esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Solution Types/Algorithms
### Heuristic Algorithms  
Heuristic algorithm is a solution approach which is based on trial-and-error to achieve reasonably accurate solutions to a complex problem in a relatively practical time. Abundant literature is available in which heuristic algorithms are used to solve the multi-objective optimization problems. For example in [55], the authors have used greedy heuristic approach to solve a hybrid multi-objective optimization for simultaneously optimizing the coverage and connectivity of wireless sensor networks. A noisy optimization problem for neuronal signaling in medical sensor-actuator networks was formulated in [189], where authors used heuristic algorithm to achieve a tradeoff between signaling latency and signaling robustness. A heuristic algorithm has been proposed in [190] to solve multi-objective optimization formulation for optimizing sensor queries. The authors have used the multi-objective technique to minimize the response time of queries and energy consumption of the networks. A large number of WSNs is usually deployed in the area under observation and each node has its own set of configurations. Every configuration affects the quality of observation significantly. Therefore, configuring the network with proper parameter is critical for the performance of the network. Since the overall network configurations are huge, exhaustive search in the configuration space is not feasible. In [191], authors used a heuristic multi-objective search method to find near optimal configurations. A multi-objective optimization formulation has been used in [192] for dynamic spectrum allocation in wireless sensor networks. The proposed technique has been solved by using heuristic algorithm to obtained a trade off between maximizing fairness, maximizing spectrum utilization, reflecting the priority among sensor data and avoiding unnecessary spectrum handoff. A heuristic algorithm has been used in [193] to solve the problem of optimum design of a dual range force sensor for obtaining high sensitivity, broad bandwidth and large measurement range. In [194], the authors have used an heuristic algorithm to solve a multi-objective optimization approach for sensor arrangement in a complex indoor environment. The optimal arrangement was achieved to maximize coverage rate, minimize interference rate, and the number of sensors.
###### Abstract  
In this study, the effect of polylactide (PLA) material on the susceptibility to hydrophobic recovery due to storage was examined, the effectiveness of plasma activation of the film, defined as improving the hydrophilicity of the material, was studied, and the selected film properties that are important from an application point of view were investigated. The study was conducted on three films using oxygen and argon plasma. Contact angles of water, diiodomethane, and ethylene glycol were investigated, and the values of surface free energy (SFE) and its polar and dispersive components were determined immediately after activation, as well as after a specified storage time (after 1, 7, 14, 30, and 60 days). The effects of film activation on its roughness, weight loss, tensile strength, color changes, and gloss were also determined. Based on the results, it was concluded that the type of film influences the course of hydrophobic recovery. At the same time, the trend of changes depends on the type of gas used for activation, while the magnitude of changes depends on the film. Moreover, it was observed that the type of PLA film had a significant effect on the changes in the polar and dispersion components of SFE, as well as on roughness and mass loss due to plasma activation. Furthermore, these parameters also depended on the type of gas used for plasma activation.  
Keywords:aging; hydrophobic recovery; storage time; biodegradable polymers; PLA films; plasma activation; argon plasma; oxygen plasma +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Smartphones for Monitoring System  
Smartphones come equipped with sensors such as accelerometers, gyroscopes, and magnetometers. The availability of this low-cost and universally accessible technology presents an opportunity to address public health challenges related to assessing the mobility levels of older adults. The Samsung Galaxy phone model A14 (Suwon-si, Republic of Korea) was used. Its features allowed for adequate registration and monitoring of older adults. The accuracy and reliability of smartphones equipped with MEMS-based inertial sensors, such as the Samsung Galaxy A14's accelerometer and gyroscope, have been demonstrated in laboratory tests [70; 71]. Studies show acceptable accuracy in measuring movement and orientation, making them viable for non-clinical applications such as gait analysis and fall prediction [72; 73]. The phone was placed on the torso at the level of the dorsal spine, firmly attached to the person's body by means of a harness (Figure 1). The sampling rate for the accelerometer it was 50 datas per second.
