esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Nowadays, in every digital museum, inside the object showcase room, an electronic visual display and surveillance camera were fixed for each precious object. These display screens were used to present the history of an object in the form of moving images which will improve the quality of visitors' experience [1; 2; 3; 4; 5]. Similarly, surveillance cameras were used for monitoring the objects [6] and the visitors' movement [7; 8]. Due to the advancement and advantages of Optical Wireless Communication (OWC), Optical Camera Communication (OCC) have been used in many locations [9]. A primary OWC system consists of a single LED and photodiode, with the combination of the LED and photodiode acting as transmitter and receiver. The data rate of such a system depends on the reception rate of the photodiode [10]. Later on, the MIMO concept was extended to OWC systems [11]. In the OWC system, to implement the MIMO concept, LEDs are arranged in a matrix form called a Light Emitting Array (LEA). Each LED in an LEA is considered as a transmitting antenna, transmitting useful information in parallel. The LEA is captured using a camera and a region of each LED is extracted. The extracted region of the LED is considered as a receiving antenna. Hence, the system is called a _visual MIMO communication system_[12; 13; 14]. The visual MIMO communication system has found wide applications in outdoor settings such as Vehicle to Vehicle communications (V2V), Object tracking, and so on [15; 16; 17]. It is also tested in indoor applications [18; 19]. Recently the authors proposed a novel Visual MIMO interface for interfacing computer and control systems [20]. This technique was further implemented as a screen-camera communication device by considering each pixel in the display and camera as a transmitting and receiving antenna [21]. To achieve hidden communication between the display and camera, various time and transform domain image steganographic methods were introduced [22; 23; 24; 25]. From the captured images, specifically ROI, having few straight lines, were detected by Hough transform (HT) [26]. Similarly, for object detection, nowadays more efficient filters are used [27].  
The significant contribution of this paper is (i) the realization of a dual security service using a single vision sensor; (ii) the use of a Kalman filter algorithm for object monitoring; (iii) the extraction of a display screen using Hough transforms; (iv) the implementation of a robust invisible visual MIMO communication system using IWT based ARC-LSB substitution technique; and (v) investigation into the performance of the proposed system using standard measurement parameters.  
This article has been organized as follows. Section 2 presents the proposed system model. Section 3 investigates & compares the performance of the proposed algorithm against the existing algorithm. Finally, Section 4 concludes the paper.
# A Coordinated Supply Contract for a Two-Echelon Supply Chain Considering Learning Effects
## 4 Coordinated Supply Contract under the Learning Curve (LC)  
This section outlines a procedure for achieving overall supply chain optimization in the presence of learning effects. The manufacturer acts as a Stackelberg leader, initiating the process by designing and presenting the contract. Based on the contract terms, the retailer determines its order quantity to maximize expected profits. The contract employs revenue sharing with two key parameters: the wholesale price (\(w\)) and the revenue-sharing ratio (\(\phi\)). The supply contract parameters are determined to ensure that the retailer's order quantity aligns with the global solution.  
The sequence of events in our supply-contract scheme under learning effects is as follows:  
1. The manufacturer predicts production costs, \(c_{Q}\), depending on the production amount and determines \(Q_{LC}^{*}\) so that the SC profit can be maximized. The production costs are assumed to follow the log-linear function given in expression (2);
2. The manufacturer designs the revenue-sharing contract with the wholesale price \(w\) and the profit-share ratio \(\phi\), and presents the contract to the retailer;
3. The retailer determines the order quantity \(Q_{Lr}\) to maximize the profit based on the uncertain demand, and places an order to the manufacturer;
4. The manufacturer produces and delivers a quantity \(Q\) and receives payment from the retailer.
5. The retailer sells the product at a given selling price during the sales season and gives a portion of the revenue to the manufacturer (revenue sharing);
6. At the end of the season, the retailer disposes of unsold products for a salvage price.  
In the following subsections, we introduce how the manufacturer can determine the optimal production quantity when the learning effects exist and set the contract parameters that lead to the retailer's order quantity aligning with the global solution.
