esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Experiment Facilities
### The Flow Distribution in Different Sections  
In addition to the volume flow rate and average velocity, the velocity along each path also provided information about the flow distribution. Here, the definition and quantification of flow characteristic parameters expressing the types of perturbations developed were as follows, which were also used by most manufacturers to qualify the flow state [41; 42]:  
* Profile factor: described the distribution of axial flow in the pipe, and it was therefore a quantification as to how parabolic the flow profile was. Profile factor = (\(v_{\text{B}}+v_{\text{C}}\))/(\(v_{\text{A}}\) + \(v_{\text{D}}\)), when it was within (1.17 \(\pm\) 0.05) meaning a fully developed flow profile [42; 43];  
Figure 6: Results with different installation conditions at 0.36 MPa.  
Figure 7: The difference between two devices at different pressures.  
Symmetry: how symmetric the flow velocity was with the respect to the center of the pipe, \(\text{Symmetry}=(v_{\text{A}}+v_{\text{B}})/(v_{\text{C}}+v_{\text{D}})\), when it was 1 meaning a symmetric flow profile, the further away from 1 the Symmetry was, the greater the asymmetry; Cross-flow: described the transversal flow or rotation, \(\text{Cross-flow}=(v_{\text{A}}+v_{\text{C}})/(v_{\text{B}}+v_{\text{D}})\), when it was 1 meaning no cross-flow or rotation.  
The characteristic parameters of flow distribution in both sections for the tests at the SN device at 0.36 MPa are shown in Figure 8. The results showed that when the FC was not installed, the velocity distribution of all paths was disorderly and irregular, and most of the characteristic parameters do not meet the requirements (Figure 8a). There were obvious differences between two sections, indicating that there were significant swirl or cross-flow in the pipe. When the FC was installed, the situation was greatly improved: the flow distribution became symmetric and uniform, and the difference between the two sections was greatly reduced (Figure 8b). On the other hand, the value of the Symmetry and Cross-flow was stable, while the value of Profile factor was decreased with the Reynolds number increasing.  
Figure 8: The characteristic parameters of flow distribution in different sections. (**a**) Without flow conditioner (FC), (**b**) With FC.  
It should be noted that the final velocity was the average of the two sections. The average results with and without FC are shown in Figure 9. It was clear that the values of the Symmetry and Cross-flow were all close to 1 and almost equal under different conditions, and the difference was within \(\pm\)0.01, while the parts of the Profile factor were smaller than the minimum value of 1.12 and the maximum difference could be 0.025. However, the characteristic parameters under different installation conditions were significantly different, which might be the main reason leading to the 1% difference in the final results. Thus, it clearly showed that characteristic parameters could be used as a diagnostic indicator of whether or not the measured flow profile was within an acceptable range.
## 3 Results and Discussion
### Results of Microwave-Assisted Extraction
#### 3.3.1 MAE Extracts of Chicory and Fennel By-Products  
Regarding the TPC, the following effects were significant (\(p\) < 0.05): linear and quadratic effects of'solid-water ratio' and linear effect of 'power'. The linear and quadratic effects of 'time' and quadratic effects of 'power' were not significant. Based on the \(F\) values, the major impacts on TPC were exerted by linear and quadratic'solid-water ratio' terms.  
The TPC of chicory extracted by MAE is described by Equation (8):  
\[\text{TPC}=1.1643\text{A}+0.0155\text{C}-0.0964\text{A}^{2} \tag{8}\]  
where A, solid-water ratio (g/100 mL); C, power (W).  
According to the equation, the TPC of the extracts increased mainly with the increase in the linear'solid-water ratio' and 'power' terms, and decreased with the increase in the quadratic'solid-water ratio' term. To allow the graphic visualization of the effects of the independent variables on the TPC, the application of RSM gave rise to three surfaces and the related equations that plotted two independent variables at a time, keeping fixed the other factor at the central value of the experimental design (Figure 2).  
Figure 2: Three-dimensional response surface plots for TPC (mg gallic acid/g dm) of chicory extracts obtained through MAE: effect of solid–water ratio (A), extraction time (B), power (C), and their interactions.  
Concerning the antioxidant activity, only the linear'solid-water ratio' term was significant (\(p<0.05\)), and the equation that describes the results is the following (9):  
\[\text{AA}=0.0363-0.0017\text{A} \tag{9}\]  
where A, solid-water ratio (g/100 mL).  
As can be inferred from the regression coefficients, the antioxidant activity decreased as the'solid-water ratio' increased. The response surfaces are shown in Figure S4.  
Concerning the fennel, the linear'solid-water ratio' term was the only one to exert a statistically significant effect on fennel TPC. The total phenolic content of fennel extracted by MAE is described by Equation (10), which highlights that its increase was associated with the increase in the'solid-water ratio'. Figure S5 shows the visual representation of the response surfaces.  
\[\text{TPC}=3.1220+0.3945\text{A} \tag{10}\]  
where A, solid-water ratio (g/100 mL).  