# Comparison of Postural Stability and Regulation among Male Athletes from Different Sports
## 1 Introduction  
Postural control involves a complex integration of the entire sensorimotor system to maintain an individual's center of mass over their base of support [1]. Furthermore, this system must constantly adapt to changing environments caused by uneven surfaces, decreases in sensory input, as well as acceleration and deceleration forces (just to name a few) [2]. Not only is this control critical in activities of daily living to prevent falls, but this system can be taxed even more during competitive athletics when the kinematics and kinetics required by a particular sport are increased substantially. As such, deficiencies in an athlete's postural control can result in decreased performance and potential injury [3, 4].  
Postural regulation is critical for athletes regardless of sport. Whether it is athletes who are required to change directions quickly, such as soccer, basketball, handball, tennis, etc. [3, 5, 6, 7], athletes who produce highly-skilled technical movements, which are oftenscored by a judge, such as diving, gymnastics, ski jumping, cheerleading, etc. [4, 8, 9, 10] or athletes who must maintain their balance while riding a horse over jumps and around obstacles [11, 12], postural regulation is a key component of peak performance.  
Although similar research has been conducted on female athletes [3], currently, there is a paucity of similar comparisons among male athletes. Due to the identified neuromuscular differences between men and women, such as deviations in proprioception, muscular activation patterns, muscular strength, and balance [13], there may similarly be differences in the postural control of male athletes between sports. Therefore, this study investigated the postural regulation and stability in male athletes from a variety of sports. More specifically, we examined the postural control between elite-level male athletes in diving, ice hockey, and soccer.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 4 Experiments
### Conventional Experiment
#### 4.1.2 Experimental Results  
As shown in Figure 13, when the degree of overlap is 50% or 60%, the waveforms of \(x_{1}(t)\) and \(x_{2}(t)\) reconstructed by CD-SR are consistent with the actual waveforms (see Figure 12a), but SR can no longer successfully separate the overlapping echoes. The reason why SR fails in the experiment is that the actual overlapping echoes have relatively small differences in time-frequency parameters, while SR distinguishes overlapping echoes based on the difference in time-frequency parameters of the echoes. CD-SR forms a dictionary through pre-calculation and constraint. The atoms in the constraint dictionary are similar to the echoes before the overlap, and none of them are similar to the overlapping echoes, so the overlapping echoes can be successfully decomposed and reconstructed. Due to the tailing and noise of the experimental echoes, the experimental performance is not as good as the simulation. Table 3 shows the amplitude error \(A_{err(x1)}\) and position error \(P_{err}\) of CD-SR, indicating that the errors are small.
## 2 Methodology
### Feature Extraction
#### 2.2.4 Kurtosis  
Kurtosis measures the \"peakedness\" of the signal distribution and is especially useful for detecting localized defects such as bearing faults [18]:  
\[\text{Kurtosis}=\frac{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\overline{x})^{4}}{ \left(\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\overline{x})^{2}\right)^{2}} \tag{5}\]  
Higher kurtosis values indicate the presence of sharp peaks in the signal distribution.
## 3 Statistical Analyses  
All statistical analyses were performed using SPSS (version 25.0; SPSS, Inc., Chicago, IL, USA) and were shown as means with standard deviations (\(\pm\)SD) with their 95% confidence intervals (CI). The relative (two-way mixed effects, absolute agreement, single rater intraclass correlation coefficient) and absolute (coefficient of variation and standard error of measurement) reliability were calculated. The thresholds for interpreting intraclass correlation coefficient results were: <0.5 \"poor\", 0.5-0.75 \"moderate\", 0.75-0.9 \"good\", and >0.90 \"excellent\" [38]. For coefficient of variation results, they were: <10% \"very good\", 10-20% \"good\", 20-30% \"acceptable\", >30% \"not acceptable\" [39]. Statistical significance was set at \(p\) < 0.05. The normality of data distributions was checked using Shapiro-Wilk tests. The three-way ANOVAs (2 groups (EXP and CTRL) \(\times\) 2 times (pre-post measure) \(\times\) 4 load (50, 70, 90, and 70% 1 RM-UF (until failure))) were used to determine the influence of load on muscle mechanical properties. When a significant main effect or interaction was found, pairwise comparisons were conducted using a Bonferroni test. Additionally, to establish the influence of a set at 90% 1 RM, paired sample \(t\)-tests were used to assess changes in barbell velocity during the bench press at sets with 70% 1 RM. The magnitude of mean differences was expressed with standardized effect sizes. Thresholds for qualitative descriptors of Hedges g was interpreted as \(\leq\)0.20 \"small\", 0.21-0.9 \"medium\", and >0.80 as \"large\".
