esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for 3D Plant Simulation of Meal-Kit-Packaging Robot Automation System
## 2 Materials and Methods
### 3D-Model, Meal Kit, Robot Automation System
#### 2.2.3 3D Modeling of Robot-Implemented Meal Kit Packaging Process  
In this study, we developed a framework for the 3D simulation of the meal-kit-packaging robot automation system by not only implementing robots but also automating packaging devices to determine the optimal layout in order to improve performance as well as reduce the number of workers. In this study, we designed the robot automation system composed of a delta robot (IRB360X800 WD4D, ABB, Zurich, Switzerland) with a custom-made frame (Figure 4A,B). A pneumatic rotational gripper, which is attached to the moving platform of the delta robot for the pick-and-place process of individual meal kit items, was custom-designed to be configured with two types of gripper vacuum pads: sponge cup and suction cup (Figure 4C). In addition, 5 different types of automatic, individual meal-kit-product feeders (Figure 5) were designed and implemented in 3D simulation. The individual meal kit items can be classified into two types: noodles, which are relatively heavy with an uneven surface, and the others (powder and sauce), which are light with an even surface. The sponge-type pad attached to the custom-designed gripper can pick the noodles, while the suction-cup-type pad can pick the powder and sauce.  
Figure 3: Real meal kit production site’s image and 3D-Lidar-scanned point cloud data with each production equipment. (**A**) represents the loading of each meal kit product to the conveyor belt by workers; (**B**) represents the loading of each meal kit product to the tray for packaging process.  
As mentioned above, there are 5 workers deployed in loading each meal kit into the tray which requires an automatic feeder to replace the workers. To develop a robot automation system, manual loading of the meal kit items to the conveyor can be replaced by the automation devices. There are five automatic feeding devices that we designed for each meal kit item as shown in Figure 5.
## 3 Results and Analysis
### Sensory Method and RBF-SVM Grading Systematic Error Analysis  
Two methods were used to score the Yuba skins with different concentrations, and 10 groups of Yuba skin samples were produced using the same concentration, and the scoring results were shown in Figure 10. Both methods showed that, in the concentration range of 6.5% to 8.5%, the score of the Yuba skin gradually increased with the increase in concentration. Moreover, the average score difference obtained by the two methods was small, which verifies that the results of the RBF-SVM grading system are in good agreement with the results of the actual sensory method. The average score data of each concentration using the two methods were calculated and analyzed, and the relative error results of the actual sensory method score value and the RBF-SVM grading system score value are shown in Figure 11. As shown in Figure 11, the relative error of the two methods reached a maximum of 3.31% when the concentration was 8.0%, and the minimum error of the two methods was 0.06% when the concentration was 7.0%. The relative error of the different concentration test groups was within 5%, and the test results were in line with expectations, which further proves the applicability of the RBF-SVM grading system in the quality detection of Yuba skin.  
Figure 10: Comparison of sensory method and RBF-SVM grading system scores. (**a**) Sensory method scores; (**b**) RBF-SVM grading system scores.
## 2 Theoretical Study  
The automatic retransmission request policy consists of a total of three parts: signal broadcast, forwarding and retransmission. First the signal is broadcasted by the source node to the surrounding nodes, after that the relay node forwards the signal to the destination node. The destination node gives feedback to retransmit the signal because of decoding failure. Finally, the listener node retransmits the signal. Relay selection and retransmission competition are two key techniques for the design.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 5 Conclusions  
In this paper, in order to solve the problem of large delay estimation error caused by NLOS in 5G vehicle positioning for modern urban canyon environments, a time-delay estimation algorithm based on MUSIC for NLOS propagation is proposed, namely NC-MUSIC. The algorithm improves the MUSIC algorithm under the non-line-of-sight transmission spectral function peak submergence problem. First of all, the received signal is calculated, before NLOS transmission is identified, and the NLOS paths reconstructed to eliminate the NLOS paths of received signal through the unsupervised multipath estimation method to calculate the noise subspace of the received signal. Through the calculation of the noise subspace spectral function, we improve the calculation method of spectral function in MUSIC algorithm is used to emphasize the non-line-of-sight under direct transmission signal peak. Through simulation experiments, it can be seen that the NC-MUSIC algorithm proposed in this paper has obvious advantages in terms of NLOS propagation for a modern urban canyon environment. It provides a high-precision time delay estimation algorithm for OTDOA, TOA+AOA, and other positioning methods in the 5G vehicle positioning method, which can effectively improve the positioning accuracy of 5G vehicle positioning in the modern urban environment.  
