esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
## 2 Related Work
For instance, a study on edge-case scenarios trained a tomato disease detector solely on iteratively refined synthetic images, achieving competitive performance on real data. This approach could be adapted to poultry farming by simulating flock images under different conditions [38]. Another study presents a comprehensive survey on the role of synthetic data in computer vision, emphasizing how GANs can generate realistic imagery that preserves essential features such as feather texture, lighting variations, and occlusions. The study highlights that well-trained GANs can reduce the dependency on costly real-world data collection, making them a practical tool for scaling deep learning applications in agricultural monitoring [39]. In another research, authors explored the effectiveness of blending synthetic and real data for object detection and classification tasks. Their findings suggest that incorporating 30-50% GAN-generated images into training datasets can improve model generalization. However, the study also underscores the importance of domain adaptation techniques, as models trained on purely synthetic data may struggle with real-world variability if proper calibration is not applied [40]. Most existing related studies have demonstrated the value of synthetic data in agriculture, primarily comparing its effectiveness to real data. The extensive exploration on how varying proportions of real and synthetic images impact model performance is still lacking. This study takes a systematic approach by analyzing different dataset compositions, providing insights into the optimal balance for improving detection accuracy over real data. By doing so, a practical framework for integrating synthetic data in AI-driven poultry monitoring is offered, helping to address challenges related to data availability while maintaining model reliability.
## 3 Results and Discussion
### Factors Investigation
#### 3.2.1 Catalyst Particle Shape
However, their wetting efficiencies were comparable, as the small inner holes of hollow cylinders got wet easily due to the capillary effect. It can be concluded that sphere particles were suitable in situations which prefer low liquid holdup and wetting efficiency, while single-hole and 4-hole cylinders showed the opposite characteristics. Cylinder particles give lower liquid holdup, but higher wetting efficiency. As for the pressure drop, the cylinder packing got the highest one due to the lowest porosity. Comparing cylinder and sphere packing, their liquid holdup and saturation were similar, but the lower porosity gave less free space for gas flow between cylinders, making a remarkable higher pressure drop. While for the single-hole and 4-hole cylinders, whose liquid holdups were much higher than sphere and cylinder packings, the high porosity provided a lot of space for gas flow even though most of them were filled with liquid. Therefore, their pressure drops were lower than the cylinder packing. However, the pressure drop in 4-hole cylinders packing was slightly higher than that in the single-hole cylinder. Comparing these four kinds of particles, the 4-hole cylinder has both the highest liquid holdup and wetting efficiency, which are preferred by most of the gas-liquid-solid catalyzed systems. Therefore, the 4-hole cylinder was decided as the main investigation object in the following chapters.
## 1 Introduction
The most challenging issue hereby is that a large-scale \"dynamic\" (i.e., with differential/difference equations rather than only algebraic equations) [6], including mixed-integer nonlinear programming (MINLP) difficulties which must be solved in real-time. Software simulation cannot explain everything as a real and practical benchmark. It is necessary to develop a new hardware simulation that can test the battery pack based on the scaled-down ratio. This opens the opportunity for more advanced technologies, with lower costs during testing and manufacturing process. In this paper, a scaled-down system for the real-time test of the battery pack for EVs is introduced. Since the hardware simulator can charge and discharge any types of rechargeable batteries as confirmed in [7; 8; 9; 10; 11; 12; 13; 14; 15], it can practically simulate the charging and discharging characteristics using a single cell up to four series connection model constituting the battery pack installed in the storage system. In order to confirm the reliability and the accuracy of the proposed simulator, real data obtained from EV tests were used to confirm the efficacy of the model. Finally, a synthesis algorithm of an equivalent circuit of each battery was implemented into the simulator using the abilities of ANN which accelerate the estimation in case of reduced data by increasing the vast of testing scenarios. A further investigation will be made on the optimization and simplification of the ANN structure by reducing the necessary input cells for accelerating the calculation, training and estimation without sacrificing the accuracy. During estimation, the results are compared at the same time with a practical test based on unsupervised learning process (UL). If the accuracy is low, then ANN will continue to train and updating its weights until the error is reduced and the structure is finally optimized. This helps for the implementation of real-time estimation of the SoC or SoH of the battery as well it can enrich the generalization of the model. Although the SoH estimation is not the main focus of this manuscript, the authors are under investigation regarding this matter, and some papers are already published using the proposed MTC device as a cost-efficient method of SoH evaluation method. In simple words, this study proposes an alternative and cost-effective approach of benchmark stress test and diagnosis of the BSS during the early designing phase comparing to high-cost industrial devices. Also, through this study it was possible to obtain an accurate SoC estimation of the BSS without compromising the high generalization of the proposed model, which can be used not only in EV's but as well in the renewable generation system. Comparing to other methods of SoC estimation is lacking in generality, which leads to wrong SOC estimation in certain battery types. This method is based on a high accurate and reliable model in order to obtain the characteristics of the real-life battery and predict its behavior under a wide variety of conditions. In addition, it is a time saving and mobile friendly approach for low- and medium-power applications.
