esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The main task of the article was to define a method to test a system that is provable and complete (releasable in the open-world context) based on the Kolmogorov entropy analysis of the Euler-Lagrange equation, boundary conditions, and the experience of the detection knowledge.  
The main issue of the article is as follows:  
The completeness of the detection system describing a traffic node could be realized if the entropy-based error-doubling time of the trajectories of the Euler-Lagrange equation, interpreted at the transport junction, is less than 1.3. The error doubling is defined as follows (19):  
\[T_{2}=\frac{ln2}{K_{2}}<1.3 \tag{19}\]  
where \(K_{2}\) is the Kolmogorov entropy of the trajectory. The analysis of the error doubling is equivalent to testing the solvability of the Euler-Lagrange system.  
The advantages of the method are as follows:  
1. It helps the designer build a system that is consistent, complete, and releasable.
2. Due to the application of the variation principle, the created system becomes extreme, i.e., we can find the minimum or maximum of all existing systems.
3. The previous know-how, which means detection, perception, classification, etc., can be integrated into the system.
4. The system can be easily expanded (more cars, boundaries, sensor sets for detection, and the complexity of the node can be changed), and then all we have to do is solve the partial differential equation to see whether the completeness or provability of the system exists. If not, what decisions need to be made to ensure completeness.  
We have presented tests of extensive real traffic scenarios ((1) urban traffic, (2) urban highway, (3) freeway, (4) minor road, (5) mountain road, and (6) construction area on a highway) and found that the Kolmogorov entropy is an excellent tool to characterize the completeness of the traffic situation in real-time embedded environments. The outliers observed in Figure 4 show that in this case, the information content of the system is not sufficient, and another additional sensor needs to be activated.  
The next steps in modeling are as follows:  
1. Recording the traffic dynamics of the selected traffic node.
2. Constructing the Euler -Lagrange system and using it to calculate the completeness.
3. Creating a small-scale model and sensor set and performing traffic experiments.
4. Comparing and scaling the results.  
Conceptualization, G.K. and P.B.; methodology, P.B.; software, P.B.; validation, G.K. and P.B.; formal analysis, G.K.; investigation, P.B.; resources, P.B.; data curation, G.K.; writing--original draft preparation, P.B.; writing--review and editing, G.K.; visualization, P.B.; supervision, G.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Analysis of Lower Limb Performance Determinants in Sport Climbing