Conceptualization, X.Z.; Methodology, X.Z.; software, X.Z. and H.W.; validation, X.Z., H.W., and X.F.; formal analysis, X.Z., H.W., and X.F.; investigation, X.Z., H.W., and L.Y.; resources, Z.D.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, Z.D., X.Z., H.W., and X.F.; visualization, X.Z.; funding acquisition, Z.D. and W.L. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the National Key Research & Development Program under Grant No. 2016 YFB0502003, No. 2016 YFB0502001 and the State Key Laboratory of Information Photonics and Optical Communications (BUPT) under Grant IPOC2019 ZT09.  
Figure 11: The impact of estimation error of delay and amplitude of NLOS signal.  
**Acknowledgments:** The authors would like to express thanks to [PERSON] and [PERSON] for the help in the paper's revisions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Thermodynamic Stabilization and Binding Energy Distributions
The distributions of p53-binding energy fitted to the Gaussian-type function and box plots for the (**A**) BAX response element sequences and (**B**) p53-binding sequences extracted from trifluoro-thymidine (FTD)-resistant cells. The blue and orange lines indicate the distribution for a normal and FTD-incorporated DNA, respectively. The p-values between the two distributions are (**A**) 3.04 \(\times\) 10\({}^{-26}\) and (**B**) -0.00, respectively. Figure 6: Comparison of the positions of proteins and each DNA. Representative superposition of binding conformations of the (**A**) BAX promoter sequences and (**B**) sequences extracted from FTD-resistant cells to p53 protein. The red circle indicates the position of trifluoro-thymidine (FTD). Red-dashed and blue-dashed circles indicate the average position of the FTD-incorporated and normal DNA loop, respectively. the normal DNA, whereas for the sequence extracted from the resistant strain, the FTD-incorporated sequence tended to be farther away (Figure 6). This method can be used to calculate the binding energy for any mutation containing p53. In addition to p53, this method also facilitates the analysis of the structural and energetic properties of other DNA-binding molecules, such as NANOG. Furthermore, although the energy distributions were estimated using the MM method, future studies should analyze the intermolecular interactions and estimate the binding energies using quantum mechanical methods as needed. Therefore, this strategy has several applications and great potential for expansion by modifying the computer specifications.
## 2 Materials and Methods
### Synthesis of CuAAC-Grafted Arborescent Copolymers  
#### 2.4.1 Synthesis of Alkyne-Functionalized Arborescent PBG Cores  
The synthesis of the arborescent PBG substrates, either randomly or chain end-functionalized with carboxylic acid groups, was described earlier [PERSON] et al. (2000). To obtain alkyne-functionalized arborescent PBG substrates, propargylamine was coupled with the carboxylic acid moieties using \(N\),\(N^{\prime}\)-diisopropylcarbodiimide (DIC) and 1-hydroxybenzotriazole (HObt). For example, chain end carboxylic acid-functionalized G1 PBG (0.202 g, 6.64 \(\times\) 10\({}^{-5}\) mol CO\({}_{2}\)H, 1 eq.) was placed in a 10-mL round bottomed flask with 4 mL of dry DMF. DIC (52 \(\mu\)L, 3.32 \(\times\) 10\({}^{-4}\) mol, 5 eq.) and HObt (45 mg, 3.32 \(\times\) 10\({}^{-4}\) mol, 5 eq.) were then added to the reaction, followed by propargylamine (8 \(\mu\)L, 1.33 \(\times\) 10\({}^{-4}\) mol, 2 eq., 98%, Sigma-Aldrich, Etobicoke, ON, Canada). The reaction was stirred overnight under N\({}_{2}\) at RT. The crude product was purified by preparative SEC in DMF, to ensure the complete removal of excess propargylamine. The purified product was concentrated to 4-6 mL, precipitated in methanol, centrifuged, and dried overnight under vacuum. Yield: 0.153 g (76%). SEC (DMF): \(M_{\mathrm{n}}\) = 280,000 g/mol, \(M_{\mathrm{w}}\)/\(M_{\mathrm{n}}\) = 1.05 (MALLS). \({}^{1}\)H NMR (300 MHz, \(d_{6}\)-DMSO) \(\delta\): 8.2-7.8 (b, 1H), 7.28-7.20 (s, 5H), 5.03-4.89 (s, 2H), 4.35-3.80 (b, 1H), 3.80-3.70 (b, 2H), 3.10-2.90 (b, 1H), 2.33-1.70 (b, 4H), 1.35-1.10 (b, 1H), 0.80-0.70 (b, 3H).
