esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Results and Discussion  
### Models for Verification  
Firstly, two classical one-dimensional nonlinear models, including the Duffing oscillator and the gap model, are utilized to verify the accuracy and efficiency of the methods adopted.
# Natural Motion for Energy Saving in Robotic and Mechatronic Systems
## 2 Mechanical Design for Natural Motion
### Discussion of Elastic Elements Configurations
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref.** & **1 st Author** & **Year** & **Path and Trajectory Planning** & **Compliance of Elastic Elements** \\ \hline \multicolumn{4}{l}{**Defined trajectory**} \\
[89] & [PERSON] & 2017 & cycloidal, 5-degree polynomial trajectory & linear parallel \\
[90] & [PERSON] & 2019 & double-S speed profile & linear parallel \\
[40] & [PERSON] & 2018 & harmonic trajectory & variable parallel \\
[16] & [PERSON] & 2012 & harmonic trajectory & variable parallel (mono- and bi-articular) \\
[91] & [PERSON] & 2019 & adjustment of limit cycle & variable parallel \\
[65] & [PERSON] & 2014 & circular path in the operational space & non-linear variable parallel \\
[92] & [PERSON] & 2012 & desired motion given & linear parallel \\
[72] & [PERSON] & 2012 & harmonic trajectory & variable parallel \\
[93] & [PERSON] & 2016 & desired motion given & variable parallel \\
[94] & [PERSON] & 2016 & harmonic trajectory & variable parallel \\
[58] & [PERSON] & 2017 & harmonic trajectory & linear and non-linear variable parallel \\
[64] & [PERSON] & 2005 & harmonic trajectory & linear and non-linear parallel \\
[95] & [PERSON] & 2005 & harmonic trajectory & linear parallel \\
[96] & [PERSON] & 2017 & sinusoidal path given & non-linear parallel \\
[77] & [PERSON] & 2007 & harmonic trajectory & variable parallel \\
[97] & [PERSON] & 2008 & harmonic trajectory & variable parallel \\
[98] & [PERSON] & 2008 & harmonic trajectory & variable parallel \\
[99] & [PERSON] & 2009 & harmonic trajectory & variable parallel \\
[78] & [PERSON] & 2014 & harmonic trajectory & variable parallel \\
[86] & [PERSON] & 2013 & harmonic trajectory & linear serial/parallel \\
[79] & [PERSON] & 2015 & squared trajectory & linear and variable serial \\ \hline \multicolumn{4}{l}{**Optimized trajectory**} \\
[73] & [PERSON] & 2017 & cyclic end-effector path given & non-linear parallel (mono- and bi-articular) \\
[80] & [PERSON] & 2018 & polynomial trajectories for robot and & variable parallel \\  & & variable stiffness springs & variable parallel \\
[56] & [PERSON] & 2011 & trajectory optimization & linear parallel \\
[100] & [PERSON] & 2016 & trajectory optimization & linear parallel \\
[82] & [PERSON] & 2018 & optimal control theory path planning & linear parallel \\
[101] & [PERSON] & 2009 & trajectory optimization & linear parallel \\
[102] & [PERSON] & 2012 & trajectory discretized in the joint space & non-linear parallel \\
[61] & [PERSON] & 2013 & trajectory discretized in the joint space & non-linear parallel \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature on natural motion in robotic and mechatronic systems.
# Numerical Study of Heat Transfer Enhancement for Laminar Nanofluids Flow
###### Abstract  
The laminar forced convection has been investigated for the flow of nanofluids in conventional straight tube (L = 5.34 m, d\({}_{t}\) = 10 mm) and straight microtube (L = 0.3 m, d\({}_{t}\) = 0.5 mm) under the constant temperature and constant heat flux conditions, separately. A wide range of the process parameters has been studied by varying three different type of base fluids including water, ethylene glycol and turbine oil with five different type of nanoparticles viz. Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), CuO, SiO\({}_{2}\) and ZnO. Six different combinations of the geometries, base fluids and nanoparticle concentrations are considered in the present study. In addition to the single-phase model (SPH), the single-phase dispersion model (SPD) has been also used for effectiveness of the computed results. The results showed that Nusselt number (Nu) increases with increase in Reynolds number (Re). Further, the Nu considerably enhanced (up to 16% at volume fraction \(\phi_{b}\) = 4%, Re = 950) with increase in nanoparticle concentrations. Heat transfer correlations are developed for the flow of nanofluids in conventional straight tube and straight microtube over a wide range of process conditions (25 \(<\) Re \(<\) 1500, 0 \(<\)\(\phi_{b}\) \(<\) 10, 6 \(<\) Pr \(<\) 500) to enable a large number of engineering applications.  
