esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Physical Exercise on the Release of Micorparticles with Angiogenic Potential
###### Abstract  
Cellular communication has a fundamental role in both human physiological and pathological states and various mechanisms are involved in the crosstalk between organs. Among these, microparticles (MPs) have an important involvement. MPs are a subtype of extracellular vesicles produced by a variety of cells following activation or apoptosis. They are normally present in physiological conditions, but their concentration varies in pathological states such as cardiovascular disease, diabetes mellitus, or cancer. Acute and chronic physical exercise are able to modify MPs amounts as well. Among various actions, exercise-responsive MPs affect angiogenesis, the process through which new blood vessels grow from pre-existing vessels. Usually, the neo vascular growth has functional role; but an aberrant neovascularization accompanies several oncogenic, ischemic, or inflammatory diseases. In addition, angiogenesis is one of the key adaptations to physical exercise and training. In the present review, we report evidence regarding the effect of various typologies of exercise on circulating MPs that are able to affect angiogenesis.  
micorparticles; microvesicles; extracellular vesicles; molecular markers; cell-cell communication; physical exercise; physical activity; angiogenesis; secretome; paracrine signaling; cellular crosstalk +
Footnote †: journal:  
abundant population of MPs derives from platelets, but also other different cell types release these vesicles into the circulation such as endothelial cells [9], leucocytes [10] erythrocytes [11], epithelial cells [12], muscle cells, and various tumor cell lines [12]. Therefore, MPs express surface protein and antigens deriving by their parental cell as well as cytoplasmic and nuclear content (e.g., proteins, mRNAs, microRNAs, small-interfering RNAs, long non-coding RNAs), that are signature of their cellular origin [13]. MPs are released in response to cellular activation or apoptosis, elicited by a variety of stimuli, such as inflammation, oxidative stress, or mechanical/hemodynamic fluctuations depending on the involved parental cell [14].  
Physical exercise can induce modifications in MP concentration [15]. In fact, MP concentration is modified by stimuli such hypoxia and shear stress [16], conditions elicited to a different extent by physical exercise. It has been demonstrated that MPs produced in response to physical exercise have a stimulating effect on new vessels growth [17].  
The purpose of this review is to summarize the effects of different patterns of physical exercise on the concentration of circulating MPs with a known angiogenic potential.
## 4 Wear and Corrugation Simulation
### Additional Comments  
Minor frequency components may also be relevant. According to the previous analysis, the wavelength of the wear changes with the corrugation amplitude A; however, Figure 8a shows a constant wear wavelength because Figure 8a was high-pass filtered at \(\lambda\) = 20 mm. When the same signals are high-pass filtered at \(\lambda\) = 30 mm, i.e., when the frequencies of 1185 Hz and 1291 Hz are given a more dominant role, the wavelength also varies (Figure 8b), and the phase of the wear changes with A. This behavior indicates that certain minor frequency components of wear shown in Figure 9 also play a relevant role in determining the wavelength and phase of the wear and thus the development of corrugation.  
Studying corrugation by pre-selecting certain modes may lead to different and possibly non-physical results, thereby highlighting the subtleness of the corrugation problem. However, this also reveals one of the advantages of the proposed modeling approach, which can contain all necessary modes of the structure and continua in the system by modeling the relevant components and interfaces with realistic geometry and material properties. Several questions for further research could focus on the cut-off wavelength for filtering: what is the shortest cut-off wavelength for filtering? Could it be related to the shortest observed corrugation wavelength of 20 mm? Is the optimal cut-off frequency equal to 20 mm or should it be shorter? Is this frequency related to the concept of the contact filter [58]?  
According to the above discussion, instead of increasing to 1296 Hz, the major frequency in Figure 9 should decrease from 1290 Hz at A = 2.5 um toward the frequency of the vertical mode of 1185 Hz when the corrugation amplitude grows and the vertical mode becomes dominant. This mismatch is caused by the inconsistency between the modeled corrugation wavelength of 30 mm and the natural modes of the track, which are determined by the nominal track parameters used in the modeling. The prescribed wavelength is a field observation related to deviation from the nominal track. The nominal track parameters do not necessarily cause corrugation; otherwise, corrugation would be observed everywhere. Because of this mismatch, the calculated wear tends to erase the corrugation.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 4 Performance Analysis  
The SNR analyses presented in Equations (38), (43), and (49) characterize the noise performance of the three demodulation methods under AWGN conditions for carrierless PPM signals. Since any practical PPM communication system requires one of these demodulation methods at the receiver, analyzing their individual performance characteristics provides essential insights into the overall system performance limits. These analyses are relevant to the proposed derivative-based modulation technique as follows. The demodulation methods examined--integration, PLL, and FM-PWM conversion--process the PPM pulse timing information regardless of the generation method used. Consequently, the fundamental noise conversion mechanisms from amplitude domain to time domain, as described in Equations (33)-(36), apply to PPM signals generated through our proposed VCO-monostable multivibrator approach.  