# Litholytic Activities of Natural Bioactive Compounds and Their Mechanism Insights
## 3 Litholytic Activity of Flavonoids and Stilbenes
### In Vivo Investigations
#### 3.2.3 Curcumin  
Furthermore, several authors evaluated, in vivo, the effect of curcumin on CaOx uroilithiasis in rats [2; 13; 14; 15]. Curcumin at 100 mg/kg/day for 28 days improved renal function, reduced elevating oxidative stress, increased antioxidant enzyme activity, and decreased urinary oxalate levels as well as renal deposition of CaOx [13]. After one year, the same compound at 60 mg/kg for 28 days restored calcium and oxalate levels in urine and kidney tissue to near normal levels (0.20 mg/day vs. 0.17 mg/day and 7.37 mg/day vs. 4.10 mg/day, respectively in urine, and 0.16 mg/g vs. 0.15 mg/g and 7.76 mg/g vs. 5.52 mg/g, respectively in kidneys). The lipid peroxidation of kidney tissue was prevented (21.59%). Using [PERSON]'s method, the number of CaOx deposits was less in kidneys treated with curcumin compared to those in uroilithiasis rats (9.00 vs. 34.17, respectively) [15]. These results corroborate the findings of [14; 15], who found that after 28 days of treatment with curcumin at the doses of 60 and 200 mg/kg/day, respectively, the calcium and oxalate levels and the number of CaOx crystals in the kidney tissue were significantly decreased in calculogenic rats in curative treatment [15] and in both prophylactic and curative treatment [14]. [PERSON] et al. [25] showed that curcumin (at 50 and 100 mg/kg for 14 days) reduced significantly CaOx crystal deposition and attenuated the renal tissue damage with no significant difference between the curcumin 50 mg/kg and curcumin 100 mg/kg groups. Simultaneously, a significant increase in the levels of SOD, CAT, glutathione peroxidase (GPx), CR, and GSH, and a decrease in MDA, were observed in nephrolithiasis mouse kidneys treated with this molecule. Further, the high expression of OPN, CD44, and MCP-1, and IL-6, \(\alpha\)-SMA, collagen I, and collagen fibril deposition decreased with no dose-dependent manner [25]. The anti-nephrolithiasis effect of curcumin was mediated by activating the nuclear factor erythroid-2-related factor (Nrf2), HO-1, NQO1, and UGT expression, which led to the minimization of ROS formation and the attenuation of inflammation and renal fibrosis. [PERSON] and co-workers reported that curcumin acts through two mechanism pathways, via inhibiting oxalate synthesis and increasing the bioavailability of NO to sequester calcium through the cGMP pathway [15]. The improvement of neuroprotective activity observed by curcumin in these works on various _in vivo_ models could be attributed to a combination of several mechanisms of action involved in antioxidant, antiapoptotic, inhibition autophagy, anti-inflammatory, and anti-fibrotic activities [13; 15; 25].
## 3 Analysis of Damping Layer Parameters
### The Effect of the Damping Layer's Thickness (\(h_{d}\) Remains Unchanged)  
Consider that the position of the damping layer is confined between \(x_{d1}=0.14\,m\) and \(x_{d4}=0.26\,m\), and the thickness is taken as \(h_{d}=0.0002\,m\), \(0.0005\,m\), \(0.001\,m\), \(0.005\,m\), respectively. The comparative outcomes are depicted in Figure 5. An examination of Figure 5 reveals that for low-order modes, the modal loss factor is very sensitive to the thickness of the damping layer, with the maximum increase in the modal loss factor being 16 times when the thickness is increased by 25 times. As the order of the vibration mode increases, the increase in the modal loss factor caused by the increase in damping layer thickness will exhibit a more pronounced fluctuation phenomenon. This indicates that increasing the thickness of the damping layer does not always enhance system performance. When the damping layer becomes too thick to the point where it significantly affects the mass and stiffness of the truncated section, it may actually have an adverse effect by resonating with a specific order of frequency.  