# The Loop-Mediated Isothermal Amplification for the Rapid Detection of Porphromonas Gingivalis
## 2 Materials and Methods
### Detection of Tested Periodontal Pathogens  
#### 2.5.1 LAMP (Loop Mediated Isothermal Amplification)  
The isothermal LAMP DNA amplification technique requires a set of four specially designed primers (F3, B3, an internal forward primer (FIP), and an internal reverse primer (BIP)) that recognise a total of six different template DNA sequences (F1, F2, F3, B1, B2, and B3).  
During the determinations carried out with the LAMP technique, the following template sequences, characteristic of the tested periodontitis, were used:  
1. _P. gingivalis_--chromosome, complete genome;
2. _F. nuclatum_--transcription termination/anti-termination protein (NusG);
3. _T. forysthia_--peptidylarginine deaminase.  
The sequences of the primers used in the analysis were designed using the software Primer Explorer version 2.0 (Fujitsu Co., Ltd., Tokyo, Japan)--Table S1. Their specificity was initially verified using the BLAST software on the National Center for Biotechnology Information's server ([[http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)]([http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/))) and then confirmed by an internal LAMP test. Reagents were manufactured at OptiGene Ltd. (Unit 5 Blatchford Road, Horsham, West Sussex, RH13 5 QR, UK).  
The LAMP isothermal amplification reactions were carried out in a volume of 10 \(\upmu\)L with the use of the Isothermal Master Mix reagent kit (Novazym, Poznan, Poland) dedicated especially for this type of analysis. The reaction mixture contained GspSSD polymerase allowing rapid amplification, reaction buffer, thermostable inorganic pyrophosphatase, MgSO\({}_{4}\), a mixture of nucleotides (dNITPs), a fluorescent dye (derivative of 6-carboxylfluorescein), and appropriate primers and tested DNA templates.  
The amplification process was performed using a Rotor Gene thermal cycler (Qiagen, Hilden, Germany). The following cycle scheme was carried out: holding at 63 \({}^{\circ}\)C for 2 min, cycling at 63 \({}^{\circ}\)C for 10 s (cycle was repeated 90 times), and melting ramping from 65 to 98 \({}^{\circ}\)C, rising by 0.5 \({}^{\circ}\)C at each step. The bacterial DNA templates were subjected to a melting point ranging from 70 to 95 \({}^{\circ}\)C. The fluorescence measurement was performed with a temperature change in the range of 1.0 \({}^{\circ}\)C per minute. In order to verify the results, the detection of the relative fluorescence intensity and melting point values for individual strains was performed using the FAM Green fluorescence channel in the range of 470 nm (Figure S1).
# Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques
## 4 Reliability Analysis of CFRP-Packaged FBG Sensors Using the FTA Technique
### FTA of CFRP-Packaged FBG Sensors  
The failure modes with higher RPN are defined as the key faults [25]. According to Section 3, the 19 failure modes of CFRP-packaged FBG sensors are listed in descending order by their RPN shares (Figure 11). From Figure 11, it can be seen that the failure modes with the highest RPN share are \"FBG-FM7 (with RPN share 10.20%)\", \"BL-FM15 (with a total of 9.56% RPN shares)\", and \"CC-FM17 (with a total of 9.56% RPN shares)\".  
Figure 10: Flow chart of fault tree analysis.  
Figure 9: The RPN and RPN shares of each subsystem: (**a**) RPN and RPN shares of FBG failures; (**b**) RPN and RPN shares of PL failures; (**c**) RPN and RPN shares of BL failures; (**d**) RPN and RPN shares of CC failures; (**e**) RPN and RPN shares of FBG-D failures.
## 3 Neural Network Based DPD Architecture
### Features of the NN Model  
This section will discuss all the features of the Neural networks that are essential to be used in DPD based RoF system. The summary of the topics discussed in this section is given as follows. Firstly, Section 3.1.1 discusses the loss functions while Section 3.1.2 talks about optimizers. Section 3.1.3 talks about activation functions and Section 3.1.4 talks about hyperparameters. Section 3.1.5 discusses the regularization methods and Section 3.1.6 talks about are characteristics of NN.  
