esa-sceva/satcom-chunk-collection huggingface dataset:
# Phenomenologies in Hypersphere Soliton and String Photon Models
## 2 Baryon Predictions in HSM
Note that in Prediction II we have finite charge radii, while in Prediction III we have infinite charge radii. Next, we discuss the evaluations of Prediction I. First, the six predicted values for \(\mu_{\Delta^{++}}\), \(\langle r^{2}\rangle_{M,I=0}^{1/2}\)\(\langle r^{2}\rangle_{M,I=1}^{1/2}\), \(\langle r^{2}\rangle_{M,I=1}^{1/2}\) (in addition to the input parameters \(M_{N}\) and \(\langle r^{2}\rangle_{M,p}^{1/2}\)) are within about 1% of the corresponding experimental data. Second, the three predictions for \(g_{A}\), \(\langle r^{2}\rangle_{E,I=0}^{1/2}\) and \(\langle r^{2}\rangle_{p}\) are within about 6% of the experimental values. Third, the three predictions for \(M_{\Delta}\), \(\mu_{p}\) and \(\langle r^{2}\rangle_{E,I=1}^{1/2}\) are within about 10% of the experimental values. Now we comment on the hypersurface \(A_{3}\) of the hypersphere \(S^{3}\) of radius parameter \(\lambda_{B}\), and the charge radius \(\langle r^{2}\rangle_{E,I=1}^{1/2}\) in (29). Exploiting the hyperspherical three metric in (2), we find that \(A_{3}\) can be analyzed in terms of three arc length elements \(\lambda_{B}d\mu\), \(\lambda_{B}\sin\mu d\theta\) and \(\lambda_{B}\sin\mu\sin\theta d\phi\), from which we find the three-dimensional hypersurface manifold with \(A_{3}=2\pi^{2}\lambda_{B}^{3}\). Note that \(\lambda_{B}\) is the radial distance from the center of \(S^{3}\) to the hypersphere manifold \(S^{3}\) in \(R^{4}\). In fact, inserting the value \(ef_{\pi}=(0.876\ {\rm fm})^{-1}\) in (32) into the condition \(L_{B}=1\) in (9), in the HSM we obtain the fixed radius parameter given by \(\lambda_{B}=\frac{1}{ef_{\pi}}=0.876\ {\rm fm}\). On the other hand, the charge radius \(\langle r^{2}\rangle_{E,I=1}^{1/2}\) is the physical quantity expressed in (29). Integrating over a relevant density on \(S^{3}\) corresponding to the integrand in (29), we evaluate \(\langle r^{2}\rangle_{E,I=1}\), which is now independent of \(\mu\), to yield a specific value of the electric isovector root mean square charge radius. The calculated charge radius then can be defined as the fixed radial distance to the point on a hypersurface manifold, which does not need to be located only on the compact manifold \(S^{3}\) of radius parameter \(\lambda_{B}\). This hypersurface manifold is now a submanifold in \(R^{4}\), which is located at \(r=0.80\ {\rm fm}\), far from the center of \(S^{3}\). Note that \(\langle r^{2}\rangle_{E,I=1}^{1/2}\) denotes the radial distance, which is a geometrical invariant giving the same value both in \(R^{3}\) (for instance, in volume \(R^{3}\), which contains the center of \(S^{3}\) and is described in terms of \((r,\theta,\phi)\) at \(\mu=\frac{\pi}{2}\)) and in \(R^{4}\). Next, the physical quantity \(\langle r^{2}\rangle_{E,I=1}^{1/2}\) calculated in \(R^{3}\) (and in \(R^{4}\)) then can be compared with the corresponding experimental value, similar to the other physical quantities such as \(M_{N}\) and \(M_{\Delta}\).