Figure 4: The impact of different damping layer positions on the system’s modal loss factor when the damping layer thickness is fixed at \(h_{d}=0.0002\,m\).
# Quantifying Emotions in Architectural Environments Using Biometrics
## 4 Discussion  
### Limitations of Current Research  
The review from the included papers showed some limitations in terms of population, settings, interventions, comparators, and outcomes measured using biometric tools. The limitations need to be reflected in the enhancement of the testing conditions and this paper discussed suggestions.  
The population was limited to healthy subjects to reduce gaps in bio reaction because the results may vary depending on the physiological characteristics of an individual, such as physical condition and health status. However, the limitation is that it does not reflect age or cultural differences, and draws results limited to specific subjects. Some additional limitations included the lack of representation of the population that, as a result, failed to reflect the age and cultural differences in the results. The subjects ranged in age from 20 to 30 years. It would be necessary to extend the research to a diverse group of age ranges by considering schools for children and hospitals for seniors. Experiments using neuroscience techniques find it difficult to recruit subjects, and small sample sizes are another limitation.  
As for the experimental setting, we suggest the creation of a virtual environment which is more immersive than traditional 3D rendering. Using a CAVE or VR headset, measuring biometric data while immersed in the space is essential. Future research needs to be extended to a fully immersive virtual environment simulation with intentional interactions and tasks using emerging technologies. The VR environment that provides an audio-visual connection could compensate for the limitation of visual connection to the environment.  
A lack of variety in the design intervention of independent variables, usually limited to one factor, such as form, color, lighting, and furniture, also signals the need to design multiple and complex conditions. When designing an architectural space, architects and designers consider many factors. In particular, the atmosphere is not determined by only one environmental factor. Thus, environmental factors (e.g., thermal, humidity, light, acoustics, air quality) and architectural/interior design factors must be considered at multisensory levels [9]. In addition, experiments should be conducted to test various types of space. Finally, it is necessary to consider design elements of architectural environments according to purpose and function, such as hospital, school, commercial, and exhibition spaces.  
Regarding the biometric tools used, most studies are limited to one biometric mean, and it might damage the accuracy of generalizing the results. In addition, the analysis methods in most studies are limited to statistical analysis. Additionally, the study's results were limited to descriptive analyses of related research. However, the novelty of these techniques means that no standard protocol has been developed to guide their use in research.
## 4 Experimental Setup
### Experimental Details  
The experiments were conducted in a high-performance computing environment utilizing NVIDIA A100 GPUs (Santa Clara, CA, USA), providing efficient parallel processing capabilities for deep learning on large-scale datasets. The implementation leveraged the PyTorch (23.11) framework to optimize GPU acceleration and streamline model training. Key hyperparameters included a batch size of 64, which balanced memory usage with computational efficiency, and an initial learning rate of 0.001, decayed by a factor of 0.1 every 10 epochs. The Stochastic Gradient Descent (SGD) optimizer, configured with a momentum of 0.9, was employed to improve convergence and mitigate oscillations. Data preprocessing involved image normalization to match the mean and standard deviation of the SAR imaging sensors, coupled with data augmentation techniques to enhance model generalization. Augmentations such as random rotations, horizontal and vertical flips, and Gaussian noise injection were applied to simulate various real-world conditions encountered in SAR imagery. This preprocessing pipeline was designed to bolster the models' robustness against environmental variability and typical imaging artifacts. Training and validation datasets were split in an 80-20% ratio, with cross-validation implemented to evaluate model stability across different data subsets. Each model underwent training for a maximum of 50 epochs, with early stopping applied after 5 epochs of non-improvement to prevent overfitting. ResNet-50 and Vision Transformers (ViTs) were utilized as baseline architectures due to their strengths in capturing local and global features within SAR data. Model performance was assessed using mean Average Precision (mAP) for object detection tasks and accuracy for classification tasks. Additional metrics, including precision and recall, were calculated to provide a comprehensive evaluation of the models' ability to detect small or occluded objects within cluttered SAR environments. Throughout training, key performance indicators such as loss, accuracy, and mAP scores were logged systematically at each epoch, enabling detailed tracking of model convergence and improvements. These experimental setups and evaluations were integral to analyzing the performance and adaptability of both convolutional and Transformer-based models for SAR imaging applications (Algorithm 2).