Keywords:nanofluids; laminar force convection; heat exchangers; heat transfer enhancement; microtube +
Footnote †: journal: Applied Sciences
## 1 Introduction
Whereas the volumetric antennas may have an advantage in terms of higher efficiency and more tunability in general, it is the planar ones that can be relatively thinner [26] or even conformal with a generally easier form factor to handle [27; 28]. In this study, we designed a highly efficient and electrically small quasi-isotropic planar antenna at 2.45 GHz and examined its feasibility in improving wireless link reliability under NLOS channels. The proposed antenna design is compact and shows a good quasi-isotropic pattern. The simulated results of the impedance and radiation properties were successfully verified experimentally. Wireless link reliability was evaluated using the proposed antenna under line-of-sight (LOS) and NLOS channels. The comparison group used an omni-directional antennas pair, and the possible and simple solution of improving PDR and RSSI is discussed here.
# Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences
## 2 Related Work  
Face identity recognition can usually be done manually by forensically trained facial reviewers or automatically, usually by a computer expert system. The automated face identity recognition is a research area which has experienced a rapid growth in terms of accuracy and reliability in recent years. This success is achieved mainly thanks to the utilization of the so-called deep neural networks [4], higher computational power, and also the presence of large training and evaluation datasets such as Labeled Faces in the Wild (LFW) (13,233 images) [5], PubFig (58,797 images) [6], FERET (14,126 images) [7], CelebA (200 k images, 10,177 identities) [8] or Youtube faces DB (1595 identities) [9]. These datasets contain face images with various illumination conditions, expressions, races, and poses. Although there has been great progress in face identity recognition and its accuracy in recent years, the reconstruction of an image from a low-resolution one still remains a challenge [4]. There is currently no large-scale face dataset containing several sequences of images. Many of the current super-resolution methods have a generative nature and, therefore, cannot be used for identification purposes since they are too creative.  
The approaches, which addresses the problem of increasing resolution, can be divided by both the content type and input data type. The methods based on the content type can be divided into general super-resolution techniques and biometry-focused super-resolution techniques. From the point of view of the input data type, the methods can be divided into two groups: with a single input image (the so-called single-frame) or with many input images (the so-called multi-frame, i.e., video). Super-resolution, mainly thanks to the success of deep learning, has made significant progress in recent years [10]. This is also the reason why the most successful methods for super-resolution are mainly based on neural networks. In particular, they are Convolutional Neural Networks (CNN) or Generative Adversarial Networks (GANs).
# Crack Location and Degree Detection Method Based on YOLOX Model
## 2 Methodology  
To quickly assess the crack position and degree, this paper proposes a crack location and degree detector based on YOLOX. This algorithm can detect cracks with different degrees automatically and quickly. Figure 1 shows the flow chart of the method, which is divided into four main parts: (1) dataset construction, which collects data using an image acquisition system and labels these crack images for training; (2) detector training, which is the process of finding the optimal hyperparameters of the model; (3) crack location and degree detection, which outputs model predictions; and (4) actual crack damage assessment, which converts crack degrees in pixel level to actual scale. The detection principle and the transformation of damage levels are explained in the following subsections.  
Figure 1: Overview of crack location and degree detection algorithm.