## 5 Vibration Control of an Isolated Structure
### Adaptability, Controllability and Nonlinearity  
With the implementation of \"smart\" magnetorheological elastomers, an adjustable broadband frequency-tunable isolator can provide adaptability of natural frequency for real-time vibration control against non-stationary disturbance. In order to evaluate the controllability of MRE isolator with developed control algorithms, a sweep sine wave is used to excite the structure. Acceleration transmissibility is calculated to measure the effectiveness of vibration mitigation. Considering high nonlinearity of the material of the isolator, structural system and the control system, system transmissibility is used to assess the controllability of the structure.  
Taking Model 5 as an example, sweep sine wave is used as the input signal and the responses of different floors are regarded as output signals. The sweep sine signal has a changing frequency ranging from 0 Hz to 150 Hz at the end of the excitation signal. Sampling rate is chosen to be 1000 Hz. Figure 23 shows the relationship between the system transmissibility function and the system frequency. All transmissibility functions indicate the intrinsic of the structural system is within 0-2 Hz. For the structure without control, the transmissibility functions of all floors are almost similar, which means that the structural integrity is good. But for the structure with control, the transmissibility functions differentiate from one to another. This illustrates good controllable performance of the structure. For reaching law-based control, chattering is also obvious, which can be inferred from the transmissibility of the base floor. Although the control forces adaptively change according to the magnitude of the excitation, they change slightly slow and gradual. Thus, former excitation has a lasting and longer influence on the subsequent structural dynamic response. For the RBF based control, the transmissibility functions between different floors are very smooth but separate.  
Figure 22: Max Control Force for Different Control Schemes.  
This type of control is modeled by replacing the system matrix with an RBF approximation in the state space expression. The system response with the RBF adaptive control almost simultaneously follows the seismic excitation, showing a good control performance and control effect.  
This demonstrates when the excitation is applied to the structure, the structure can react to the excitation quickly and adaptively with the developed adaptive control scheme. Large transmissibility values indicate better controllability which reduces the responses of the superstructure to a minimum. At the same time, energy can be absorbed and dissipated by designing the \"smart\" MRE isolation system appropriately. Most importantly, external non-stationary and uncertain disturbances are well \"absorbed\" by designing the RBF adaptive control scheme together with the isolator with the novel MRE material.
## 3 Pose Estimation for Multi-Marker Systems
### Initial Pose Acquisition and Transformation  
The initial stage involved acquiring pose measurements from all visible markers within the camera's field of view. For each _i-th_ marker, a homogeneous transformation matrix encapsulating both position and orientation information was generated as follows:  
\[T_{cm,i}=\left[\begin{array}{ccc}R_{cm,i}&\overset{\rightarrow}{t}_{cm,i}\\ 0&0&0&1\end{array}\right] \tag{6}\]  
In Equation (6), \(\overset{\rightarrow}{t}_{cm,i}\) and \(R_{cm,i}\) represent the position vector and the rotation matrix for the _i-th_ marker with respect to the camera coordinate system.