## 2 Advancements in Ultrafiltration for Protein and Peptide Separation
### Electroultrafiltration (EUF) and Electrodialysis with UF Membrane (EDUF)
Figure 3: Electrodialysis with UF membrane (EDUF) with six different configurations for (**a**) parallel fractionation of cationic and anionic peptides (single feed); (**b**) cationic and anionic peptides fractionation at basic (left) and acidic pH of feeds (right), respectively (single feed); (**c**) series fractionation by double UFM for anionic peptides in different compartment with single feed (left) or for anionic peptides in single compartment with dual feeds (right); (**d**) series fractionation by double UFM for cationic peptides in different compartment (single feed); (**e**) alternating anion-exchange membrane and UFM for acidic peptides fractionation (multiple feeds); (**f**) double CEM for cationic peptides fractionation (double feeds). CEM: cation-exchange membrane; A or AEM: anion-exchange membrane; UFM: UF membrane. Amidst all of the advantages in operating EDUF, water splitting, fouling, and vulnerable membrane integrity still obstruct the scale-up strategies. Despite having minor degradation in UFM, the protein fouling on the ion-exchange membrane cannot be ignored because the ion-exchange membrane is a nanoporous membrane [102]. In the same work, after six consecutive fractionations, a more debilitating AEM as resulted by electrochemical degradation (showed by increase in roughness) was detected since its diluted side generated a great amount of OH- ions. This phenomenon also turned up while EDUF operated at high current densities, provoking extreme basic solution in favor of alkaline hydrolysis, thermal degradation, and electrochemical deterioration [103]. Although generally H+ and OH- ions are developed by water splitting, the emergence of concentration polarization would expedite the process of coming across \"limiting current density\" on diluted side of ion-exchange membrane [104]. In this context, ion concentration is close to zero, so both H+ and OH- ions' mobility may shift away from the diluted part (electroconvection), which will reduce concentration polarization in one side, but will contrarily digress pH in all compartments and curtail membrane selectivity [32; 105].
## 1 Introduction
### State-of-the-Art 5G C-RAN Solutions  
The basic concept in the NG-RAN domain, which is promoted by 5 GPPP, is the multi-layered architecture of XAhaul [1,2]. This solution is based on an optical network that is designed to support traffic from various Split/Option interfaces. This architecture also includes D-RAN solutions, which means joint support for management of traffic from the fronthaul and backhaul. Based on this solution, the authors of publications [3, 4, 5] proposed network architectures based on optical and microwave (radio-line) transport. The presented solutions introduce traffic optimization, which, however, does not take into account the possibility of transmitting signals occurring in the A-RoF format in the network. The architecture of an Optical Transport Network (OTN) system is prepared for an efficient transport of digital traffic, which is provided at client access points. The situation is similar in the case of Time Shared Optical Network (TSON), which is a very good solution supporting the transport of information from radio-over-Ethernet (RoE) (enhanced common public radio interface (eCPRI) as well as next generation fronthaul interface (NGFI)) interfaces. A lot of research and development have been devoted to various optimization solutions for transportation systems of streams from D-RoF interfaces. The author reviewed the available studies [6, 7, 8, 9, 10, 11, 12, 13], which contained the results of studies showing the undoubted legitimacy of using the D-RoF technique. In these studies, particular emphasis was put on showing that a particular type of Split/Option or method of digitizing a radio signal to a bit form gives the opportunity to increase a link efficiency. There is no difference in the selection of the Split/Option method in the context of the load on the fronthaul network or the RRH unit. In special solutions, attempts were made to use compression methods when processing from analog to digital [14]; which, however--with a very large number of digital streams delivered to RRH working in massive-MMIO format--will not bring much efficiency. There remains the A-RoF solution with the least research, especially for 5G C-RAN applications. The papers [15, 16, 17, 18, 19] show a focus on specific solutions, which consist in conducting experiments documenting that radio signals in the BB, IF, and RF bands can be transmitted in optical fiber paths. These solutions were already tested many years ago. Several books have also been written on this subject [20,21]. In each of the presented experiments, however, no attention was paid to what generation of optical fibers will be used and to what extent future xWDM networks can be used to transport de facto analog signals. Particular attention of the author was caught by the study [22], which indicates the next field of activity, where the use or disposition of an optical fiber of the appropriate generation, located in the optical path, can be decisive in the selection of the radio signal transport system in its original form.
## 4 Fiber Sensing Technologies
### Temperature Measurement with Fiber Bragg Gratings and Microstructured Fibers  
Reliable high-temperature sensors are indispensable in some fields, such as tunnel monitoring, material processing, mine monitoring, structure health monitoring, oil well monitoring, and so on. Traditional electrical high-temperature sensors have some disadvantages, including low reliability, large temperature fluctuations, and danger of accidental fires. Optical fiber Bragg grating (FBG) sensors have numerous advantages over traditional electrical sensors, such as those inherent to fiber  
Figure 2: (**A**) Organic particle with cracks and dark reaction rims; (**B**) pyrolytic carbon; (**C**) iron oxide sphere.  
sensors in general, as well as the fact the measures (in this case temperature) are encoded in an absolute parameter, the optical wavelength [39]. Therefore, FBG sensors are the most appropriate sensors for monitoring applications in the fields mentioned above.  
