellite weighing up to 20 kg. Second, the separation mechanism is non-explosive and is capable of being reused multiple times on the ground. In this paper, we analyze the separation mechanism's task requirements and develop the separation mechanism's overall scheme and motion system in accordance with the task requirements. Then, to verify the design's rationality and reliability, the finite element model and dynamic model of the combination of the satellite and the separation mechanism were established. Finally, the rationality and reliability of the separation mechanism design were verified by comparing the simulation results with the prototype test results.
## 3 Results and Discussion
### Current Monitoring Capabilities  
Consider again the five current signals reported in Figure 2. Each curve corresponds to a nominal dose D\({}_{nom}\) of 4 Gy released at the entrance of the water-tank. Integrating the current of each signal, we calculated the charge per dose unit (sensitivity) and measured an average value of \(<\)Q\({}_{exp}\)/D\({}_{nom}\)? = 0.367 nC/Gy and a standard deviation of 0.005 nC/Gy.  
The expected theoretical sensitivity Q\({}_{Th}\)/D\({}_{nom}\) was calculated from the expression (1):  
\[\frac{\text{Q}_{Th}}{\text{D}_{nom}}=\frac{\text{N}_{p}\cdot q_{e}}{\text{D}_{ nom}}=\frac{\text{S}\cdot\text{q}_{e}}{(\frac{dE}{dx})_{60\text{ MeV}}} \tag{1}\]  
where S is the beam spot surface at isocenter (S = 5.31 \(\pm\) 0.21 cm\({}^{2}\)); \(\frac{dE}{dx}\)/0 MeV is the 60-MeV (energy at the isocenter point) proton massive stopping power in water (10.78 \(\pm\) 0.01 MeV\(\cdot\)cm\({}^{2}\)/g); D\({}_{nom}\) is the nominal absorbed dose in water at the entrance of the water phantom; N\({}_{p}\) is the total number of the incident protons; q\({}_{e}\) is the elementary charge unit.  
A value of Q\({}_{Th}\)/D\({}_{nom}\) = 0.393 \(\pm\) 0.020 nC/Gy was found, deviating 6.6% from the experimental \(<\)Q\({}_{exp}\)/D\({}_{nom}\)? result. This demonstrated the ability of the developed system to estimate the total charge received and, hence, of the dose absorbed at the entrance of the water tank. We quantitatively analyzed the SEM and Electrode signals, comparing their main characteristics: signal rise- and fall-time (defined as the time intervals needed to the signal for raising from 10% to 90% and for falling from 90% to 10% of its maximum, respectively), Full Width at Half Maximum (FWHM), and the curve integrals of each signal. Results are summarized in Table 2.  
Despite the rise-time differences, mainly due to the RC time constants of the Electrode front-end read-out circuit (see Section 2.1), the percentage difference between the FWHMs and total integrals was less than 0.02% and 0.4%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline Short-term reproducibility & \(\leq\)2\% \\ Mid-term reproducibility & \(\leq\)2.3\% \\ Long-term reproducibility & \(\leq\)4.3\% \\ \hline \end{tabular}
\end{table}
Table 1: Short-, mid-, and long-term reproducibility expressed as percentage variations of the detector sensitivities for the whole set of measurements.
## 1 Introduction  
Sample preparation is a crucial step in analytical methods for determining organic compounds. The isolation of the analytes from the matrix is a major task to ensure the quantification and unambiguous identification of such compounds [1]. Classical sample preparation techniques, such as liquid-liquid extraction (LLE) and solid-phase extraction (SPE), are usually time-consuming and labor-intensive. These techniques usually use large volumes of organic solvents, which are expensive and generate a considerable amount of waste that is harmful for human health and the environment [2].  
Microextraction techniques such as those based on sorbent microextraction and liquid-phase microextraction are considered of great importance, since they represent an environmentally friendly alternative to classical extraction methods [3]. There are different microextraction configurations and modes of use. Sorbent microextraction may be considered as an advanced and miniaturized solid phase extraction (SPE) technique. Solid phase microextraction (SPME) [4] and thin film microextraction (TFME) [5] belong to this category. Similarly, liquid phase microextraction (LPME) can be considered as miniaturized liquid-liquid extraction procedures [6]. Most LPME techniques used include dispersive liquid-liquid microextraction (DLLME) [7] and single drop microextraction (SDME) [8,9].  
In general, microextractions are carried out using an appropriate extraction phase, which can be a liquid [6] or a solid material [10], depending on the technique chosen. There is a large variety of extraction phases commercially available. However, in the last decade, efforts have been devoted to the development of new materials to be used as \"greener\" extraction phases. The green aspects of these alternative materials contribute to a less harmful and lower-cost analysis [3]. Furthermore, their usage reinforces the environmentally friendly character of microextraction techniques. In some specific cases, it increases selectivity and hence applicability for treating complex samples. For example, ionic liquids (ILs) and their tunable properties meet the criteria for extracting some compounds.  
Based on that, the aim of this work is to review the new materials used as green extraction phases for the determination of organic compounds by microextraction and chromatographic techniques. Furthermore, some recent applications of these materials in various matrices are presented.
# Blockchain-Based Distributed Patient-Centric Image Management System
## 5 Evaluation
### PCIM System Analysis
#### 5.3.2 Costs and Practicality  
In our proposed system, we define the actual transaction cost to be Ether = gas used \(\times\) gas price. Here, 'gas used' represents the constant computational cost. The gas price is adjusted by the network [6] to compensate for changes in the value of Ether. Thus, the total transaction cost (Ether) is kept relatively constant for the accessibility of health care data. As for the payer segment, every participant has to pay in gas for executing an operation in SC. Thus, the automated process of SC would cause significant cost savings for the patient.  
In the implemented PCAC-SC prototype, we set a gas limit of 30,000, where each unit of gas is set equal to 2 Gwei. The total transaction fee in this scenario is 0.11 USD. Table 1 summarizes the cost of the executed operations in SC. The _create_contract()_ function is implemented once with a cost of 0.025 USD. The _request_access()_ function cost is 0.093 USD, which is higher than that of other functions due to the additional input bytes included during the function execution, such as those corresponding to the patient blockchain address and notes for the usage agreement. The overall costs can be decreased further if the size of the input data is minimal. However, these costs are still lower than those associated with buying a storage space from a third-party service or maintaining a database using a centralized system such as ISN [16], MedBlock [22], MeDShare [23], and MedChain [24].