# Filtration Scheduling: Quality Changes in Freshly Produced Virgin Olive Oil
## 1 Introduction
Although it is well-known that these operations stabilise the olive oil quality, many producers and consumers prefer unfiltered olive oil, because it is considered \"less processed\", \"greener\", \"healthier\", and \"whole\" [16]. For these reasons, several unfiltered olive oils can be found in commerce. The veiled appearance of unfiltered olive oil is due to the presence of suspended material. The latter is a combination of microdroplets of vegetation water and insoluble fruit solids, which are responsible for a wide spectrum of \"turbidities\" [20; 21; 22]. Veiled olive oils are rich in microflora, notably yeasts of candida species [11; 16; 22; 23; 24; 25]. The presence of these solid particles, which are rich in sugars, proteins, and water droplets, is associated with high water activity (greater than 0.6) [24]. This, in turn, supports microorganism and enzyme activity and is responsible for the development of sensory defects such as the fusty attribute [25]. Filtration, when applied, is typically used to obtain a limpid olive oil, as it is an efficient way to completely remove water in emulsion and solids in suspension [11; 13; 26; 27; 28]. There are currently two filtration scheduling approaches. Some extra virgin olive oil producers are used to filter olive oil in-line with the milling process, while others wait until the end of the harvesting season. The former approach aims to minimise the risk of olive oil spoilage but increases the mill's workload. Conversely, the latter approach does not increase the workload but does expose the olive oil to the risk of spoilage. To the best of our knowledge, there is a lack of data regarding the degradation rate of olive oil and, consequently, the optimal moment for filtration. Thus, the aim of this study is to assess the effects of the delayed depth filtration of veiled olive oil on volatile compound contents and the presence of sensory defects that downgrade oil from the \"extra virgin\" to \"virgin\" quality category. For this investigation, during the first month of storage in protective conditions, we have examined the kinetics of volatile compound contents, and sensory analyses, in immediately filtered olive oil samples and in the respective veiled olive oil samples, which would be hypothetically filtered after several days, as often happens during olive oil production.
## 7 Results and Analysis  
Figure 8a,b and Figure 9a,b illustrate the performance of FANET in terms of throughput and end-to-end delay under RPGM and RWP mobility models, respectively. In the graphs, the x-axis indicates the simulation time, in seconds, and the y-axis specifies the throughput and delay, in bits per second. The proposed scheme was tested on an area of 1 \(\times\) 1 km with 42 UAVs. Seven zones were considered with each comprised of 6 UAVs. Five among the six UAVs were equipped with 802.15.1, while one UAV carried both 802.11 and 802.15.1 radios. Simulation were carried out for two different scenarios in order to emulate a group of UAVs under both RPGM and RWP. Table 2 depicts the remaining parameters along with respective values. From our simulation results, it is evident that the desired QoS requirements are met by the proposed scheme. That is, it provides significant performance improvements compared to IEEE 802.15.1 in terms of throughput and end-to-end delay (Figure 8a,b). At the beginning stage of the connection, there is no great difference between Bluetooth and the proposed scheme. The data transmission speed of Wi-Fi is relatively low due to the long connection establishment time of Wi-Fi technology. As a result, the proposed hybrid overcomes the shortcomings of a long initial connection setup time of Wi-Fi as well as a low transmission speed of Bluetooth. Moreover, a slight decline was observed in network performance, particularly in reference to throughput and end-to-end delay under RWP mobility model (Figure 9a,b). It can be observed from Figure 8a,b that RPGM provides higher throughput and minimum end-to-end delay for both IEEE 802.15.1 and the proposed hybrid scheme compared to \(\times\) RWP mobility model (Figure 9a,b). This is because the movement of UAVs under the RPGM model is governed by defining a specific trajectory around a reference point, i.e., the group leader. Thus, the network is in a fully connected state most of the time.  
Figure 8: **(a)** Throughput vs. simulation time under RPGM. (**b**) End-to-end delay vs. simulation time under RPGM.  
Figure 9: **(a)** Throughput vs. simulation time under RWP. (**b**) End-to-end delay vs. simulation time under RWP.