A fiber Bragg grating is a periodic microstructure, typically a few millimeters in length, inscribed in the core of a fiber. It has the ability to reflect certain emission wavelengths and transmit others. The reflection wavelength of the FBG (Fiber Bragg wavelength, \(\lambda_{B}\)) depends on the grating characteristics (period, refractive index modulation amplitude) and is influenced by the ambient conditions such as strain and temperature. This allows the utilization of FBG as a sensor for strain and temperature. In the case of temperature sensing, the Bragg wavelength is a function of the temperature. This temperature dependence results from changes of the refractive index of the fiber as well as from thermal expansion of the glass material. The Bragg wavelength of the structure is given by \(\lambda_{B}=2n_{eff}\Lambda\), where \(n_{eff}\) is the effective index of refraction of the fiber guided mode and \(\Lambda\) is the period of index modulation. For a \(\Delta\)T temperature change the Bragg wavelength variation is given by:  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=(\alpha+\zeta)\Delta\mathrm{T},\]  
where \(\alpha\) is the coefficient of thermal expansion of the fiber and \(\zeta\) is its thermo-optic coefficient (dependence of the index of refraction on temperature).  
In the sensing system installed, FBGs were used for point measurements and co-located with the gas sensors for calibration purposes. Looking essentially to technology validation, a second type of temperature sensor was installed, namely an interferometric structure based on a microstructured optical fiber designed and fabricated within the project consortium [40].
## 2 Methodology
### _Chassis Modeling_  
The model of the double-layer shielding chassis containing hole and slit structures is shown in Figure 2, where the front of the chassis is the plane where the holes and slits are located parallel to the zoy-plane of the coordinate system; the total dimensions of the chassis are 170 mm \(\times\) 130 mm \(\times\) 60 mm; the thickness is 3 mm, in which the dimensions of the outer shielding cavity are 170 mm \(\times\) 50 mm \(\times\) 60 mm; and the dimensions of the inner shielding cavity are 170 mm \(\times\) 80 mm \(\times\) 60 mm. There are two rows of rectangular hole arrays on the inner and outer shielding cavities, and the size of each rectangular hole is 15 mm \(\times\) 7 mm. The three arrangements of the hole arrays of the inner shielding cavity are shown in Figure 3, which are \"Horizontal-Vertical\", \"Horizontal-Horizontal\", and \"Vertical-Vertical\". The hole array arrangement of the chassis and the chassis numbering are shown in Table 1. The observation point is set at the center of the inner shielding cavity. The electromagnetic wave is incident from the front of the chassis with aperture slits. The incident wave is a double-exponential pulse, which can be written as follows:  
\[E=AE_{0}[\exp(-at)-\exp(-bt)] \tag{3}\]  
where \(E_{0}\) = 50 kV/m, \(A=1.3\), \(a=1.0\times 10^{9}\) s, and \(b=1.5\times 10^{10}\) s; where \(E_{0}\) is the peak field strength, \(A\) is the peak pulse correction factor, and \(a\) and \(b\) are pulse parameters.  
For the numerical electromagnetic model, there are four types of media included in the calculation grid: vacuum, carbon fiber, nickel-plated carbon fiber, and stainless steel. The total number of grids is 190 \(\times\) 150 \(\times\) 80. The discrete grid in the FDTD calculation is \(\delta=1\times 10^{-3}\) m. As we know, the time step size is \(\Delta t=0.00167\) ns. The CPML absorbing boundary is employed in the FDTD calculation. The connecting surface boundary is \(-\)98:98 (x-direction); \(-\)78:78 (y-direction); and \(-\)43:43 (z-direction). The absorbing boundary is \(-\)108:108 (x-direction); \(-\)88:88 (y-direction); and \(-\)53:53 (z-direction).  
Figure 2: Diagram of chassis structure.
## 4 Results
### Dataset
#### 4.1.3 Data Enhancements  
To evaluate the rationality of the dataset, we conducted a statistical analysis on the number of annotated bounding boxes in each image and the freq.  
