esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{tabular}{c c c c c} \hline \hline
**Condition** & \begin{tabular}{c} **MBV** \\ **(m/s)** \\ \end{tabular} & \begin{tabular}{c} **g** \\ **(95\% CI)** \\ \end{tabular} & \
\begin{tabular}{c} **PBV** \\ **(m/s)** \\ \end{tabular} &
\begin{tabular}{c} **g** \\ **(95\% CI)** \\ \end{tabular} \\ \hline \multicolumn{5}{c}{**50\% 1 RM \({}^{\mathbf{a,b,c}}\)**} \\ \hline TRAD & 0.80 \(\pm\) 0.07 & - & \(1.27\pm 0.14\) & - \\ AEL-MAX & 0.78 \(\pm\) 0.07 & \(-0.18\) & \(1.25\pm 0.14\) & \(-0.22\) \\ AEL-SUPRA & 0.77 \(\pm\) 0.07 & \(-0.17\) & \(1.24\pm 0.15\) & \(-0.07\) \\  & (\(-0.91\)–\(0.58\)) & \(1.24\pm 0.15\) & \((-0.81\)–\(0.67)\) \\ \hline \multicolumn{5}{c}{**60\% 1 RM \({}^{\mathbf{a,b}}\)**} \\ \hline TRAD & 0.70 \(\pm\) 0.07 & - & \(1.18\pm 0.12\) & - \\ AEL-MAX & 0.69 \(\pm\) 0.07 & \(-0.16\) & \(1.18\pm 0.12\) & \(-0.01\) \\  & (\(-0.90\)–\(0.58\)) & \(1.18\pm 0.12\) & \((-0.73\)–\(0.75)\) \\ AEL-SUPRA & 0.70 \(\pm\) 0.07 & \(0.07\) & \(1.17\pm 0.13\) & \(-0.08\) \\  & (\(-0.67\)–\(0.82\)) & \(1.17\pm 0.13\) & \((-0.82\)–\(0.66)\) \\ \hline \multicolumn{5}{c}{**70\% 1 RM \({}^{\mathbf{a}}\)**} \\ \hline TRAD & 0.62 \(\pm\) 0.07 & - & \(1.12\pm 0.11\) & - \\ AEL-MAX & 0.59 \(\pm\) 0.08 & \(0.05\) & \(1.09\pm 0.14\) & \(-0.26\) \\  & (\(-0.69\)–\(0.79\)) & & \((-1.00\)–\(0.48)\) \\ AEL-SUPRA & 0.61 \(\pm\) 0.06 & \(0.19\) & \(1.10\pm 0.13\) & \(-0.12\) \\  & (\(-0.55\)–\(0.93\)) & & \((-0.86\)–\(0.62)\) \\ \hline \multicolumn{5}{c}{**80\% 1 RM**} \\ \hline TRAD & 0.49 \(\pm\) 0.06 & - & \(1.02\pm 0.12\) & - \\ AEL-MAX & 0.47 \(\pm\) 0.07 & \(-0.11\) & \(-0.99\pm 0.12\) & \(-0.19\) \\  & (\(-0.85\)–\(0.63\)) & & \((-0.93\)–\(0.55)\) \\ AEL-SUPRA & 0.47 \(\pm\) 0.08 & \(0.19\) & \(1.00\pm 0.11\) & \(-0.14\) \\  & (\(-0.56\)–\(0.93\)) & & \((-0.88\)–\(0.60)\) \\ \hline \end{tabular}  \% 1 RM based on the concentric load used during the movement; MBV = mean barbell velocity; PBV = peak barbell velocity; \({}^{\mathbf{a}}\) = significantly different from values at 80% 1 RM (\(p<0.001\)); \({}^{\mathbf{b}}\) = significantly different from values at 70% 1 RM (\(p<0.001\)); \({}^{\mathbf{c}}\) = significantly different from values at 60% 1 RM (\(p<0.001\)). Significant condition \(\times\) load interaction effects were present for braking mean force (\(p\) = 0.048) and impulse (\(p\) < 0.001); however, the interaction effects for braking duration (\(p\) = 0.559), propulsion mean force (\(p\) = 0.583), duration (\(p\) = 0.182), and impulse (\(p\) = 0.647) were not statistically significant. In addition, there were no significant condition \(\times\) load interaction effects present for MBV (\(p\) = 0.376) or PBV (\(p\) = 0.654). Post hoc significant differences are displayed in Table 2.