_Applied_  
_sciences_  
_Article_  
**Investigation and Enhancement of the Detectability**  
**of Flaws with a Coarse Measuring Grid and Air**  
**Coupled Ultrasound for NDT of Panel Materials**  
**Using the Re-Radiation Method**  
**[PERSON] 1,*(r)**, **[PERSON] 2(r)**, **[PERSON] 2(r)** and **[PERSON] 1(r)**  
1 Institute of Dynamic and Vibration Research, Leibniz University of Hannover, 30167 Hannover, Germany; [EMAIL_ADDRESS]  
2 Fagus-GreCon Greten GmbH & Co. KG, 31061 Alfeld, Germany; [EMAIL_ADDRESS] (T.M.); [EMAIL_ADDRESS] (J.H.)  
*Correspondence: [EMAIL_ADDRESS]  
Received: 2 December 2019; Accepted: 5 February 2020; Published: 8 February 2020  
**Abstract: Non-destructive ultrasonic testing is utilized widely by industries for quality assurance. For sensitive materials or surfaces, non-contact, non-destructive testing methods are in demand. The air-coupled ultrasound (ACU) is one possible solution. This can be used to investigate large, panel-like objects for delaminations and other flaws. For a high detectability, fine measurement grids are required (typically <\(\lambda\) is used), which results in extremely long data acquisition times that are only practicable for laboratory applications. This paper aimed at reducing the required measurement grid points for obtaining high detectability evaluations. The novel method presented in this paper allows a measurement grid that is much coarser than the resulting grid. The method combines a software refinement of the measured data with the Rayleigh-Sommerfeld diffraction integral for the calculation of the pressure distribution on the object's surface. This result allows the precise prediction of delaminations and flaws in the tested object. The presented method shows a decrease in the total investigation time by up to 98%.**  
**Keywords: Rayleigh; Sommerfeld; diffraction integral; NDT; non-destructive testing; air coupled ultrasound; flaw detection; resolution enhancement**
## 2 Materials and Methods
### Differential Expression Analysis and Functional Enrichment  
RSEM was used to quantify unigene expression levels and generate read counts for each sample. Differential expression analysis was performed using DESeq2 (v1.26.0) [36], with a significance threshold of padj < 0.01. When fewer than 10 differentially expressed unigenes (DEGs) were identified, the threshold was relaxed to an unadjusted \(p\)-value < 0.05. Functional enrichment analysis of GO terms and KEGG pathways was then conducted using the R (v4.1.2) package clusterProfiler. Significant enrichments were determined by comparing DEGs against the whole transcriptome background, with parameters set as pAdjustMethod = 'BH' (Benjamini-Hochberg) and pvalueCutoff = 0.05.
# A Coaxial Dielectric Barrier Discharge Reactor for Treatment of Winter Wheat Seeds
## 2 Materials and Methods  
### Plasma Source  
The plasma source used in this study consists of a laboratory scale dielectric barrier discharge (DBD) reactor with coaxial configuration. It comprises of a borosilicate glass tube with one closed end mounted on top of a vortex mixer, as shown in Figure 1. Around the glass tube (length of 10 cm, outer diameter of 40 mm, and glass thickness of 2.3 mm), a wrapped copper tape in spiral shape is connected to high voltage, while in its central axis, a copper rod crossing the entire length serves as the grounded electrode, forming a gap distance of 13 mm. Details of the electrode's arrangement can be seen in the cross-section cut (A-A) in Figure 1. The reactor is closed by a plastic cap that allows gas introduction. Some holes in the cap next to the gas entrance connection help control the pressure rise inside the reactor and serve as a gas outlet.  
The DBD was operated with an alternating current (AC) power supply with a frequency of 10 kHz and adjustable applied voltage. The sinusoidal voltage signal was amplitude modulated using burst mode, which helps reduce the discharge temperature. In this work, the voltage duty cycle was kept at 30% with a repetition frequency of 10 Hz for all experiments. In Section 3.5, the duty cycle was  
Figure 1: Experimental setup of the coaxial DBD reactor used for seed treatment.  
varied, but repetition frequency was kept constant. In order to assess its effectivity to seed treatment, the reactor was fed with two different gases, argon and helium, at a flow rate of 2.0 slm.
# A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion
## 4 Experimental Setup  
### The Measured Noise Source Data Set  
The measured data obtained from a 1:1 ship module model is used to validate the proposed method. The sampling frequency is 2048 Hz. The primary noise sources are the motor, the pump, and the high-frequency exciters, representing three typical fault sources with main vibration frequencies 90 Hz, 296 Hz and 360 Hz, respectively. Accelerometers PCB 352 CC were placed on the above three devices to collect the vibration signal and recorded by an 8-channel B&K 3560D pulse system. In addition, 1024 sampling points in the time domain are chosen as observation samples. The positive frequency bands of the signal power spectrum are obtained as feature vectors, and its dimension is 512. There are 900 mechanical noise samples obtained for each class, from which 800 measured samples are chosen as training samples and the remaining 100 as testing samples.