The antioxidant activity of the fennel extracts was significantly affected only by the quadratic'solid-water ratio' term and, according to Equation (11), the AA decreased with the increase in the'solid-water ratio'. The response surfaces that visually describe the relationships between dependent and independent variables are shown in Figure S6.  
\[\text{AA}=0.0603-0.0004\text{A}^{2} \tag{11}\]  
where A, solid-water ratio (g/100 mL).
# A 3D Printed Vitrification Device for Storage in Cryopreservation Vials
## 4 Discussion
### Computer Simulations  
Thermal finite element computer simulations can facilitate the design process of devices for cryogenic applications. With computer simulations, promising candidates can be identified within several days (depending on the computing time with average convergence time of ~2 h in the present study) instead of the weeks that would be needed for the fabrication and empirical evaluation of multiple prototypes to identify the optimal geometries. To the best of our knowledge, no computer simulations of cooling rates for sperm vitrification devices have been previously reported. The cooling rates of 0.7-1.5 \(\times\) 105 \({}^{\circ}\)C/min were within the temperature range (>2000 \({}^{\circ}\)C/min) of vitrification occurrence [4]. When compared with previous numerical simulation studies of various oocyte vitrification devices [12], the cooling rates in the present study were higher than the open-pulled straw and pipette tip methods (1.7-8.7 \(\times\) 104 \({}^{\circ}\)C/min) comparable to Cryotop(r) (1.0-3.7 \(\times\) 105 \({}^{\circ}\)C/min) and lower than Cryolop(r) (3.6-18 \(\times\) 105 \({}^{\circ}\)C/min). Cryotop(r) was created to vitrify embryos on a thin plastic film that has subsequently been adapted for research to hold droplets of sperm suspension for vitrification but with a relatively high cost (i.e., ~$20/ unit vs. $0.5/ unit material cost of VDCD). The mechanism of enabling vitrification by the VDCD were similar to those of the Cryolop(r) by forming thin films to increase the cooling rate. The higher apparent cooling rate of devices such as Cryolop(r) could be caused by the smaller sample size (i.e., <1 uL) and minimal supporting materials (i.e., <20-um height). The lower cooling rate of loops with axial supports could be caused by larger thermal masses of the central support. The purpose of the computer simulations herein was to compare the contributions of various geometries on the relative loop cooling rates. An exact determination of the actual temperature profiles in these PLA loops would need to consider several of the polymer physicochemical properties that have not been reported and was thus beyond the scope of this study.
# Autonomous Operation Method of Multi-DOF Robotic Arm Based on Binocular Vision
## 1 Introduction
[PERSON] proposed a method on the basis of ADAMS software for inverse driving trajectory planning for the difficulty of traditional methods for solving the inverse kinematics problem of joint robots, and achieved good experimental results [17]. [PERSON] proposed a comprehensive trajectory planning method for mechanical arm time-lapse and obtained the optimal time-lapse trajectory for the robot [18]. The main problems of the existing methods for autonomous operation of the robotic arm can be summarized as follows:  
1. The traditional method of object extraction based on image segmentation is simple to implement, but it has the disadvantages of low robustness and poor universality. The target extraction method based on machine learning ideas is robust and versatile. However, its implementation is complicated and it is difficult to apply it to actual engineering. 2. The trajectory planning method based on Cartesian space is the common method of motion trajectory planning currently applied to the robotic arm. This method has a large amount of mathematical operations because it is necessary to solve the inverse kinematics equation of the robotic arm to obtain the optimal amount of motion of each joint, so it is difficult to implement in practical engineering. Aiming at the problems of the current autonomous operation methods of the robotic arm, a method based on binocular vision for the autonomous operation of the multi-DOF (Multiple Degree of Freedom) robotic arm is proposed in this study. The specific content is as follows: the spatial location and extraction method of the target object, the kinematic modeling, and the trajectory planning method design of multi-DOF robotic arm are introduced in the Materials and Methods section. The simulation verification of the robotic arm trajectory planning method and the experimental verification of the proposed method are reflected in the Results section. Finally, the experimental results are analyzed and explained in the Discussion section. The experimental results show that the robotic arm autonomous operation method proposed in this paper has good performance and simple implementation. It can be applied to the grasping and recycling of target objects in real life, such as the recycling of waste products in urban construction, the recycling of athletes' training balls, the recycling of hazardous explosives, the recycling of hazardous substances, and so on.
## 7 Challenges of Edge AI in Early Health Prediction and Future Directions
### Data Quality and Accuracy  
Another obstacle AI algorithms face is the quality and accuracy of the data they analyze. Information coming from wearable devices and other sources can be inaccurate, inconsistent, or erroneous. All of this can lead to inaccurate predictions and false warnings [95; 96]. Here are the key aspects to consider:  
* **Labeling and Annotation:** High-quality labeled data are crucial for training accurate and reliable AI models. However, in early health prediction, obtaining ground truth labels can be challenging. This may require expert knowledge, clinical validation, or long-term follow-up to confirm the accuracy of predictions.