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Test**} & **Intermediate (_n = 11_)** & **Advanced (_n = 13_)** & **Difference (\%)** & _p-Value_ \\ \hline SJ (cm) & 27.0 \(\pm\) 4.0 & 29.8 \(\pm\) 6.5 & \(-\)10.5 & 0.227 \\ CMJ (cm) & 30.7 \(\pm\) 4.2 & 32.9 \(\pm\) 6.6 & \(-\)7.3 & 0.341 \\ DJ (cm) & 28.9 \(\pm\) 4.2 & 32.3 \(\pm\) 6.6 & \(-\)11.5 & 0.166 \\ Power (W) & 2912.8 \(\pm\) 641.2 & 2894.2 \(\pm\) 661.7 & 0.6 & 0.945 \\ Power (W/kg) & 42.5 \(\pm\) 4.1 & 44.0 \(\pm\) 5.9 & \(-\)3.5 & 0.495 \\ Power (W/FFM) & 50.2 \(\pm\) 3.8 & 50.9 \(\pm\) 6.1 & \(-\)1.4 & 0.737 \\ DS left (\({}^{\circ}\)) & 45.9 \(\pm\) 6.2 & 45.8 \(\pm\) 8.8 & 0.3 & 0.968 \\ DS right (\({}^{\circ}\)) & 47.2 \(\pm\) 3.5 & 45.4 \(\pm\) 8.4 & 3.9 & 0.509 \\ Sit and Reach (cm) & 9.7 \(\pm\) 11.6 & 9.2 \(\pm\) 6.7 & 4.9 & 0.901 \\ LFM (cm) & 77.2 \(\pm\) 6.7 & 75.8 \(\pm\) 6.6 & 1.8 & 0.619 \\ RFM (cm) & 78.5 \(\pm\) 7.6 & 76.9 \(\pm\) 6.8 & 2.1 & 0.588 \\ Hop Test Left (m) & 1.7 \(\pm\) 0.2 & 1.6 \(\pm\) 0.3 & 6.7 & 0.248 \\ H3 JL (m) & 5.5 \(\pm\) 0.6 & 5.3 \(\pm\) 0.9 & 3.5 & 0.572 \\ H3 JR (m) & 5.4 \(\pm\) 0.7 & 5.4 \(\pm\) 0.8 & 0.1 & 0.989 \\ Pull-ups (rep.) & 8.6 \(\pm\) 4.1 & 13.0 \(\pm\) 5.8 & \(-\)50.5 & 0.047 \\ Handgrip strength (kgf) & 42.9 \(\pm\) 9.8 & 45.6 \(\pm\) 8.7 & \(-\)6.3 & 0.482 \\ Relative handgrip strength & 0.6 \(\pm\) 0.1 & 0.7 \(\pm\) 0.1 & \(-\)11.6 & 0.036 \\ (kgf/fat-free mass) & & & & \\ \hline \end{tabular} Source: values expressed as mean \(\pm\) standard deviation; SJ, squat jump; CMJ, counter movement jump; DJ, drop jump; FFM, fat-free mass; DS, dorsification; LFM, left foot mobility; RFM, right foot mobility; H3 JL, triple Hop Test left; H3 JR, triple Hop Test right. Kgf, force in kilograms.  
\end{table} 
Table 3: Comparison of performance variables between climbing level. Finally, a Pearson correlation analysis was performed to explore the relationship between climbing level, measured by the IRCRA scale, revealing significant correlations with years of experience (r = 0.433, \(p\) = 0.034), body fat percentage (r = \(-\)0.415, \(p\) = 0.043), number of pull-ups (r = 0.598, \(p\) = 0.002), as well as handgrip strength relative to body weight (r = 0.484, \(p\) = 0.016) and to fat-free mass (r = 0.412, \(p\) = 0.046). In contrast, no significant correlations were found with lower limb-related variables, including vertical jump height (S): r = 0.347, \(p\) = 0.097; CMJ: r = 0.354, \(p\) = 0.090; DJ: r = 0.363, \(p\) = 0.081), or with horizontal jump tests (Hop3 Test left: r = 0.197, \(p\) = 0.356; Hop3 Test right: r = 0.330, \(p\) = 0.116). Similarly, flexibility as assessed by the sit-and-reach test showed no association with climbing performance (r = \(-\)0.159, \(p\) = 0.458).
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 4 Solutions and Algorithms
### Decomposition Strategy
#### 4.2.1 Battery Management  
For the optimization of the BMO problem, we aim at minimizing the first part in (17), i.e.,  
\[\text{BMO}: \min_{\rho_{i}(t)}\sum_{i=1}^{M}\rho_{i}(t)(\tau-\omega_{i}(t))(E_{i }(t)-\mathcal{B})\] \[s.t. (\ref{eq:BMO_1}),\ (\ref{eq:BMO_2}).\]  
According to constraint (11), we have \(E_{i}(t)-\mathcal{B}\leq 0\), and \(\tau-\omega_{i}(t)\) is a constant in each time slot due to the periodic motion of the celestial bodies. Note that it is pointless to optimize \(\rho_{i}(t)\) when the satellite is in shadow during time slot \(t\). Thus, the optimal solution can be obtained by maximizing energy harvest rate \(\rho_{i}(t)\) when \(0<\tau-\omega_{i}(t)\). In other words, the RSN should harvest energy from the sun as much as possible until the battery capacity is full. The optimal solution \(\rho_{i}^{*}(t)\) can be obtained as follows  