## 3 Numerical Results and Discussion
### Free Vibration Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Boundary Condition**} & \multirow{2}{*}{**Type**} & \multicolumn{8}{c}{**Modal Order**} \\ \cline{3-10}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline SFSF & Cylindrical-Cylindrical & 40.72 & 40.72 & 80.60 & 80.61 & 84.44 & 85.79 & 94.26 & 117.14 \\  & Spherical-Cylindrical & 40.22 & 45.54 & 80.32 & 84.99 & 91.37 & 94.36 & 117.49 & 118.88 \\  & Conical-Cylindrical & 36.96 & 41.79 & 72.51 & 85.06 & 89.66 & 92.25 & 108.49 & 112.10 \\ FFFS & Cylindrical-Cylindrical & 40.63 & 40.67 & 61.31 & 80.56 & 80.60 & 84.99 & 94.25 & 117.14 \\  & Spherical-Cylindrical & 40.46 & 47.00 & 80.59 & 85.02 & 91.73 & 94.41 & 117.23 & 119.00 \\  & Conical-Cylindrical & 35.55 & 45.95 & 72.03 & 85.57 & 89.88 & 92.68 & 108.50 & 112.31 \\ SCSF & Cylindrical-Cylindrical & 40.72 & 40.73 & 80.60 & 80.60 & 84.99 & 94.24 & 108.33 & 117.13 \\  & Spherical-Cylindrical & 40.22 & 45.54 & 80.32 & 84.99 & 91.37 & 94.36 & 117.49 & 118.88 \\  & Conical-Cylindrical & 38.03 & 41.31 & 72.61 & 84.63 & 89.80 & 92.30 & 108.39 & 111.61 \\ SCSC & Cylindrical-Cylindrical & 40.73 & 40.73 & 80.60 & 80.63 & 85.00 & 94.24 & 117.13 & 118.97 \\  & Spherical-Cylindrical & 40.26 & 46.28 & 80.29 & 84.91 & 91.50 & 94.17 & 117.48 & 118.83 \\  & Conical-Cylindrical & 38.51 & 40.80 & 74.63 & 83.79 & 90.86 & 95.28 & 106.32 & 112.51 \\ CCCC & Cylindrical-Cylindrical & 40.73 & 40.74 & 80.55 & 80.60 & 85.00 & 94.25 & 117.14 & 118.98 \\  & Spherical-Cylindrical & 41.07 & 46.98 & 80.78 & 85.11 & 91.16 & 94.19 & 117.98 & 118.55 \\  & Conical-Cylindrical & 38.57 & 45.26 & 67.83 & 80.51 & 87.18 & 92.83 & 108.57 & 112.34 \\ E\({}^{1}\)E\({}^{1}\)E\({}^{1}\)E\({}^{1}\)E\({}^{1}\) & Cylindrical-Cylindrical & 40.71 & 40.72 & 69.96 & 80.60 & 80.62 & 85.06 & 94.24 & 117.13 \\  & Spherical-Cylindrical & 41.36 & 46.99 & 80.51 & 85.05 & 91.90 & 94.21 & 117.13 & 119.08 \\  & Conical-Cylindrical & 38.96 & 44.47 & 72.59 & 86.07 & 89.47 & 90.95 & 104.14 & 111.78 \\ E\({}^{2}\)E\({}^{2}\)E\({}^{2}\)E\({}^{2}\) & Cylindrical-Cylindrical & 40.71 & 40.90 & 68.34 & 80.60 & 80.63 & 85.13 & 94.22 & 117.13 \\  & Spherical-Cylindrical & 41.30 & 47.06 & 80.44 & 84.95 & 91.58 & 94.04 & 117.13 & 119.00 \\  & Conical-Cylindrical & 37.39 & 44.33 & 72.77 & 85.70 & 87.06 & 92.05 & 108.37 & 111.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The natural frequencies of the coupling system between the composite laminated rotationally stiffened plate and the acoustic cavity under different boundary conditions. Figure 13: Trends of the first four natural frequencies of the coupled system between the composite laminated rotationally stiffened plate and the conical-cylindrical cavity as height L2 of the cylindrical cavity and the cone top angle \(\alpha\) of the conical cavity vary.
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 3 Reused Waste Water Dyes
### Dyes  
Another reuse option involves employing recovered colorant during another dyeing process. In this case, the clay-dye bond must be less to allow desorption, unlike the previous situation of utilizing a pigment. For this purpose, clays such as Lap are used [97; 312] that, according to tests, presents some degrees of desorption from 20% to 40%. Another example of a mineral that allows dye desorption is zeolite [109].  
According to the bibliography, desorption processes are carried out by subjecting clay to a bath with either distilled water [312] or ethanol [229], and by means of stirring. This results in dye desorption.  