# Drilling Condition Identification Method for Imbalanced Datasets
## 2 Feature Selection Algorithm Based on Weighted Symmetric Uncertainty  
Feature selection is an important dimensionality reduction technique. Its core goal is to remove irrelevant, redundant, and noisy features to extract the optimal subset of features, thereby improving the generalization ability of the model, reducing the risk of overfitting, lowering computational costs, and enhancing model interpretability [22; 23]. Based on the relationship between feature selection and the model, feature selection methods are generally divided into three categories: filter methods, wrapper methods, and embedded methods [24]. Among these, filter methods ignore the relationship between features and model adaptability during the selection process, making it difficult to uncover the combined value of features. Embedded methods require hyperparameter tuning to optimize the model, which incurs high computational costs and poor transferability. Wrapper methods, on the other hand, assess the quality of feature subsets using a specific model, allowing for the full exploration of the potential advantages of feature combinations.  
When traditional feature selection methods are applied to handle imbalanced drilling data, the distribution of each feature often exhibits bias due to the imbalanced data distribution. Under this imbalanced distribution, the features selected by traditional methods often align more closely with the characteristic patterns of majority-class conditions. When these biased features are fed into the classifier for condition classification, the recognition accuracy for majority-class conditions becomes significantly higher than that for minority-class conditions. As a result, minority-class conditions are difficult to identify effectively during classification, rendering the model's classification results unreliable and lacking reference value.  
To address this issue, this paper proposes a wrapper-based feature selection method. In this method, weighted symmetric uncertainty is used to calculate the correlation between different drilling condition categories and features, assigning different weights to featuresaccordingly. During the feature selection process, this ensures that features critical for minority-class recognition are not removed, allowing the feature set to comprehensively reflect the data variation across different conditions. Ultimately, this approach balances the classification performance across various drilling conditions and enhances the reliability of the model's classification results. Specifically, the weighted symmetric uncertainty index between each feature and the drilling condition categories is first calculated to generate a candidate feature subset. Then, a sequential search mechanism is employed, using a classification model as the evaluation tool for the quality of the feature subsets. Based on the model's classification performance on different feature sets, the optimal feature set is selected, thereby improving the model's classification performance under class imbalance conditions.
## 2 Materials and Methods
### Antioxidant Activity  
The antioxidant activity of the essential oils was evaluated with three assays, Folin-Ciocalteu, DPPH and ABTS. The results obtained from two radical scavenging assays (ABTS and DPPH) were compared with the commercial antioxidant, BHT.  
The reductive capacity of the antioxidants present in the essential oils was evaluated using a Folin-Ciocalteu method [19]. The Folin-Ciocalteu reagent (mixture of phosphopholybodic and phosphotungstic acids) was diluted by mixing 10 mL of the FC reagent with90 mL of distilled water. The essential oils were diluted with methanol. A measure of 100 \(\upmu\)L of the methanolic solution was mixed with 0.4 mL of distilled water and 1 mL of diluted FC reagent. After 10 min, 2 mL of a 7.5% aqueous Na\({}_{2}\)CO\({}_{3}\) solution was added. After 1 h, the absorbance was measured at 750 nm. Gallic acid was used as a standard. Antioxidant activity, AA, was expressed as gallic acid equivalent (GAE) in mg GA/mL.  
The radical scavenging ability of the essential oils was evaluated using a modified DPPH assay, according to [20]. A 0.36 mM solution of the free radical DPPH was prepared by dissolving it in methanol. A 0.1 mL sample solution (methanolic solution of the essential oil) at different concentrations, was then mixed with 0.1 mL of DPPH solution in a 96-well plate and incubated in the dark at 25 \({}^{\circ}\)C for 30 min. A blank sample was prepared by mixing 0.1 mL of methanol with the DPPH solution. The absorbance of each reaction mixture was measured at 517 nm.  