\[\rho_{i}^{*}(t)=\min\bigl{\{}(\mathcal{B}-E_{i}(t))/(\tau-\omega_{i}(t)),\rho_ {i}(t)\bigr{\}}. \tag{18}\]
# Intra-Reliability of a Wearable Near-Infrared Sensor for Monitoring the Intensity of Exercise
## 2 Materials and Methods
### Measurements of Muscle Oxygen  
During the SIET, local muscle oxygen saturation (SmO\({}_{2}\)) was measured by a portable near-infrared spectroscopy device, Q-LAC (Oxymotion, Lublin, Poland), with a sampling frequency 8 Hz. It was attached firmly to the belly of the vastus lateralis (VL) muscle of the dominant leg. The device was approximately 15 cm above the proximal border of the patella (see Figure 1). This muscle was selected based on previous research and considering the role of this muscle in cycling exercise [4; 20]. The system uses the light waves (600-900 nm) from light-emitting diodes into the tissue. It has two emitters to each detector spacing at 15 and 30 mm. The sensor was attached in the special supporting thigh sleeve with a pocket on the sensor, which allows direct absorption of light into the tissue. The data were recorded continuously by software version 1.2.5 (Oxymotion, Lublin, Poland),which calculated the muscle oxygen saturation from the following equation: SmO\({}_{2}\) = 100 \(\times\) (O\({}_{2}\)Hb)/(O\({}_{2}\)Hb + HHb), where O\({}_{2}\)Hb is oxyhemoglobin and HHb is deoxyhemoglobin. For further analysis, values were registered at intensities of 50, 100, 150, 200, and 250 W. The data show differences between resting and exercise values. This approach includes the assumption that the most important marker is an amplitude of change in local tissue saturation compared to the baseline level. It means that the absolute value of tissue saturation should be interpreted as the delta of value changes with the assumption that the initial value is constant. This method demonstrates muscle responses on exercise intensity by measurement changes of oxyhemoglobin, deoxyhemoglobin, total hemoglobin, and intensity zones referring to concentration of lactate.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
###### Abstract  
Non-orthogonal multiple access (NOMA) technologies are considered key technologies for terrestrial 5G massive machine-type communications (mMTC) applications. It is less known that NOMA techniques were pioneered about ten years ago in the satellite domain to match the growing demand for mMTC services. This paper presents the key features of the first NOMA-based satellite network, presenting not only the underlying technical solutions and measured performance but also the related deployment over the Eutelsat satellite fleet. In particular, we describe the specific ground segment developments for the user terminals and the gateway station. It is shown that the developed solution, based on an Enhanced Spread ALOHA random access technique, achieves an unprecedented throughput, scalability and service cost and is well matched to several mMTC satellite use cases. The ongoing R&D lines covering both the ground segment capabilities enhancement and the extension to satellite on-board packet demodulation are also outlined. These pioneering NOMA satellite technology developments and in-the-field deployments open up the possibility of developing and exploiting 5G mMTC satellite- and terrestrial-based systems in a synergic and interoperable architecture.  