The variables in this section retain the same definitions as those previously established. As these demodulation methods respond to pulse timing and amplitude characteristics rather than the modulation generation process, the derived SNR expressions remain applicable to the proposed system. However, the VCO and monostable multivibrator components may introduce their own noise contributions that are not captured in the current analysis. Therefore, while Equations (38), (43), and (49) provide a theoretical framework for understanding the demodulation performance, a complete SNR analysis of the proposed system would require additional consideration of these factors.
# Anisotropy and Directivity Effects on Uniaxial Compression of Carbonaceous Slate Form Jinman Mine
## 3 Results and Discussion
### Loading Effect of Mechanical Parameters
#### 3.2.2 Effect of Slate Structure on Elastic Modulus  
The elastic modulus is another important engineering parameter that characterizes the mechanical properties of slate samples. Tables 1 and 2 show the distribution characteristics of the elastic modulus of slate samples for different loading conditions, which demonstrates the effect of slate structure on elastic modulus. The elastic modulus of the gray samples under vertical loading is 35.11-43.01 MPa, and the average elastic modulus is 38.97 MPa. The elastic modulus of the cinerous samples is 16.02-64.34 MPa, and the average elastic modulus is 37.73 MPa, which is slightly smaller than that of gray samples. This shows that the gray samples have stronger elastic deformation, which is consistent with the variation of peak strength.  
The slate stratum structure under parallel loading conditions shows a parallel relationship with the principal stress, and the slate structure plays a major bearing role on the slate rock. The slate samples had significant expansion deformation while the compression deformation was relatively restricted. This resulted in a steeper linear growth of the stress-strain curve and the elastic deformation was relatively intensified. As presented in Tables 1 and 2, the elastic moduli of gray samples and cinerous samples are 41.44-88.55 MPa and 41.11-78.28 MPa, respectively, and their average elastic modulus fractions are 56.24 MPa and 58.02 MPa, respectively, which are 1.44 times and 1.54 times higher than those under vertical loading, respectively. It can be seen that although the slate structure has a significant cracking effect on the bearing strength of slate rock, it has a certain strengthening effect on the elastic deformation.
## 3 Underlying Terminology and Concepts  
### Calibration and Validation  
Following the CEOS-recommended terminology [36], we define calibration as \"the process of quantitatively defining a system's responses to known, controlled signal inputs\". Validation, on the other hand, is defined as: \"the process of assessing, by independent means, the quality (uncertainty) of the data products derived from those system outputs\" [37].  
The radiometric and geometric calibration of optical land imaging sensors is the fundamental preprocessing step for ensuring that the considered mission provides reliable, accurate and consistent observations over space and time to meet the stringent requirements for scientific applications. The outcomes of the calibration process are, in the optical solar spectrum domain, the radiance or reflectance data at the Top-Of-Atmosphere (TOA) level, which are generally referred to as Level 1 (L1) products.  
The validation consists in quantifying the uncertainties of the satellite dataset by analytical comparison with a space-and-time-collocated independent measurement of the same quantity, which is assumed as the reference value (_ground truth_). Validation is a generic term that applies to any satellite-based product, both Level 1, e.g., validation of at-sensor radiance, and Level 2 (L2) surface reflectance and bio-geophysical products.  
The ultimate goal of the validation is to ensure that the users' requirements, initially set as part of the Mission Requirement Document (MRD), in terms of desired products' accuracy, are effectively met in orbit. Therefore, a comprehensive and accurate set of validation reference data is fundamental to verify that the mission successfully fulfilled its scientific objectives and to support corrective actions, e.g., upgrades of the retrieval algorithms and/or calibration methods in the case of shortcomings.  
The geophysical validation of satellite-derived land products is an extremely challenging task, owing to the intrinsic heterogeneity and anisotropy of terrestrial surfaces and the complex dependency on observation geometries, illumination conditions, canopy structure and layering. Within this chapter, we review the main concepts and underlying physical challenges associated to the Cal/Val of optical land imaging sensors.