The ABTS assay was performed according to the previously described procedure [21] with slight modifications. To generate the ABTS radical, equal volumes of aqueous ABTS solution (4.064 \(\upmu\)g/mL) and aqueous potassium peroxdisulfate solution (0.704 \(\upmu\)g/mL) were mixed and incubated in the dark for 14 h. After the incubation period, the absorbance of the prepared solution was adjusted to 0.7 by adding methanol at 734 nm. The reaction mixture contained 200 \(\upmu\)L of the sample solution, in different concentrations and 3800 mL of ABTS solution. After an incubation period of 7 min (in the dark) the absorbance was measured at 734 nm. The blank sample contained 200 \(\upmu\)L methanol and 3800 mL ABTS solution.  
For both assays the inhibition was calculated as follows:  
\[\%\text{ inhibition}=\frac{ABS(\text{blank})-ABS(\text{sample})}{ABS( \text{blank})}.100 \tag{2}\]
# Study of the Algorithm for Wind Shear Detection with Lidar Based on Shear Intensity Factor
## 3 Test of the Improved Algorithm Based on Numerical Simulation  
Four typical wind fields with wind shear are simulated based on MATLAB toolbox (Natick, MA, USA), including frontal wind field, downburst wind field, low-level jet wind field and turbulent wind field. Lidar is simulated to scan the slide path in the four simulated wind fields, then we can use the simulated lidar data and different wind shear detection algorithms to detect the wind shear in the four simulated wind fields. In this way, we can reasonably compare the effectiveness of different algorithms in wind shear detection. In the numerical simulation, the position of lidar is set to be the same as the landing point of the slide path; the elevation angle of the laser beam and slide path are both set to be 30 ; the azimuth angle of the laser beam is set to be 45'the range resolution of lidar is set to be 1000m; and the simulated wind field is limited to a space area of 10,000 10,000 10,000 m\({}^{3}\).  
According to the standard of wind shear intensity from the International Civil Aviation Organization (ICAO), when the wind shear intensity value is between 0 and 0.07 S \({}^{1}\), it is rated as mild wind shear. When the wind shear intensity value is between 0.08 S \({}^{1}\) and 0.13 S \({}^{1}\), it is rated as moderate wind shear; when the wind shear intensity value is between  
Figure 5: Framework of the project in this paper.  
0.14 S 1 and 0.20 S 1, it is rated as strong wind shear; when the wind shear intensity value is greater than 0.20 S 1, it is rated as severe wind shear. The wind shear intensity factor value \(S_{simple}\), which corresponds to the standard of ICAO, can be calculated based on the wind shear intensity values, which are shown in detail in Table 1.  
Footnote 1: The wind shear intensity is defined as \(S_{simple}=\frac{\partial S_{simple}}{\partial S_{simple}}\).