# Semantic Information for Robot Navigation: A Survey
## 4 Semantic Navigation
### Principles of Semantic Navigation  
Approaches of semantic navigation follow some common principles, including a framework for topological mapping that includes geometrical information, adding topological abstraction. Integrating a lower level navigator is common in semantic navigation approaches. Examples can be found in the work of [PERSON] et al. [57], where authors describe how to generate a topological map, assuming that the robot has a priori map of the environment, using an occupation grid approach [110]. Following these ideas, [PERSON] et al., presented an interface to integrate different low-level navigators in a semantic navigation system [78].  
The semantic navigation methods also integrate approaches for classification of places, or labelling, as studied in Section 2.2.1. [PERSON] et al., use a histogram-based classification method to classify indoor environments in university buildings (e.g., corridors, laboratories, meeting-rooms, offices, etc.) [111]. Another approach uses AdaBoost for classification of six indoor places (room, corridor, doorway, kitchen, office, seminar room and laboratory.) [57]. More recently, another work includes a place classification method based on people activities, resulting on the labelling of six environments (cafeteria, library, corridor, exhibition room, conference room and indoor soccer field) [29]. These approaches include a partition of unsupervised places, for instance segmenting the environment to have distinguishable places on top of a topological map [111], or creating an ontology that relates a conceptual hierarchy to the spatial hierarchy needed by a lower-level navigator [19]. [PERSON] et al., include semantic knowledge into a Reinforcement Learning approach [112]. Authors use a graph representation to model knowledge and Graph Convolutional Networks [113] to handle relationships between features.  
Finally, a conceptual representation of the detected places is necessary. In this sense, [PERSON] et al., proposed the use of augmented navigation graphs to relate the concepts of the conceptual hierarchy of places with each other [111]. Figure 12 shows how the nodes of the topological map (topometric) are matched with categories of the conceptual hierarchy. This conceptual hierarchy can also be expanded adding objects, utilities and subjective evaluations [108].  
Figure 11: Multi-layer conceptual spatial map [108].
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 4 Discussion
### Results from Mathematical Analysis  
A normalized throughput (\(\eta\overline{\otimes}\)) was obtained for TCP Reno, TCP Westwood, TCP-NACK Reno and TCP-NACK Westwood. The TCP Reno and Westwood results were calculated by (16), and the TCP-NACK results by (17). Figure 8 shows a comparison of the generated curves for each protocol as a function of the error packet probability. We found that both TCP-NACK versions had a similar behavior among them, and a better performance that its generic versions. Therefore, based on this analysis, for the next experiments, we only compare TCP-NACK with TCP Reno, declared as Generic TCP.
# _Echinacea purpurea_ Alleviates Cyclophosphamide-Induced Immunosuppression in Mice
## 1 Introduction  
Immunity is a physiological protective system of human bodies that distinguishes the \"self\" and \"non-self\" components thereof to maintain human health [1]. The largest lymphoid organ, which contains immune cells for immune responses, is the spleen [2, 3, 4]. Spleen is composed of lymphocytes, macrophages, dendritic cells, natural killer (NK) cells, and several immune cells, mediating immune responses [5]. Upon splenic lymphocytes' activation, cytokines, such as IL-2, IL-6, and IFN-\(\gamma\), as well as other inflammatory mediators, are released, initiating innate and adaptive immune responses [6]. NK cells, which act as primitive killers, play a major role in defending the host from cancer cells, bacteria, and virus-infected cells [7].  
Generally, most anticancer chemotherapeutics drugs often cause many harmful side effects, such as immunosuppression and myelosuppression [8]. Cyclophosphamide (CP) is a commonly-used alkylating agent for the chemotherapeutic treatment of several cancers [9].  
Administration of CP can generate excessive oxidative stresses via the disruption of redox signaling [10]. In addition, CP can cause an imbalance of Th1/Th2 responses and a reduction in the absolute number of T cells and B cells [11; 12]. Therefore, many previous studies have used the Balb/c mice model with CP as an immunosuppressive model [13; 14].  