## 5 Conclusions  
This paper proposes SA-MPCMOEA, a Kriging-assisted evolutionary algorithm based on a multi-preference model designed to address expensive, constrained multi-objective optimization problems. SA-MPCMOEA dynamically determines subsequent optimization operations by analyzing the current population distribution state. In Mode 1, the algorithm employs an angle-based convergence strategy to rapidly guide the population toward feasible regions using angular distribution information, thereby reducing computational resource waste. Mode 2 enhances convergence performance by driving the main population P and auxiliary population A to approximate the constrained Pareto front and the unconstrained Pareto front, respectively, achieving preference decoupling between objective functions and constraints. Under Mode 3, while maintaining the constraint compliance of population P, the auxiliary population A strengthens exploration  
Figure 6: IGD value convergence.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Problem** & **M** & **D** & **NSGA-II** & **RVEAa** & **CTEA** & **CCMO** & **ABSAEA** & **SA-MPCMOEA** \\ \hline RWMDP1 & 2 & 4 & \(\begin{array}{c}3.612\times 10^{-5}\,(4.93\times\\ 10^{-5})\end{array}\) & NaN (NaN) & \(\begin{array}{c}5.998\times 10^{-5}\,(4.57\times\\ 10^{-5})\end{array}\) & \(\begin{array}{c}3.623\times 10^{-5}\,(2.91\times\\ 10^{-5})\end{array}\) & \(\begin{array}{c}3.635\times 10^{-5}\,(2  
capabilities in underdeveloped regions through constraint-free search mechanisms guided by diversity and convergence metrics coupled with hybrid update strategies. Additionally, the dynamic evaluation strategy and novel filling sampling criterion enable effective screening of high-quality candidate solutions, allowing the algorithm to allocate expensive evaluation resources to truly promising individuals. Collectively, SA-MPCMOEA achieves a balanced optimization between objective pursuit and constraint satisfaction in computationally expensive scenarios. The code for SA-MPCMOEA can be found at [[https://github.com/AMA123-sx/AMA](https://github.com/AMA123-sx/AMA)]([https://github.com/AMA123-sx/AMA](https://github.com/AMA123-sx/AMA)) (accessed on 28 March 2025).  
Conceptualization, Y.S.; data curation, Y.M.; resources, Y.S.; software, Y.S.; validation, Y.S. and Y.M.; visualization, Y.M.; writing--original draft, Y.M.; project administration, B.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China OF FUNDER grant number 61763002 and 62072124, Guangxi Major projects of science and technology OF FUNDER grant number 2020 AA21077021, and the Foundation of Guangxi Experiment Center of Information Science OF FUNDER grant number KF1401.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all participants involved in the study.  
The data presented in this study are openly available in PlatEMO at [10.1109/MCL2017.2742868], reference number [25].  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Telemetric Data Collection  
One goal of the integration of the TIS-II into the greenhouse systems is to enable future automated monitoring of growth and development of plants during periods without human intervention. The pictures in the top panel of Figure 4 show a subset of the data collected under an autonomous scenario  
Figure 3: Installation of the TIS-II GFP Imager in the Arthur Clarke Mars Greenhouse.  
in 2006. Images were collected twice daily by the ACMG systems and downloaded via satellite link to a remote location as part of the normal flow of commands and data from and to the ACMG. The imager/greenhouse collected and telemetrically transferred these data for several weeks before decommissioning at the end of the field season. For additional image referencing, the bottom panel shows a bench-top germination experiment, a portion of a time-course experiment which monitored biosensor plant germination and initial growth over a two week period. In both cases, close inspection of the images reveals two phenotypes of plants, one that glows brightly green and one that can be seen only as a much less intense fluorescence in the red wavelengths due to the auto-fluorescence of chlorophyll. These two genotypes provide the range of GFP image responses that can be expected in an actual deployment of GFP biosensor plants.  
The images in Figure 4 indicate that the TIS-II Imager is fundamentally capable of capturing differences in the expression range of GFP fluorescence. Moreover, these images demonstrate the  
Figure 4: Automated collection of GFP images over time. Top panel: TIS-II GFP imager pictures taken autonomously by the ACGH and remotely downloaded as part of the sensor and image data packets. The large image to the left shows the full camera view, the smaller images were cropped. The date each image was taken is indicated in the bottom left corner. These data were collected through the middle of August, 2006. Bottom panel: in a separate lab-bench test, a plate seeded with two reporter gene genotypes was allowed to grow for two weeks. The images in this set were captured every 6 hours and reflect about 5 days of growth. In all images it is possible to distinguish those plants expressing GFP from those plants not expressing GFP. The non-expressing plants are faintly visible because of the red fluorescence caused by excitation of chlorophyll. In these GFP genotypes the normal red fluorescence of the leaves is overpowered by the green fluorescence of the GFP.  