## 2 Materials and Methods
The charge represents the electrostatic potential of macromolecules and accurately models the interactions between the ligand and the receptor during docking [40]. A grid box measuring 40 \(\times\) 40 \(\times\) 40 A, centered on the active site of ER\(\alpha\) (x = 30.010, y = \(-\)1.913, and z = 24.207), was set up for the docking simulation conducted with AutoDock 4.2. The Lamarckian Genetic Algorithm was run iteratively 100 times. The ligand conformation with the lowest free binding energy (\(\Delta\)G) was selected from the generated clusters for further investigation of ideal ligand interactions. Discovery studios Visualizer 2024 (Dassault Systemes BIOVIA\({}^{\text{\textregistered}}\), San Diego, CA, USA) was used to clarify receptor-ligand interaction [41; 42]. **Molecular Dynamics Simulation:** The molecular dynamics simulations were performed using the AMBER ff14 SB protein force field [43]. In the beginning, we generated partial charges of ligands using Austin Model 1--Bond Charge Corrections (AM1-BCC), which were implemented in the antechamber program AmberTools22. The ER\(\alpha\) structure was set up with pdb4 amber and set ff19 SB force field [44]. TIP3P water was placed into a 10 \(\times\) 10 \(\times\) 10 A box for the simulation setting, and the ligands were subjected to the general AMBER force field [45]. Topology files were created after the ligand's charges were balanced using a limited electrostatic potential. The minimization, heating, and equilibration procedures were completed using the Sander module [46]. For both ligands and receptors, a heating function was applied prior to starting the production phase of the molecular dynamics simulations. This required a three-step heating procedure that mimicked physiological settings by gradually increasing the system temperature from 0 to 310 \({}^{\circ}\)K at regular intervals [47]. The system was then allowed to stabilize via equilibration, arriving at a steady state before molecular dynamics simulations were generated. To further evaluate the structural dynamics and fluctuations in the system, investigations using Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) were carried out.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 4 Approach, LPWAN Communication Technology, and Energy Consumption Analysis
### LPWAN Communication Technology
#### 4.2.3 Sigfox
We have calculated the energy consumption for a unidirectional packet transmission from the end-device as depicted in Figure 17. The analysis of the power consumption for different process in the communication such as current, time, and energy consumed are presented in the Table 5. The states in the Figure 17 demonstrates the different process involved in communication such as: state 1,8 is the sleep mode; state 2 is the phase where device warms up and activates the module; state 3 is the packet transmission for the first message; state 4,6 is the wait time before sending the replica message; and state 5,7 are the transmission of the replica message for the first original message. Figure 15: Sigfox network architecture. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**State** & **Process** & **Time (ms)** & **Avg. Current (mA)** & **Energy (mJ)** \\ \hline
1,8 & Sleep & variable & 0.0065 & variable \\
2 & Module active & 316.60 & 2.06 & 2.46 \\
3 & Tx 1 & 1798 & 48.32 & 321.45 \\
4,6 & Wait time & 48.03 & 3.78 & 0.67 \\
5 & Tx 2 & 1795 & 49.50 & 328.75 \\
7 & Tx 3 & 1803 & 49.57 & 330.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Current composition split into different states of operation for Sigfox.
## 6 Discussion
### Limitations  
Our models and conclusions about altered driving behavior are based on a narrowly defined, homogeneous group of participants. It is essential to verify whether these findings can be generalized to a broader population, as the classification accuracy may shift with a more diverse subject pool. Factors like varying drinking habits and driving experience could impact the reliability of detection [28; 30; 35].  
Additionally, this study was conducted under controlled, optimal conditions. Participants were required to appear sober, having consumed only a light snack, and to abstain from alcohol, caffeine, or medication prior to the study. Given these conditions and the fact that the study took place in a driving simulator rather than a real vehicle--where the driving routes were identical each time--the applicability of the results to real-world scenarios must be considered. Furthermore, individual factors such as daily physical and mental conditions, as well as inherent differences between participants, could strongly influence the results. Factors like adrenaline rushes, fatigue after partying, or other variables that impact real driving behavior were not accounted for.  
However, this study's primary objective was to provide a direct comparison between sober and intoxated driving. The controlled simulator environment, with its consistent conditions, was the most suitable approach for achieving this comparison. Nonetheless,the validity of these findings could be strengthened by increasing the number of participants and conducting tests in a more realistic driving environment.
# Mechanical and Fatigue Behavior of Cellular Structure
## 4 Conclusions  
The aim of this study is to identify the fatigue durability of the cementless functionally graded and homogeneous porous structure femoral stems. Fifteen different volumetric porosities arrangements within the stems layers in a radial direction were grouped into three average porosities of 30%, 50%, and 70%. Different physiological loading conditions corresponding to three walking speeds (1 km/h, 3 km/h, and 5 km/h) were applied on the stem-femoral bone assembly. The von Mises stresses were computed within the stem's layers and studied against the yield strength associated with the relevant porous layer porosity. Fatigue factors of safety were computed for each design under the three loading conditions using Sodeberg approach. The outcomes obtained from this study lead to the following conclusions:  
The porous structure's mechanical properties varied as the volumetric porosities changed and this is affecting the stems performance and fatigue endurance under daily human physical activities;  
Changing the porosities arrangement within the stem's layers affects the stems durability and the fatigue performance;  
Designs D1, D2, D3, and D5 with volumetric percentage of 30%, D6, D8, and D10 with volumetric percentage of 50%, and D11 and D15 with volumetric percentage of 70% were found to be safe at walking speed 1 km/h in terms of von Mises stress values compared with the associated yield strength and factor of safety based on Soderberg approach;  
Design 8 (D8) is the best candidate that found not failed up to walking speed of 2 km/h.  