Many previous studies have approached new candidates for modulating immunoreaction [13; 14; 15] because the current clinical drugs or chemicals induce various adverse effects [16]. Thus, natural products and herbal medicines are receiving attention as alternative agents to regulate immunity or enhance people's health [17; 18]. Echinacea is a perennial flowering plant of the _Asteraceae_ family, native to North America and commonly called purple coneflower [19]. Several plants of the _Asteraceae_ family, including _Cynara scolymus_ and _Cichorium intybus_, have already been reported to have immunostimulatory activities [20; 21]. _Echinacea purpurea_ (EP) is the most popular species in this genus and it has been used to treat the common cold, coughs, bronchitis and inflammation of the mouth and pharynx [22]. It also has been reported that the extracts of EP exert various biological functions, such as antibacterial, antioxidant, and anti-inflammatory activities [23; 24; 25]. However, no studies have reported on the immuno-enhancing activity of EP in CP-induced, immunosuppressive mice.  
In this study, EP extract was investigated for its immunostimulatory activity in vivo. We evaluated the beneficial effects of EP against immune suppression by the chemotherapeutic agent CP in BALB/c mice and explored its mechanism of action.
## 3 Method  
SAR radiometric calibration is a process of establishing the exact relationship between an image and ground target backscatter coefficients, which usually establishes the relationship between the image impulse response energy of the point target and its RCS through the overall radar system transfer parameter, i.e., the calibration constant [17].  
The flow of the method used in this study is shown in Figure 7. Subsequent to the completion of satellite transit and ground synchronization experiment, the impulse response energy \(\varepsilon_{p}\) of the seven flexible CRs was first extracted from the C-SAR/01 image, and by combining this with the theoretical RCS of each CR, the absolute calibration constant \(K\) was calculated. Using the calibration constant, the SLC data were absolutely calibrated to establish the quantitative relationship between the images and the radar backscattering coefficients.
## 4 Recent Applications of Paleoparasitology
### Contribution to Our Knowledge of Parasite History  
The multiplication of paleoparasitological studies for different periods and in different geographical areas fuels, in a second stage, questions related to the history of parasitic diseases. As a result of the compilation of data collected during analyses, it becomes possible to monitor parasites over time (appearance, disappearance, resurgence, migration), and to place these phenomena in parallel with the history of human or animal populations. In 2010, the synthesis of available data on the small liver fluke, _Dicrocoelium dendriticum_, a parasite of herbivores, highlighted the role of modern European migrations in the transfer of the parasite from Europe to the American continent [65]. This same work on the horse pinworm (_Oxyuris equi_) highlighted an increased presence of the parasite and a widening of its range in Roman-era sites, probably because of the use of the horse at that time for trade, conquests, or population movements [66] (Figure 5).  
Figure 5: Eggs from the oldest and most recent period observed in the Besancon laboratory: (**A**) Lancet liver fluke egg from Concise (Neolithic period, Switzerland), 41.48 μm \(\times\) 24.59 μm; (**B**) Lancet liver fluke egg from Reims (Modern period, France), 42.78 μm \(\times\) 23.12 μm; (**C**) _Oxyuris equi_ egg from Berel (Iron Age, Kazakhstan), 84.28 μm \(\times\) 43.12 μm; (**D**) _Oxyuris equi_ egg from Bourges (Medieval period, France), 85.65 μm \(\times\) 45.23 μm. Scale bars, 20 μm.  
The use of immunology since the early 2000s has also provided information for some unicellular parasites of humans. For example, the human pathogenic amoeba, _Entamoeba histolytica_, present in Europe since the Middle Neolithic, seems to have appeared on the American continent around the twelfth century, three centuries before the 'discovery' of this territory by [PERSON]. The nature of the migratory phenomena behind this transfer remains unknown, but these analyses have challenged the old assumption of an American origin for the parasite [29]. The history of many other parasites remains to be elucidated and only further analysis will provide some answers. This is the case, for example, for the roundworm, whose almost systematic presence in paleoparasitological assemblages makes monitoring and interpretations difficult. This parasite, detected in Europe in Paleolithic samples, is predominant in parasitic fauna spectra from the Roman period onwards and remains so until the most modern periods of history [40; 43].