satellite communications; massive machine type communications; Internet of Things; non orthogonal multiple access; random access 20212120215 June 202123 June 202123 June 202123 June 202123 June 2021
# Analysis and Reduction of Nonlinear Effects in Optical Fiber Frequency Transfer
## 3 Simulation and Results
### Raman Scattering
Specifically, with gain coefficients of 5 \(\times\) 10\({}^{-13}\) m/W for Raman scattering, the threshold power for forward Raman scattering is approximately 0.7 W, which is 0.2 W less than that for backward Raman scattering. In this study, we compare the research on the threshold powers of Brillouin scattering and Raman scattering in high-precision optical fiber frequency transfer with previous works [11; 12; 13; 14; 15; 16; 17; 18] (Table 1). However, unlike these works, our study focuses on investigating the Brillouin scattering threshold power and Raman scattering threshold power in detail. We also examine the influence of various parameters on these threshold powers. This research is crucial for long-distance and high-precision fiber optical frequency transfer. For instance, in the \"high precision ground-based timing system\" of the national major scientific and technological infrastructure plan [12; 13], the attenuation coefficient \(\alpha\) of the optical fiber in the field differs from the attenuation coefficient in the laboratory, which can impact the threshold powers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Research Team**} & \multicolumn{3}{c}{**Brillouin Scattering Threshold Power**} & \multirow{2}{*}{**Raman Scattering Threshold Power**} \\ \cline{3-3} \cline{5-6}  & & \(L\) & \(\gamma_{B}\) & \(\alpha\) & \(\Delta v_{laser}\) & \\ \hline
1 & Ref. [11] & & No & & No & \\ \hline
2 & Ref. [18] & & No & & No & \\ \hline
3 & Refs. [14; 15] & & Yes & No & Yes & No \\ \hline
4 & Refs. [16; 17] & & Yes & & No & \\ \hline
5 & Refs. [12; 13] & & No & & Yes & Yes \\ \hline
6 & This work & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of research on the threshold powers of the Brillouin scattering and Raman scattering in the high-precision optical fiber frequency transfer. Figure 7: The threshold power for generating the Raman scattering as a function of the attenuation coefficient \(\alpha\) of the optical fiber and the gain coefficient \(\gamma_{R}\) of the Raman scattering. (**a**) The attenuation coefficient \(\alpha\) based on the gain coefficient \(\gamma_{R}\) = 4.2 \(\times\) 10\({}^{-13}\) m/W and the effective mode area \(A_{eff}\) = 1.0 \(\times\) 10\({}^{-10}\) m\({}^{2}\). (**b**) The gain coefficient \(\gamma_{B}\) based on the attenuation coefficient \(\alpha\) = 0.2 dB/km and the effective mode area \(A_{eff}\) = 1.0 \(\times\) 10\({}^{-10}\) m\({}^{2}\).
# Numerical Estimation of Bending in Holographic Volume Gratings by Means of RCWA and Deep Learning
## 2 Theory
### FNNs and CNNs Theory Foundations  
Deep learning (DL), a subset of machine learning encompassing all neural network architectures, has proven to be a revolutionary field with applications that impact a wide range of scientific and technological areas [18; 35; 36]. These algorithms mimic the structure and function of the human brain and are used to recognize patterns for supervised, semi-supervised, or unsupervised purposes. The most significant distinction of DL algorithms compared to traditional machine learning algorithms is that DL algorithms autonomously learn hierarchical representations of data, eliminating the need for hand-crafted feature engineering to transform the data into a more suitable form.  
Figure 2: Flow chart of the RCWA-SM method proposed.  
All artificial neural networks are composed of interconnected nodes, or neurons, organized into layers. FNNs are the simplest form of neural networks, consisting of an input layer, an output layer, and one or more hidden layers in between, as illustrated in Figure 3a. Each hidden layer is essentially a matrix and an activation function that introduces nonlinearity to the system, which is a fundamental trait of neural networks. The components of these matrices are called weights, and they are updated iteratively to minimize the loss function between the model and the target outputs, typically using a gradient descent-based optimization algorithm [18; 19; 35; 36].  
On the other hand, a one-dimensional CNN hidden layer consists of several filters or kernels that slide along the input or preceding layers sequence, as shown in Figure 3b, with its subsequent activation function [35; 37]. The input data are typically a one-dimensional array featuring some spatial auto-correlation, such as a time series, a sequence of words in natural language processing or, in our case, a diffraction pattern. This allows kernels to extract the underlying patterns derived from that spatial auto-correlation of the input. In this case, the weights consist of the components of the convolutional kernels, which are updated in a completely analogous fashion to the FNNs mentioned above.  
FNNs and CNNs differ fundamentally in how they handle data. FNNs treat individual data points as separate, fixed units, while CNNs are designed to capture local correlations, spatial hierarchies, and patterns within data through their sliding convolutional kernels. This makes CNNs ideal for image and signal processing, in which the spatial and temporal dependencies are present and critical. By optimising their kernel weights through backpropagation and gradient descent, CNNs can improve their ability to generalise spatial or temporal auto-correlated data predictions.  