integration of GFP biosensor plants and imaging technology into, and as a part of, a distantly deployed and autonomous greenhouse data collection system. Over the coming field seasons the ACMG TIS-II imager team will continue to integrate the TIS-II Imager into the greenhouse environmental monitoring system allowing for the use of TIS-II Imager data in greenhouse control strategy just as current greenhouse thermocouples, RH sensors, light sensors are presently used for remote operations.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles  
The linewidth of a laser mainly derives from random phase jitter, and the recombination and generation of laser carriers and photons can cause instantaneous changes in photon density and carriers. An instantaneous change in photon density leads to amplitude fluctuations in the output power, resulting in background noise. Relaxation oscillation can cause instantaneous changes in carrier density, alter the refractive index, and produce phase changes, resulting in a certain spectral linewidth in the laser mode.  
Laser linewidth measurements have typically used spectrometers and F-P standard instruments, with the two measuring linewidth limits in the GHz and MHz orders [23; 24], respectively. They cannot meet the linewidth characterization requirements of existing narrow-linewidth lasers. There are several methods for characterizing the linewidths of narrow-linewidth lasers present, including the heterodyne, delayed self-homodyne, delayed self-heterodyne, and noise analysis-based linewidth characterization methods. The experimental setup is shown in Figures 1 and 5.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 5 Treatments
### Drugs Targeting Biofilm
#### 5.1.3 Promoting Biofilm Destruction  
In addition to targeting biofilm formation, promoting biofilm degradation is an ideal strategy for fighting biofilm-forming bacteria. A better curative effect can be obtained when patients suffering from TB receive antimicrobials together with nebulized cellulase [6]. In one study, [PERSON] and colleagues used _M. bovis_ BCG as an Mtb model to study the antibiofilm effects of cellulase. The results showed that after BCG strains cultivated with a mature biofilm were added via cellulase in different concentrations, the biofilm biomass value decreased in a concentration-dependent manner. Moreover, nanoparticles loaded  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Agents** & **Mechanism** & **Effect on Biofilm** & **Reference** \\ \hline DMNP & Target (p)ppGpp synthesizing & & [92] \\  & protein Rel & Inhib biofilm formation & (_M. smegmatis_) \\ Trehalose analogues (TreAz, TreNH2) & Competitively inhibit TreS-mediated & & [93; 94; 95] \\  & trehalose catalytic shift & Inhib biofilm formation & (_M. smegmatis \& M. tuberculosis_) \\ Cellulose & Hydrolyze cellulose & Promote biofilm destruction & [6; 96] \\  & & & (_M. tuberculosis_ \& M. bovis_) \\ IITI-3 & Not clarified & Inhib biofilm formation & [97] \\  & & & (_M. smegmatis_) \\ IDR-1018 & Bind and deplete (p)ppGpp & Inhib biofilm formation, & [98] \\  & promote biofilm destruction & (_P. aeruginosa_) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Antib Biofilm agents.  
with cellulase and levofloxacin showed better biofilm-eliminating and -dispersing effects compared with those loaded solely with levofloxacin [96].
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 4 Results and Discussion
### Solution
#### 4.1.1 Registration  
Before [PERSON] can access APPIFY in MEC, she has to register to the main server of APPIFY. During the sign up, [PERSON] generates a device-specific asymmetric key pair and chooses her user identity for APPIFY (Alice13) and password (\(psw\)). The main server of APPIFY checks whether the user identity is reserved. If yes, then [PERSON] is prompted to pick another user identity. If no, the main server stores [PERSON] and \(psw\) to its database. The main server of APPIFY issues a certificate \(cert(Alice13)\), which includes the information of [PERSON] being a valid user and which services [PERSON] is authorized to receive. The main server also sends \(cert(VK_{main})\) to [PERSON]. For example, the enrollment over secure transport (EST) protocol can be used for issuing the certificate [51]. [PERSON] can start using APPIFY in MEC, after receiving the certificates \(cert(Alice13)\) and \(cert(VK_{main})\).  
The password is widely used to authenticate the user identity of the application. In our scenario, the password is related to [PERSON], not to [PERSON] or the UE of [PERSON]. In addition, the public and private key pairs are valid for just one UE. If [PERSON] wants to use APPIFY with another UE, those key pairs cannot be used. Therefore, the password is a necessary part of the solution.