The NN are layered structures that have been inspired from the human nervous system and are an adequate choice due to their powerful accuracy at approximating any nonlinear function and learning the relationship between their inputs and outputs. The basic working involves an input signal provided at the input layer, processing which takes place in the hidden layer/layers and the output is produced by the last (output layer). Each layer represents a cluster of neurons which are not connected to one another within the layer but are connected to the neurons of the next layer.
# Real-Time Foreign Object and Production Status Detection of Tobacco Cabinets Based on Deep Learning
## 2 Materials and Methods  
### Acquiring the Detection Data of the Tobacco Cabinet  
This paper mainly focuses on the detection of foreign objects and the production status of the tobacco cabinet. The production status detection includes the feeding status detection of the feeder and the conveyor belt status detection of the tobacco cabinet.  
For the collection of foreign object status, the authors define foreign objects as the objects that should not appear in the tobacco cabinet during the production of shredded tobacco, such as brooms, mops and people. Thus, the authors collect data on people, brooms, and mops in different tobacco cabinets. To avoid data duplication, the authors collect data in a variety of situations, such as workers, brooms and mops in different positions of the tobacco cabinet, etc. Since the light source in the workshop comes from the lamp, after the production is completed, the worker will turn off the light. At this time, the industrial camera will automatically switch to infrared mode to shoot gray images. Therefore, our data are divided into two types: gray images and color images with RGB three channels. We collected a total of 1548 foreign object data with an image size of 2560 \(\times\) 1440.  
As the cigarette factory is divided into different workshops such as the leaf cabinet, the expansion cabinet and the pre-distributed cabinet, the shape and size of the tobacco cabinets in different workshops are different, so the authors collect data on all kinds of tobacco cabinets. The authors use an industrial camera to collect 24 h of uninterrupted video recording of all the tobacco cabinets. To prevent the data from over-fitting, the authors only intercept one image of the same situation of the tobacco cabinet over a period of time. Finally, 684 empty material data, 802 full material data and 905 material data were collected.  
Figures 1 and 2 show the original data of foreign object data and feeder in RGB and gray images. Figure 3 shows the status data of the tobacco cabinet conveyor belt.  
Figure 1: Foreign object data. (**a**) Broom (RGB image). (**b**) Mop (RGB image). (**c**) Human (RGB image). (**d**) Broom (gray image). (**e**) Mop (gray image). (**f**) Human (gray image).
## 2 Experimental Design and Data Collection
### Waveform Analysis  
The waveform obtained from the three-phase fault arc simulation experiment is shown in Figure 3. Here, the abscissa is the time axis, and the unit is second (s). The ordinate is voltage, expressed in volts (V). According to the experimental waveform, the frequency converter uses SPWM, that is, sinusoidal pulse width modulation, for single-phase to three-phase conversion and speed regulation. Different from the regular high-frequency waveform with large amplitude variation, the fault is characterized by a chaotic high-frequency burr waveform with small amplitude variation. Among them, the fault characteristics of phase A are more obvious in each stage. The fault characteristics of B and C two-phase faults are different in different stages. The phase B fault is more obvious in the first and third stages, and the phase C fault is more obvious in the second and fourth stages, indicating that the phase characteristics of different faults have strong timing.  
Figure 2: Fault generator.  
Taking the A-phase fault as an example, as shown in Figure 4, due to the change of experimental environment, the different intensity of arc combustion, the uncontrollable length of arc pulling gap and the change of contact area between fault poles caused by dust after carbon rod combustion, the fault characteristics of the same type of data are quite different. Therefore, the robustness of the model is required.