The performance of the proposed designs can be enhanced by adding outer dense shell to the porous stem structures with different thicknesses which will be part of our future study.  
**Author Contributions:** Formal analysis, N.F.A.Z.; Funding acquisition, F.T.; Investigation, N.F.A.Z.; Supervision, F.T. and H.M.; Writing-original draft, N.F.A.Z.; Writing-review & editing, F.T. and H.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was funded by QNRF grant no NPRP 8-876-2-375 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methodology and Categorization
### Context and Content  
The design and development of passive antenna sensors and systems including direct and indirect sensing through antennas remain a challenging task. The major issue arises because of a tradeoff among sensing and communication, in particular between resolution, sensitivity, size, read range, and robustness. This tradeoff and more relevant issues, as shown in Figure 3, influence the choice of antenna type, sensing principle, substrate material in the tag, implementation of test strategies and selection of sensing variables in the reader, and development of the feature extraction method. Most of them will be covered in the following sections.
## 3 Proposed Methodology
### Issa
To solve the problem that SSA is easy to fall into the local optimal solution, an improved sparrow search algorithm (ISSA) is proposed in this study. Firstly, a normal migration strategy is proposed, one which bases the population migration on the center of gravity position and which realizes the normal distribution attenuation of moving energy. This improvement effectively improves the exploration ability of local search and enables the algorithm to explore the solution space more efficiently and find better solutions in the search process. Secondly, the ISSA algorithm introduces a dynamic sinusoidal perturbation strategy. This strategy realizes the discoverer's bidirectional demand for search step size in the early stage and fast convergence in the late stage through the scaling factor. At the initial stage of the search, the algorithm needs a larger search step size to explore a wider solution space. In the latter stage of the search, smaller search steps are needed to converge to the optimal solution quickly. The dynamic sinusoidal perturbation strategy satisfies this demand by adjusting the scaling factor, thus improving the search efficiency and precision of the algorithm. Such a process can be realized as follows:  
\[x_{i,j}(t+1)=\left\{ \begin{array}{c}Levy(d)\cdot x_{bestj}(t)+\beta\Big{|}x_{ i,j}(t)-Levy(d)\cdot x_{bestj}(t)\Big{|},\ f_{i}>f_{g}\\ x_{i,j}(t)+K\Big{(}\frac{|x_{i,j}(t)-x_{worstj}(t)|}{(f_{i}-f_{g})+\varepsilon} \Big{)},f_{i}=f_{g}\\ \\ Levy(d)=0.01\cdot\frac{r_{1}\cdot\sigma}{|r_{2}|^{\frac{1}{2}}}\\ \\ \sigma=\left\{\begin{array}{c}\Gamma(1+\beta)\cdot\sin\left(\frac{\pi g}{2} \right)\\ \Gamma\Big{(}\left(\frac{1+\lambda}{2}\right)\beta\cdot 2^{\left(\frac{\beta-1}{2} \right)}\Big{)}\end{array}\right\}^{\frac{1}{2}}\end{array} \right. \tag{4}\]  
Using the improvements proposed in this study, the ISSA model greatly reduces the risk of sparrows falling into local optimality while maintaining sufficient local search capability. Random numbers of [0,1] can be employed for \(r_{1}\) and \(r_{2}\).