Given our objective of predicting the bending polynomial coefficients and other fringe parameters from spatially auto-correlated experimental diffraction efficiency patterns, it is essential to consider the inclusion of CNN layers in our deep learning solution. This study thoroughly evaluates their suitability, as discussed in the present work.
# Numerical Study of an Ultra-Broadband All-Silicon Terahertz Absorber
## 1 Introduction
In 2019, [PERSON] et al. designed a dielectric water-based metamaterial absorber that realized absorption above 90% from 8 to 20 GHz [28]. In this study, the perfect absorption mechanism is explained based on electric and magnetic resonances excited in the diamond metamaterial layer, based on free-space impedance that results in the dissipation of large amount of energy at resonance. The resonant frequencies are related to the sizes of the structure and can be tuned by adjusting the length and the thickness of the diamond layer. More importantly, even though the resonant frequency changes, the absorption remains high. In addition, our proposed structure has the characteristics of polarization independence and broadband absorption. In the case of transverse electric (TE) polarization, the absorber can remain in a high-absorption state from 0 to 70\({}^{\circ}\), but in the case of transverse magnetic (TM) polarization, it can only remain in the range of 0 to 40\({}^{\circ}\). Compared with other absorbers listed in Table 1 with the structure of VO\({}_{2}\)-SiO\({}_{2}\)-Au [29], cross-shaped graphene-SiO\({}_{2}\)-Au [30], graphene disks-SiO\({}_{2}\)[31], all-silicon V-groove [32] et al, the all-silicon diamond absorber can also achieve a broad absorption band with an absorption efficiency \(>\)90%.
## 3 Results  
### Materials Characterisation  
Table 1 shows content of identified mineral species by XRD analysis. The most abundant species were quartz (38.9%) and muscovite (49.6%). There was no presence of crystalline ZnS. Zn was only present as stannite and probably in the form of amorphous minerals formed by alteration of ZnS.  
Table 2 shows chemical composition of RD. It is important to note the content of Zn (1.38%), Pb (2.18%), and Cu (0.0435%). These metals were lower than exploitable concentrations in ores. Table 3 lists the background ranges and maximum allowable limits of some trace elements in the soils of the Region of Murcia (Spain) [29]. The background or baseline levels of an element or a substance are the natural soil concentrations in an area and therefore, pollution occurs when as a result of human activity an element or a substance is greater than those levels with a net detrimental effect on the environment and its components [30]. According to this concept, different countries/regions determine the regulatory standards over which a soil can be considered contaminated for different uses, including agricultural, industrial, or urban.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Element** & **Content (wt\%)** \\ \hline O & 39.37 \(\pm\) 0.12 \\ Si & 20.62 \(\pm\) 0.12 \\ Al & 8.32 \(\pm\) 0.10 \\ Fe & 9.07 \(\pm\) 0.12 \\ S & 1.94 \(\pm\) 0.04 \\ K & 2.10 \(\pm\) 0.07 \\ Pb & 2.18 \(\pm\) 0.07 \\ Zn & 1.38 \(\pm\) 0.05 \\ Ca & 0.344 \(\pm\) 0.17 \\ Mg & 0.283 \(\pm\) 0.014 \\ Ti & 0.252 \(\pm\) 0.013 \\ Na & 0.202 \(\pm\) 0.06 \\ As & 0.151 \(\pm\) 0.0075 \\ P & 0.0339 \(\pm\) 0.0017 \\ Ba & 0.0518 \(\pm\) 0.0048 \\ Cu & 0.0435 \(\pm\) 0.0022 \\ Sn & 0.0424 \(\pm\) 0.0021 \\ Mn & 0.0293 \(\pm\) 0.0015 \\ Zr & 0.0258 \(\pm\) 0.0013 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of RD sample.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mineral Specie** & **Content (\%)** \\ \hline Musovite & 49.6 \(\pm\) 0.1 \\ Quartz & 38.9 \(\pm\) 0.9 \\ Corkite & 6.9 \(\pm\) 0.5 \\ Calcite magnesium & 3.5 \(\pm\) 0.1 \\ Litharge & 0.5 \\ Stannite & 0.4 \\ Calcite & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: XRD analysis of RD sample.  