## 6 W-Manipulation
### W-Manipulation for Link-Additive Stability
If we restrict our attention to procedures that return optimal link-additive or link-max stable matchings, we can still prove that all such procedures are strictly w-manipulable, and they are w-manipulable when all top choices have the same weight. In fact, the same examples in the proofs of Theorems 6 and 7 still hold. We now show that not all the profiles of an SMW can be expressed as a problem, \(Link(P)\), for some \(P\), and thus, some classical forms of manipulation cannot be adopted in the context of link-based stability. In particular, we will show that the profiles, \(P\) and \(P^{\prime}\), which allowed [PERSON] in [9] to state that all the stable marriage procedures are manipulable, cannot be used in our context to prove classical manipulation, since there are no SMWs, \(P\) and \(P^{\prime}\), such that \(P=Linka(P^{*})\) and \(P^{\prime}=Linka(P^{**})\). **Theorem 8**: _Given an SM, \(P\), it may not be possible to find an SMW, \(P^{*}\), such that \(Linka(P^{*})=P\)._  
**Proof:** Let \(\{w_{1},w_{2},w_{3}\}\) and \(\{m_{1},m_{2},m_{3}\}\) be, respectively, the set of women and men. The SMW, \(P\), is as follows: \(\{m_{1}:w_{2}>w_{1}>w_{3}\), \(m_{2}:w_{1}>w_{2}>w_{3}\), \(m_{3}:w_{1}>w_{2}>w_{3}\), \(w_{1}:m_{1}>m_{3}>m_{2}\), \(w_{2}:m_{3}>m_{1}>m_{2}\), \(w_{3}:m_{1}>m_{2}>m_{3}\}\). Assume that there is an SM, \(P^{*}\), such that \(Linka(P)=P^{*}\). Then, the following conditions must hold: (1) \(la(m_{1},w_{2})>la(m_{1},w_{1})\), (2) \(la(m_{3},w_{1})>la(m_{3},w_{2})\), (3) \(la(m_{1},w_{1})>la(m_{3},w_{1})\), and (4) \(la(m_{3},w_{2})>la(m_{1},w_{2})\). This implies \(la(m_{3},w_{2})>_{(item4)}la(m_{1},w_{2})>_{(item1)}la(m_{1},w_{1})>_{(item3)}la (m_{3},w_{1})>_{(item2)}la(m_{3},w_{2})\). Thus, we have \(la(m_{3},w_{2})>la(m_{3},w_{2})\), which is a contradiction. \(\Box\)  
Notice that the theorem above holds independently from how the link of a pair is computed. Starting from the above result, we can show that some classical forms of manipulation cannot be adopted in the context of link-based stability. **Theorem 9**: _The SMs, \(P\) and \(P^{\prime}\), which allowed [PERSON] in [9] to state that all the stable marriage procedures are manipulable, cannot be used to show classical manipulation when we consider link-based stability notions._  
**Proof:** The SMs, \(P\) and \(P^{\prime}\), considered in [9], are defined as follows. \(P\) coincides with the problem considered in the proof of Theorem 8, and \(P^{\prime}\) is obtained by \(P\), by replacing the preference of \(w_{1}\) as follows: \(m_{1}>m_{2}>m_{3}\). \(P^{\prime}\) is the problem where \(w_{1}\) misreports her preferences and she gets a better result than the one obtained in \(P\) by telling the truth. In Theorem 8, we have shown that there is not an SMW \(P^{*}\), such that \(Linka(P^{*})=P\). Moreover, by using the same reasoning done in the proof of Theorem 8, we can show that there is not an SMW \(P^{**}\), such that \(Linka(P^{**})=P^{\prime}\).
## 2 Materials and Methods
### Loss Function Construction
#### 2.1.3 Weighted Adaptive Focus Loss Function  
To address the issues of category imbalance and uneven classification difficulty encountered in ability evaluation, this paper proposes the integration of category weights and the newly developed adaptive focal loss to enhance the cross-entropy loss function. This enhancement aims to bolster the ability evaluation model's proficiency in identifying samples characterized by low ability levels and greater classification challenges.  
**Definition 6**.: _Let \(y_{i}\), \(p_{i}\) be as defined in Definition 1 and let \(\alpha_{i},s_{j}\) be as defined in Definitions 2 and 5. Then, the weighted adaptive focus loss function is expressed as_  
\[L_{\alpha-Adaptive-Focal}\ (p_{i},y_{i})=-\alpha_{i}s_{i}y_{i}{}^{T}\log(p_{i})= \alpha_{i}s_{i}L_{\text{CE}}(p_{i},y_{i}) \tag{8}\]  
This loss function, outlined in Definition 6, amalgamates the category weights \((\alpha_{i})\) of the samples and the classification difficulty \((s_{i})\). Consequently, the training of the parameters in the ability evaluation model can be more acutely fine-tuned toward the samples with low ability levels that are not readily categorized. The values for \(\alpha_{i}\) and \(s_{i}\) can be deduced from Equations (2) and (6), respectively.