## 1 Introduction
Figure 1 illustrates two typical application scenarios. In Scenario 1, the destination node and the source node cannot communicate directly; therefore, the MASS acts as a relay communication node. In Scenario 2, the MASS can assist Node A and Node B in expanding their dynamic search areas. The persistence of the communication links between the MASS and Nodes A and B directly affects the efficiency of the rescue. Considering the high latency and resource overhead associated with multi-hop topologies (as analyzed previously), this paper examines communication links within two hops. To the best of the authors' knowledge, this study represents the first comprehensive investigation into the integration of node mobility models, link persistence estimation, and  
Figure 1: Application scenarios. routing metrics within the context of MASS communication scenarios. In the proposed architecture, the node mobility model serves as the foundation, and the link persistence is the result. Based on a quantified metric of link persistence, we develop a routing metric for path selection to enhance routing performance. The primary contributions of this work may be summarized as follows:  
* We propose a joint mobility model to address the limitations of existing mobility models which neglect the mutual movement of nodes within the network. The mobility model can characterize the mutual distance between two nodes within a given time period, thereby enabling more accurate descriptions of nodes' movements in MASS communication scenarios. * Considering the characteristics of maritime communication and the mobility traits of nodes, we propose a link persistence estimation method as well as channel capacity-based objective functions for different transmission modes, which can better describe the dynamic properties of links. * We compare routing performance under link persistence, shortest path, and first found path. Simulation results demonstrate that routing based on the proposed link persistence metric demonstrates superior performance in packet loss ratio, end-to-end delay and throughput compared to routing based on the other two metrics. The remaining parts of this paper are structured as follows: Section 2 provides an in-depth description of the node mobility model. Section 3 introduces the link persistence analysis model based on the mobility model. The objective function for link persistence based on channel capacity under different transmission modes is provided in Section 4. Section 5 presents and analyzes the simulation results. Section 6 concludes the article and discusses future research directions.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Analysis Type** & **Authors** & **Purpose** & **County/Region** & **Method** \\ \hline Qualitative through & [4] & To identify the factors affecting & & PLS-SEM with \\ statistical modeling & & reverse logistics performance & & Si Lanka & questionnaire from a \\  & & & reverse logistics performance & & literature review \\ \hline Qualitative through & [5] & To discover the factors influencing & & PLS-SEM with \\ statistical modeling & & consumers’ recycling behavior & Pakistan & questionnaire from a \\  & & patterns concerning plastic waste & & literature review \\ \hline Multi-criteria & [6] & To reduce the accumulation of & & \\  & & plastic waste by returning it to & Indonesia & AHP \\ decision-making & & recycling & & \\ (MCDM) & & To find a suitable recycling method & & \\  & & for managing both disposal and & India & HPF-ELECTRE III, \\  & & recycling of plastic materials & & HPF-TOPSIS \\ \hline  & [8] & To analyze and quantify Brazilian & & \\  & & post-consumer plastic packaging & Brazil & Material flow analysis \\  & & waste flows & & \\ \cline{2-5} Quantitative through & & To combine a green supply chain & & \\ mathematical modeling & [9] & with a geographic information & & \\  & & system to consider the uncertainty & & \\  & & of the price of coal and evaluate its & & \\  & & effects on the closed-loop supply & & \\  & & chain of plastic recycling & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of papers addressing plastic waste management with modeling methods. **Source:** Adapted from [2,3]. The purpose of this work is to demonstrate a PLS-SEM approach with a structured questionnaire in a survey to test hypotheses concerning factors and then qualitatively measure plastic waste management performance, aiming to propose ways to improve it. In the present study, the methodology has been applied for the improvement of the Brazilian metropolis of Salvador, where the survey took place. The importance of this work is noticed because, in Brazil, a developing country, there is a knowledge gap concerning how to improve plastic waste management performance with statistical modeling. This issue is considered the research question in the current study. This paper proceeds with the materials and methods (Section 2), results (Section 3), discussion (Section 4) and conclusions (Section 5).
## 3 Results and Discussion
### Overall Performance Evaluation  
In this part, the performance of the CB model was evaluated and compared against those of the RF, ET, and AB models across various datasets based on the statistical metrics discussed in Section 2.4. Detailed information and rich visualization on the performance of optimized models are provided in Table 3 and Figure 8. Evidently, the CB model demonstrated the best overall performance, consistently obtained the highest \(R^{2}\), and the lowest error metrics (_RMSE_, _MAE_, _MAPE_) across all datasets. Validation scores indicate that the mean value of \(R^{2}\) is 0.9170, which is nearly equal to 1. The mean values of _RMSE_ and _MAE_ are 96.4863 kN and 56.8127 kN, respectively, which are considered relatively low compared to the mean value of the SS. Based on training/testing scores, the main values of \(R^{2}\), _RMSE_, _MAE_, and _MAPE_ are 0.9971/0.9422, 18.3366/69.2136 kN, 8.4077/47.1074 kN, and 2.9568/14.4932%, respectively. On the other hand, the RF, ET, and AB models obtained reasonable \(R^{2}\) scores across all datasets. However, slightly high values of error scores were detected compared to the CB model. Thus, further investigation and evaluation are required to understand the nature of the errors for each model.