Table 4 summarizes the physicochemical properties of the carbon-based materials (AC and VC) used in the treatment of RD. Two commercial carbon materials showed high C content (>80%).
## 2. Aptamers as Biorecognition Element
The performances in the detection of Immunoglobulin E and Cellular Prions using immunosensors or aptasensors were reviewed by [PERSON] [13]. The author concluded that, even if the performances are similar when the aptamers exhibit comparable or higher affinity to a specific target, they are more favorable for biosensor development than antibodies due to their higher stability, reproducibility, and ruggedness. Recently, [PERSON] and coworkers highlighted the higher performances of aptamers, with respect to antibodies, in biosensors intended for high-throughput screening of target molecules [14]. The authors reviewed many articles covering different medical applications, from cancer to cardiovascular disease biomarkers, from Neurotransmitters and Alzheimer's to Tuberculosis disease biomarkers. The reported examples confirmed that the applications of aptamers as MRE are continuously growing, and this also favors their development. Besides antibodies, another widely used MRE is represented by molecularly imprinted polymers (MIPs), also called \"synthetic antibodies\", that are specifically designed for binding targets with a high degree of selectivity and reproducibility [15]. The most important features for a biosensor are selectivity, reproducibility, stability, sensitivity, and reusability [16; 17]; consequently, an efficient MRE has to satisfy specific characteristics to allow the achievement of the aforementioned features. Figure 1 reports a comparison between the different MREs based on their most significant characteristics able to satisfy the biosensors' requirements [16; 18]. Compared with the antibodies, MIPs and aptamers are characterized by higher stability and reproducibility and smaller molecular size while maintaining high specificity. Moreover, MIPs are characterized by a low cost compared to the other two bio-receptors, but aptamers, besides the other characteristics, have the smallest size and the highest sensitivity. [PERSON] and [PERSON] drew a road map on how to choose an MRE for the development of a biosensor [17]: the choice is mainly related to the request of selectivity and sensitivity of the biosensor itself. (POC) diagnostic system [26; 27; 28], for the detection of bacteria [29; 30] or circulating tumor cells [31]. Since 1996, aptamers have been suggested as an alternative to antibodies [32; 33] and employed in the biosensor field, highlighting their potentiality and versatility. Some diagnostic products based on aptamers are already present on the market [34; 35; 36]. Recently, besides SELEX, different aptamer selection systems have been envisaged, based either on computational \"in silico\" systems, which calculate the probability of interaction between nucleic acid sequences and targets using molecular dynamics [9] or systems based on \"machine learning\" [37]. These new approaches increase the aptamer development and, consequently, also their employment in the biosensors field.
# Action of Antimicrobial Peptides against Bacterial Biofilms
## 2 Antimicrobial Peptides
### Mechanism of Action of AMPs against Planktonic Bacteria  
The mechanism of action of AMPs usually starts by interacting with negatively charged moieties such as lipopolysaccharidesaccharides (LPS) in the outer membranes of Gram-negative bacteria and lipoteichoic acid (LTA) in the cell wall of Gram-positive bacteria [69; 70; 71]. Once AMPs cross or produce pores in the outer membrane or the cell wall of bacteria, disruption of cytoplasmic membranes occurs followed by cell lysis [72]. The mechanisms of action of AMPs have been divided into pore-forming and non-pore models [73]. Pore-forming models include the barrel stave and the toroidal pore models. Non-pore models include the carpet model. AMPs can also inhibit the synthesis of cell walls, DNA, RNA and protein, and activate enzymes such as autolysins that induce autolytic death [66; 74; 75].