A study carried out by [PERSON], [PERSON], and [PERSON] [313] (Figure 11) explains how to proceed with MB desorption by subjecting the clay-dye hybrid to temperature and then using various solvents such as HCl, ethanol, nitric acid, or acetone. Their study revealed that it was not enough to apply thermal energy to break the bonds that formed during dye adsorption by clay, which they called a chemisorption process. The solvent and heating processes alone did not provide enough energy, which is why they had to apply a thermo-chemical process to regenerate the adsorbent.  
The process begins by heating samples to about 150-200 \({}^{\circ}\)C for 45 min. Accordingly, the bonds between the adsorbent and the colorant weaken. In the next step, HCl\({}^{-}\) is introduced, which increases the charges of the positive hydrogens, which show an affinity for the clay surface and release Cl\({}^{-}\) charges with an affinity for MB. In this way, desorption is completed by releasing the colorant from the clay. As both elements are ionically neutralized, they do not rejoin.  
As a critical aspect of this procedure, the maximum temperature that both the clay and the dye can withstand should be studied by thermogravimetric (TGA) to increase the temperature of the initial phase to a maximum and to thus weaken the bonds as much as possible and facilitate the second phase by maximizing desorption efficiency.
## 4 Flexural Strength Evaluation of Corroded PC Beams  
### \(f_{ps}\) Approximation  
To evaluate the flexural strength of PC beams, ACI 318-14 [27] suggests that both the strain compatibility method and the approximate value of \(f_{ps}\), which refers to the stress of the strand when the compressive strain, \(\varepsilon_{c}\), of concrete becomes 0.003. These two methods were adopted in this paper. Equation (8) is the revised approximate expression for \(f_{c,ps}\) from ACI 318-14 [27]. The ultimate stress of a corroded strand, \(f_{cpu}\), is derived from the most corroded strand in a tendon, because the moment of the first wire rupture is defined as the ultimate strength of a corroded PC beam. The \(f_{c,pu}\) can be obtained by dividing \(F_{c,pu}\) from Equation (6) by the gross sectional area of the strand, \(A_{ps}\). The use of \(A_{ps}\) is to reflect the corrosion effect on the stress-strain curve.  
\[f_{c,ps}=f_{c,pu}\left[1-\frac{\gamma p}{\beta_{1}}\bigg{\{}\rho_{p}\frac{f_{ c,pu}}{f_{ck}}+\frac{d}{d_{p}}(\omega-\omega^{\prime})\bigg{\}}\right] \tag{8}\]  
where  
\begin{tabular}{l l} \(f_{c,ps}\): & The stress of the corroded prestressing strand or tendon when \(\varepsilon_{c}\) is 0.003 \\ \(f_{c,pu}\): & The ultimate stress of the most corroded prestressing strand in a tendon \\ \(A_{ps}\): & The cross-sectional area of the gross strand-unit section \\ \end{tabular}  
The coefficient according to the type of strand  
\(\gamma_{p}\):  
\(\left\{\begin{array}{ll}0.55\textit{ when }f_{py}/f_{pu}\geq 0.80\\ 0.40\textit{ when }f_{py}/f_{pu}\geq 0.85\\ 0.28\textit{ when }f_{py}/f_{pu}\geq 0.90\end{array}\right.\)  
The ratio between the depth of an equivalent rectangular concrete stress block and the neutral axis depth (=a/c)  
\(\rho_{p}\):  
The prestressing steel ratio \(\left(=A_{c,pt}/bd_{p}\right)\)  
\(A_{pt}\):  
The cross-sectional area of the gross tendon-unit section  
\(d\):  
The depth of the tensile reinforcement  
\(d_{p}\):  
The depth of the prestressing strand  
\(\omega\):  
The tensile reinforcement index \(\left(=\rho\frac{f_{s}}{f_{sa}},\ \ \rho=\frac{A_{s}}{bd}\right)\)  
\(\omega^{\prime}\):  
The compression reinforcement index \(\left(=\rho^{\prime}\frac{f_{s}}{f_{sa}},\ \ \rho^{\prime}=\frac{A^{\prime}}{bd}\right)\)
### Case Study No. 3: OpenPLC Linked with Node-RED Acting as a Software IIoT Gateway
#### 6.3.2 Communication  
The communication will be between OpenPLC and Factory I/O. However, we want to communicate with Factory I/O using the modern OPC UA protocol. As we already know from previous case studies that OpenPLC cannot communicate with modern protocols, so we will need a so called intermediary (middleware). The intermediary in this case will be Node-RED. OpenPLC will communicate with Node-RED using the Modbus protocol and Node-RED will further communicate with Factory I/O using the OPC UA protocol. Node-RED will provide us with the encapsulation of Modbus data to OPC UA address space as we mentioned above.