## 4 Experimental Results and Analyses  
### Experimental Setup and Evaluation Metrics  
This paper uses MS-WFFNet for the object counting task. During the training process, each image is horizontally flipped with a probability of 50% and randomly cropped with a size of 256 \(\times\) 256 for training. We choose Adam as the optimization method, set the learning rate to 1 \(\times\) 10\({}^{-5}\), and use StepLR as the learning rate scheduler, multiplying the learning rate by 0.98 every 100 epochs. In EEMA, the number of heads of the multi-head attention mechanism is fixed to four. To keep the balance of the two sub-loss terms in the total loss function, we set the weight \(\lambda\) to 1/1100. In addition, the EEMA-VGG16 sub-network is initialized using the pre-trained weights provided by PyTorch. The specific experimental running environment is detailed in Table 2.  
Evaluation indicators are one of the important tools to measure model performance and prediction accuracy. In the field of object counting, mean absolute error (MAE) and root mean square error (RMSE) are commonly used as common indicators for evaluating models, which are defined as follows:  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}}{\left|pred_{i}-gt_{i}\right|} \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}}{\left|pred_{i}-gt_{i}\right|^{2}}} \tag{8}\]  
where \(N\) represents the total number of test images and \(pred_{i}\) and \(gt_{i}\) represent the predicted number of people and the actual number of people in the \(i\)-th image; these two indicators are used to quantify the deviation between the expected value and the actual value. The lower the indicator's value, the closer the prediction result of the model is to the actual value, the more accurate the performance, and the better the generalization ability.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 2 Picture of Tactical Communications: A Panoramic View  
The analysis commences with an exploration of the core elements in tactical communications slated for advancement through the utilization of 5G/6G-driven emerging technologies: namely, the tactical radios and the waveforms employed for transmitting information on the battlefield. The waveform is the shape of a signal in the time domain and imparts information about the signal beyond its spectral characteristics.
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 3 Testbed Design and Test Cases  
### Testbed Design  
The structural diagram of the testbed network developed by us for studying the security aspects of LoRaWAN is illustrated in Figure 4. The network consists of an NS together with an AS, GW and EDs. The testbed includes the second ED and a terminal (laptop) with an RTL-SDR sniffer connected, which can be used to launch an attack. The RTL-SDR passes information to the GNU Radio software. Note that the designed testbed primarily focuses on investigating the attacks carried in the radio channel; however, after some further modifications, it can also be extended to investigate the attacks on the servers and via a backbone connection.  
The individual elements and their configuration are described below. The whole network works currently following the version 1.0.2 of LoRaWAN specification (the upgrade to newer protocol versions is planned). The open-source Chirpstack solution was used for NS and AS [29].
## 3 Results and Discussion
### Relative Density (RD)
#### 3.1.2 Regression Models and 3D Plots of RD  
Equation (5) represents the RD response's linear regression model. The regression models depend on the input of pressure (P), H/D, and wt. % of the \(\rm{Al_{2}O_{3}}\) and their interactions. The R\({}^{2}\) and adjusted R\({}^{2}\) are very close to unity in the current study.  
\[\rm{RD\%=+89.42310+0.003258P+1.81055H/D-0.013578 Al_{2}O_{3}-1.49493(H/D)^{2}-0. 049975(Al_{2}O_{3})^{2}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Input** & \multicolumn{5}{c}{**RD\%**} \\ \hline
**Pressure (MPa)** & **H/D** &
\begin{tabular}{c} **Al\({}_{2}\)O\({}_{3}\)** \\ **(wt. \%)** \\ \end{tabular} & **Exp.** & **Predict** & **Error** \\ \hline
800 & 1.5 & 2.5 & 90.15 & 90.14973858 & 0.000261419 \\
600 & 1.5 & 2.5 & 89.65 & 89.65010871 & \(-1.09\times 10^{-4}\) \\
600 & 1 & 2  
Figure 4 illustrates the relationship between the RD experimental results for the parameters of the Al-4 Cu/(Al2O3)x composite and their corresponding anticipated values. The blue points are for minimum output value and gradually changed to red points for maximum output value. It is obvious from these data that the suggested regression models were effective since the experimental and forecast values match up well. This conclusion was drawn from the fact that the majority of the intersections between the experimental and predicted values are located near the median line.  
Figure 5 shows the impact that the AMC's parameters had on the RD in accordance with the regression model's (Equation (5)) calculations. At different Al2O3% levels, there appears to be a proportional link between the pressure and the RD. The H/D and RD were inversely related at different Al2O3% levels. At a pressure of 900 MPa and an H/D of 0.75, the maximum RD was achieved for all Al2O3% levels. Additionally, the proportion of the RD showed a decrease as a result of an increase in the weight percent of Al2O3. The greatest achievable RD of 92.872% is shown in Figure 5a at 0% Al2O3, 900 MPa, and a 0.75 H/D, which was similar to the value of 92.55% obtained experimentally at 0% Al2O3, 700 MPa, and a 1.25 H/D.