* We highlight all the essential network elements of 6G system architecture and discuss defined issues that current generations of mobile networks are facing; the mobile industry should transition away from traditional strategies and toward some new ones, such as operation in shared spectrum bands, inter-operator spectrum sharing, indoor small cell networks, a large number of local network operators, and on-demand network slice leasing. * To meet the requirements of the stage where the research results on ML are to be addressed, as well as the methodology used by 5G and 6G for the Internet of Things and energy transmissions. We overview six major categories, including network resource management, security, augmented reality, network scaling, resource allocation, and the smart grid. * Towards the end, we discuss multiple challenges and research directions applications, issues, research questions, motivation, recommendation criteria, and open challenges of ML usage in incorporating energy and IoT in 5G and 6G,The remaining paper is outlined as follows. Section 2 defines the approach, including the source of material, requirements for eligibility of research, the systematic literature review (SLR) and the effects of search results of publications. The queries of any object from three website papers separated into 4 classes, the literary taxonomy on 5G and 6G Technology. Section 3 We discuss in detail about the projected 6G system architecture. We highlight all the essential network elements of 6G system architecture, application of AI and ML, existing technique, analysis of ML usage in incorporating energy and IoT in 5G and 6G, etc. Section 4 described and summarised in survey and review papers based on the latest state of the art in 5G, 6G, and research documentation. Section 5 investigated in this section, the requirements of the stage where the results of the research on ML are to be addressed, as well as the methodology used by 5G and 6G for the Internet of Things and energy transmissions. Section 6 We've done an explanatory evaluation of the use of the internet of things as the foundation for a transition strategy toward the development of 5G and 6G communications networks and technologies. Section 7 to handle various difficulties in wireless communications in the direction of merging energy and IoT in 5G, 6G, and beyond networks. Section 8 addressed inspiration, difficulties, and recommendations in that research area and a modern approach to the 5G and 6G networks that make use of IoT, energy and machine learning technologies. Finally, Section 9 presents the Conclusion. The reminder of this paper is organized as illustrated in Figure 2.  
Figure 1: Research Methodology Guideline.
###### Abstract  
Empirical systems for the classification of rock mass are used primarily for preliminary support design in tunneling. When applying the existing acceptable international systems for tunnel preliminary supports in high-stress environments, the tunneling quality index (\(Q\)) and the rock mass index (\(RMi\)) systems that are preferred over geomechanical classification due to the stress characterization parameters that are incorporated into the two systems. However, these two systems are not appropriate when applied in a location where the rock is jointed and experiencing high stresses. This paper empirically extends the application of the two systems to tunnel support design in excavations in such locations. Here, the rock mass characterizations and installed support data of six tunnel projects are used. The back-calculation approach is used to determine the \(Q\) value using the \(Q\)-system support chart, and these values are then used to develop the equations and charts to characterize the stress reduction factor (\(SRF\)), which is also numerically evaluated. These equations and charts reveal that the \(SRF\) is a function of relative block size, strength-stress ratio, and intact rock compressive strength. Furthermore, the \(RMi\)-suggested supports were heavier than the actual installed ones. If the approximate inverse relation between stress level (SL) and \(SRF\) is used, the difference between the actual and the recommended supports increases when using the \(RMi\)-recommended rock support chart for blocky ground. An alternate system is made for support recommendation using a \(Q\)-system support chart. In this system, the ground condition factor is modified from the available parameters, and a correlation is developed with a modified \(Q\) system.  
Keywords:high in situ stresses; fractured rock mass; \(SRF\); stress level +
Footnote †: journal: Appl. Sci. 2018, 8, 2659; doi:10.3390/app8122659
# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 1 Introduction  
The global demand for energy is steadily rising each year, primarily relying on fossil fuels for the majority of energy consumption. Fossil fuels are both limited and contribute to greenhouse gas emissions. In 2022, fossil fuels accounted for 82% of the total primary energy consumption, while hydro and other renewable energy sources made up about 14% (Figure 1) [1]. The burning of oil, natural gas, and coal releases around 35 billion tons of carbon dioxide into the atmosphere annually. To address this issue, it is essential to replace fossil fuels with renewable alternatives [2, 3]. In addition, it is stated that the utilization rate of renewable energy sources in the world for 2022 increased by 13%, and the installed PV power capacity increased by 22.2% compared to 2021 [1]. It is an acceptable prediction that this increase will continue in the coming years.  