Contrast Enhancement: By adjusting the contrast of the images, we emphasized the features of the target objects, thereby increasing the model's sensitivity to object detection. We appropriately enhanced the contrast of the training images to better capture the details of the target objects.  
Noise Addition: We added random noise to the training images to simulate the noise interference that may occur in real-world scenarios. This helps improve the model's robustness and generalization ability in noisy environments.  
Brightness Adjustment: We adjusted the brightness of the training images to simulate different lighting conditions. This helps improve the model's performance under varying lighting conditions.  
Flipping: We horizontally and vertically flipped the training images to increase the number of samples for target objects from different perspectives. This aids in enhancing the model's ability to recognize target objects from different viewpoints.  
Figure 6: Statistical analysis of annotated bounding boxes and category frequency in the sampled dataset.  
Rotation: We randomly rotated the training images at various angles to increase the number of samples for target objects from different orientations. This helps improve the model's ability to recognize target objects from different directions.  
Through the five data augmentation methods mentioned above, we effectively enhanced the diversity of the training set, which contributes to improving the generalization capability and performance of the object detection model. In the experimental section, we will demonstrate the specific impact of these data augmentation strategies on model performance. The effects of the five data augmentation methods are shown in Figure 7.
## 3 Results and Discussion
### Variations of Agro-Morphological Traits
#### 3.1.1 Qualitative Agronomical Traits
As for the lodging rate, the majority of barley accessions (81%) and all control cultivars experienced no lodging (0%). Although no accessions exhibited severe lodging rates (>81%), two accessions, WIR92 from Mongolia and CI 6221 from Turkey, were susceptible to lodging (61-80%). Obtaining high yields in barley requires resistance to cold damage and lodging, and hence, those accessions with high resistance to cold damage and minimal lodging rates could be valuable resources [33; 34]. Barley genotypes can be classified into two categories based on their spike morphology: two-row and six-row types. Studies have shown that such spike morphological variation is attributed to the dominance of a specific gene called _vrs1_ in the two-row spike type [35]. Despite their distinct differences, both spike types possess unique characteristics and have been extensively utilized in breeding programs [36; 37; 38]. In this study, approximately 37% of the barley accessions were found to be two-row, while 63% were six-row, like all the control cultivars. Another classification of barley is based on its grain type, which can either be hullless (naked) barley without a husk or hulled (covered) barley with a husk [39]. It was noted that 67% of the barley accessions belonged to the hulled type, while the remaining 33% were hullless. Among the control cultivars, only Heukdahyang was identified as hullless, with the rest being hulled. Once again, the diversity observed in spike morphology and grain type among the barley accessions could help in selecting desirable genotypes for future investigations. Researchers have suggested that the existence or non-existence of a husk in barley might have a significant influence on the concentrations of metabolites across various genotypes [40; 41].
# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 2 Methods  
### Data Processing and Classification  
Thermal sensation prediction based on wearable sensors can be considered as a classification machine learning problem, the input of which is the set of extracted features from the measured variables and the output is the subjects' feedback with the standard thermal sensation labels. Several machine learning techniques can be used for such a problem. Support vector machines (SVMs) is one of the efficient classification techniques used in different relevant studies [21; 22; 23; 25; 26]. In this study, the least squares support vector machine (LS-SVM) is proposed to be used for general models as it is as powerful as standard SVMs, but, it has less computational cost [30]. Most, if not all, relevant studies of thermal sensation prediction rely on global general models. Global models are models that are trained using the whole available training dataset with a uniform weight (i.e., all training points are equally contributing to the training process). However, global models are not that efficient for online classification and streaming analytics applications in which a stream of new data is collected from subjects via wearable technology, especially when aiming at personalized models. Hence, for this purpose, we suggest a localized version of LS-SVM, namely K-Nearest Neighbours (KNN)-LS-SVM [31] to be compatible with the wearable sensors for online and streaming analytics.  