## 3 Results
### Analysis of the Measured Data
Figure 9: “Forward” integrals of \(\Delta T^{2}\) with respect to time, at various depths, for 205 days, in Mataro. In Figure 10 we present the plot of the \"reverse\" integrals of \(\Delta T^{2}\) with respect to time (\(T_{plate}<T_{depth}\)), for the same 205 days period in Mataro; it is interesting to observe how important is the use of a buried heat sink (to collect energy that is stored in the soil). From the graph of Figure 10 we notice that the values of the \"reverse\" energy are not negligible when compared to the \"forward\" energy (Figure 9), and the best estimated potential for energy harvesting measured for the \"reverse\" energy (at the depth of 40 cm) is even a little higher than the best \"forward\" energy at a depth of 20 cm. The sum of both the \"forward\" and \"reverse\" energy from Figures 9 and 10 is presented in Figure 11.  
Since a DC-DC converter using the LTC3108 powered with a commercial 110 mV/\({}^{\circ}\)C Seebeck coefficient Bismuth Telluride TEG (from EVERREDtronics) requires a \(\Delta T\geq 1\)\({}^{\circ}\)C to start operating [14], we decided to refine the analysis and perform the calculations of the integrals of \(\Delta T^{2}\) only when \(|T_{plate}-T_{depth}|\geq 1\)\({}^{\circ}\)C. The plots in Figures 12 and 13 show the values of these integrals (when \(|T_{plate}-T_{depth}|\geq 1\)\({}^{\circ}\)C), for the installations in Mataro and Campinas, respectively. It is interesting to notice how the restriction \(|T_{plate}-T_{depth}|\geq 1\)\({}^{\circ}\)C influences the system. Comparing Figure 12 with the results from Figure 11 we can see how the condition \(|T_{plate}-T_{depth}|\geq 1\)\({}^{\circ}\)C reduces the performance of the TEG energy harvesting system. Figure 11: Sum of the integrals of \(\Delta T^{2}\) with respect to time at various depths, for 205 days (from 23 June 2015 to 13 January 2016), in Mataro. Figure 10: Sum of the “reverse” integrals of \(\Delta T^{2}\) with respect to time, at various depths, for 205 days, in Mataro. Due to the difference in the latitudes and in the climate of the Northern and Southern Hemispheres, it was obviously expected that the potential of solar energy harvesting in Mataro would be smaller than in Campinas. It is clear that this supposition is correct, as we observe that in a period of only 32 days during the winter in Campinas (5 July to 5 August 2015), a system with the TEG buried at 40 cm (best depth in Campinas) shows a potential of energy harvesting that is 72% of the result obtained, during a period of 205 days (from 23 June 2015 to 13 January 2016) in Mataro, in a system with the TEG buried at 30 cm (best depth in Mataro).
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 4 Research Methods
### Extraction and Selection of Feature Variables  
Using ArcMap, feature variables from the following four types of data were extracted as model driving factors, as shown in Table 2.  
All the feature variables were subjected to correlation analysis using the Pearson correlation coefficient. Ultimately, 14 variables with significant contributions to the results were selected as model driving factors. These include ARVI, Clg, GNDVI, GOSAVI, MSAVI, MVI, NDVI, monthly maximum temperature, monthly minimum temperature, monthly precipitation, soil organic carbon content, soil total nitrogen content, elevation, and slope. These variables significantly influence variations in above-ground biomass in grasslands and can effectively enhance the model's accuracy.  
A total of fourteen variables were selected for model training, including seven vegetation indices, four meteorological factors, three soil properties and two topographical factors. To address potential multicollinearity, the Variance Inflation Factor (VIF) was calculated for all variables. The results showed no significant collinearity, with all VIF values below three (mean VIF = 1.82), indicating that variables can be used jointly without compromising model reliability. This supports the retention of all 14 features, as they provide independent contributions to AGB estimation.  
Prior to model training, all selected features were standardized using z-score normalization to eliminate scale differences and enhance model convergence. The formula applied was  
\[x^{\prime}=\frac{x-u}{\sigma} \tag{1}\]  
In Equation (1), \(x^{\prime}\) represents the standardized feature value after normalization, \(x\) denotes the original feature value, \(u\) is the arithmetic mean of the original feature values, and \(\sigma\) represents the standard deviation. This process ensures that each variable contributes equally to the MLP model's learning process, particularly critical for neural networks sensitive to input scaling.