As developing countries continue to industrialize, the need for energy is expected to rise. Hence, numerous nations have made energy conservation and efficiency enhancements central to their strategies for tackling climate change and ensuring energy security, as they have initiated and advocated various measures to achieve these goals [4]. This has led to the installation of more alternative energy units, such as solar fields and wind turbines, resulting in a growing need for economic and efficient maintenance.  
Solar photovoltaic (PV) technology is a widely accepted and cost-effective method for generating clean energy by converting sunlight into electricity [5]. It is an environmentally friendly system that operates silently with no moving parts, making it highly reliable and requiring minimal maintenance [6]. It offers a highly modular technology that can be manufactured in large plants or deployed in small quantities for various applications.  
The most common figure handed out for the lifespan of solar PV panels is 25-30 years [7; 8]. That is, they technically do not have an expiration date. But the situation is slightly different from a usage perspective. Various environmental elements like wind, salt, snow, and dust can compromise the efficiency of PV modules, leading to substantial declines in power generation as well as reducing the effective lifespan by almost half the planned time [9]. Some panels can also break due to physical damage from extreme weather conditions and extreme contrasts in temperature. They can also have manufacturing defects, or become damaged beyond repair [10; 11]. Additionally, concerns like corrosion and short circuits may arise. They experience a decrease in energy yield due to various factors over time, including single-cell breakdown and inefficiencies in important components like inverters. Furthermore, the potential for a single PV cell to overheat emerges in cases of connector damage or reverse bias effects [12].  
With these facts, as the need for solar PV increases, two basic issues become important for the continuity and sustainability of the system: monitoring of the arrays and methods for fault detection and diagnosis.
## 5 Results  
Although LLC converter brings ZVS for turn-on, there are some other losses in the steady state [71]: (1) reverse conduction or circulating loss, especially when the LLC converter is operating at the step-up mode with a switching frequency lower than the resonant frequency; (2) conduction loss that happens in the on-state mode because of the on-state resistance of the transistor; (3) turn-off loss, especially when the LLC converter is working at the step-down mode with a switching frequency higher than the resonant frequency; and (4) gate drive loss, especially in high switching frequencies.  
The parasitic components of the circuit play a more important role in the higher frequency. The LLC performance can shift away from the desired operating point by ignoring these components [72]. The effects of the parasitic capacitance of the secondary side diodes on the ZVS operation have been studied in some researches, like [6; 68; 69; 70; 73]. However, experimental tests have proved that the impact of parasitic capacitors and secondary side diodes is not limited to the ZVS condition and they can also influence the waveforms of the following normal operation [74]. The parasitic capacitance of the secondary side devices is more important in the LLC converter with step-up transformers, as discussed in [70; 75].  
Conduction loss is strongly affected by transformer stray capacitance. This capacitance changes the magnetizing current and deadtime, deteriorating the LLC performance in the high switching frequency [76; 77; 78]. The lower parasitic winding capacitance contributes to smaller deadtime and higher efficiency. The stray capacitance not only distorts thetransformer voltage in light load condition, but also generates common mode noise [79; 80]. The effect of this capacitance on the LLC converter performance has been studied in some researches [69; 81]. [PERSON] et al., for instance, studied the dissipation caused by capacitance between primary and secondary windings of planar transformers in the LLC converter [82]. Two models called cylindrical and flat model have been proposed by [PERSON] et al. to calculate the primary and secondary windings turn-to-turn capacitance [76]. A three-port network of six lumped capacitors, as introduced in [83], can also be used to analyze the inter-winding capacitance between primary and secondary windings. Lumped stray capacitance modeling is more convenient for dynamic circuit modeling and numerical simulation [78].
## 3 Dynamic Time Slots Allocation Based on Genetic Algorithm
### Genetic Algorithm for Beam Hopping
They should be set appropriately to minimize the complexity while guaranteeing that the optimal solution can be obtained [37]. Finally, the dynamic time slots allocation based on GA for beam hopping is shown in Algorithm 1. Figure 3: Process chart of beam hopping time plan optimization based on GA.