The classification problem of thermal sensation is a multiclass classification problem, the input variables of this problem from which features are extracted are: aural temperature \(T_{er}\), average skin temperature \(\overline{T}_{sk}=\frac{1}{3}\big{[}T_{scap}+T_{ch}+T_{arm}\big{]}\), ambient temperature \(T_{a}\), chest skin temperature \(T_{ch}\), heart rate \(H_{r}\), average heat flux from the skin \(\overline{q}_{sk}\), temperature gradient between core and skin \(\Delta T_{sk}=T_{er}-\overline{T}_{sk}\), age, gender, body mass index \(BMI\), metabolic rate \(M_{r}\). As some variables are time measurements of the different parameters, the process of feature extraction is applied to a specified time window from the recordings, namely the last five minutes preceding the sensation labeling by the test subjects. The extracted features from time-variant variables are: minimum (_min_), maximum (_max_), variance (_var_),energy, time-derivate (\(\frac{d}{dt}\)), root mean square (\(rms\)). Target labels are the seven classes of the standard thermal comfort sensation scores: _Cold_ (-3), _Cool_ (-2), _Slightly Cool_ (-1), _Neutral_ (0), _Slightly Warm_ (1), _Warm_ (2), and _Hot_ (3).
## 4 Experiments
### Evaluation Metrics
The Kappa score ranges from \(-1\) to 1, where 1 indicates perfect agreement, 0 corresponds to agreement by chance, and negative values reflect systematic disagreement. A higher Kappa value suggests stronger alignment between model predictions and ground truth labels beyond random expectations. The Matthews Correlation Coefficient (MCC) offers a comprehensive metric by incorporating all four confusion matrix elements, _TP_, _TN_, _FP_, and _FN_, into a single correlation-based score. Unlike metrics such as Accuracy or F1-Score, which may be influenced by class imbalance, MCC remains balanced and reliable even under highly disproportionate class distributions. The definition of MCC is defined as follows:  
\[\text{MCC}=\frac{TP\cdot TN-FP\cdot FN}{\sqrt{(TP+FP)(TP+FN)(FN+TN)(FP+TN)}} \tag{4}\]  
MCC values range from \(-1\) to 1, where 1 indicates perfect prediction, 0 denotes no better than random guessing, and \(-1\) reflects complete disagreement. This metric is particularly advantageous for binary classification tasks in imbalanced datasets, as it provides a balanced evaluation of all types of classification errors. To assess the statistical robustness of these performance metrics, 95% Confidence Intervals (CI) are computed using bootstrap resampling for F1-Score, Accuracy, Kappa, and MCC. These intervals estimate the range within which the true metric value is expected to lie with high probability, thereby helping to assess whether observed differences between models are statistically significant rather than random chance. Specifically, bootstrap sampling with 1000 iterations on the evaluation is performed to construct the CIs and determine whether observed performance differences are statistically significant or attributable to random variation.
# Microbiota \"Fingerprint\" of Greek Feta Cheese through Ripening
## 5 Conclusions  
The present study is, to our knowledge, the first attempt to characterize the core microbiota of Feta cheese. Microbiological analysis revealed a variety of microorganisms, most of them belonging to the Lactic Acid Bacteria group. MALDI-TOF MS is a powerful tool for microbiological identification with adequate accuracy and sensitivity to the level of species. It requires less preparation time than conventional or molecular methods, is faster and cost effective. Between the third and the sixth month of ripening, statistically significant changes were observed in chemical parameters (PH, protein, total solids), although minor microbiological changes were detected in species level. The unique sensory properties, nutritional value, authenticity, quality, and safety of Feta cheeses are believed to be attributed to the microbial species present, their dynamics, and the functional interactions concerned. The results of this study contribute to a better understanding of the microbiota of Feta cheese and determine the core microbiota from which Feta cheese consists of. The study of Feta cheese microbiota is an important tool for development of its processing technology, leading in improvements not only in the quality characteristics, but mainly in unique identification markers.  
Conceptualization, A.T.; Methodology, A.T. and A.N.; Validation, I.S.; Formal Analysis, A.N. and E.B.; Investigation, A.T., C.V. and A.N.; Resources, I.S.; Data Curation, A.T., A.N. and E.B.; Writing-Original Draft Preparation, A.N., C.V., G.R.; Writing-Review & Editing, G.F., G.R., G.T., E.B. and I.S.; Visualization, A.N.; Supervision, I.S.; Project Administration, I.S.; Funding Acquisition, I.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project \"FoodOmicsGR_RI Comprehensive Characterization of Foods\" (MIS 5029057) which is implemented under the Action \"Reinforcement of the Research and Innovation Infrastructure\", funded by the Operational Programme Competitiveness, Entrepreneurship and Innovation (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.