* **Data Variability:** Health data collected at the edge can be highly variable due to various factors such as device sensors, user behavior, environmental conditions, and data collection protocols [97]. This variability can impact the performance and generalizability of AI models, making it essential to address data variability challenges.
* **Data Imbalance:** In health prediction tasks, class imbalances are common, where certain health conditions or outcomes may be significantly less frequent than others [98]. This can lead to biased models that perform poorly on minority classes. Techniques such as data augmentation, oversampling, or ensemble methods need to be explored to address data imbalance challenges.
* **Data Quality Control:** Ensuring the quality and reliability of collected health data is critical. Sensor errors, noise, missing values, and data corruption can negatively impact the performance of AI models [99]. Quality control mechanisms, data preprocessing techniques, and outlier detection methods need to be developed to improve data quality.  
Future research directions to address data quality challenges in Edge AI for early health prediction include:  
* **Data Augmentation Techniques:** Exploring data augmentation methods specifically tailored for health data to increase the diversity and size of the training dataset, improving model robustness and generalizability.
* **Collaborative Data Sharing:** Encouraging collaboration and data sharing among healthcare institutions while ensuring privacy and security. Pooling diverse datasets can help overcome data variability and improve the representativeness of AI models.
* **Data Quality Assessment Frameworks:** Developing standardized frameworks to assess the quality and reliability of health data collected at the edge. This can involve metrics, guidelines, and best practices for data collection, labeling, and preprocessing.
## 2 Materials and Methods
### Algorithm for Studying the Observability of Nonlinear Systems
#### 2.3.1 Initialization of the Algorithm  
The inputs of the algorithm are the state space representation consisting of \(f\left(x,\alpha\right)\) and \(h\left(x,\beta\right)\) as well the initial area \(D_{x}\). Therefore, the initial area \(D_{x}\) represents the set of all initial state values, which are of interest to the user.  
The algorithm generates and manages two levels of lists for processing the data structure. The outer list and thus the first level is called \(\mathcal{R}\) structured as a First In--First Out (FIFO) list. Since \(\mathcal{R}\) manages all intervals from \(D_{x}\), the FIFO structure provides a queue for processing the data. Thus, the intervals are bisected uniformly which is of importance for the methodology.  
For efficient processing of the examined intervals, we introduce the object  
\[\mathcal{Z}_{i}=\left\{x_{i}^{I},\ \delta_{i}^{I},\ \mathcal{L}_{i},\ q^{I} \left(x_{i}^{I}\right)\right\}. \tag{13}\]  
This object contains the intervals \(x_{i}^{I}\subset D_{x}\) and \(\delta_{i}^{I}\subset\delta^{I}\), the respective inner list \(\mathcal{L}_{i}\) and the previous results of the Lie derivatives \(q^{I}\left(x_{i}^{I}\right)\), whereby \(i\) is the identification number. The inner list \(\mathcal{L}_{i}\) is one of multiple lists that together represent the second level of the data structure. Since each interval is assigned to a separate object (13), all indistinguishable intervals are assigned to the respective list \(\mathcal{L}\). All these objects are stored in the outer list  
\[\mathcal{R}=\left\{\mathcal{Z}_{r},\,\ldots,\mathcal{Z}_{s}\right\},\,r\leq i \leq s,\,\,\,\,r,s\in\mathbb{N}.\]  
After the data structure is established, the object \(\mathcal{Z}_{1}\) is generated for the multidimensional interval \(D_{x}\). Here the identification number is 1, since it is the first object. Furthermore, the identification numbers are assigned continuously with each newly created object. The corresponding list \(\mathcal{L}_{1}\) is initially empty, since \(\mathcal{L}\) never contains the identification number of the interval itself. Then \(\mathcal{Z}_{1}\) is assigned to the list \(\mathcal{R}\). All subsequent steps of Algorithm 1 are executed as long as the list \(\mathcal{R}\) is not empty. However, the list \(\mathcal{R}\) only runs empty if the nonlinear system is observable on \(D_{x}\). If the system is not or only partially observable, the algorithm would never terminate. In this case, suitable termination criteria can be selected. For example, the smallest interval width below a threshold \(\epsilon\) can be used. At the beginning of each loop, the first object \(\mathcal{Z}_{i}\) is taken from the list \(\mathcal{R}\). Afterwards, the interval \(x_{i}^{1}\) of the object \(\mathcal{Z}_{i}\) is bisected along the longest edge in order to evenly scale down the intervals, successively.
# Trends in Performance Limits of the HOT Infrared Photodetectors
## 6 Colloidal Quantum Dot Infrared Photodetectors  
Research on QD IR photodetectors based on self-assembled epitaxial QDs started in the mid-1990 and were initially very promising. Theoretical estimates carried out by [PERSON] et al. [67] in 2008 indicate that the self-assembled quantum dot infrared photodetectors (QDIFs) are suitable for noncryogenic operation especially in LWIR region. As it happens later, that epitaxial QDs suffer from the size control and low dots density. More recently, an attractive alternative to self-assembled epitaxial QDs has been colloidalquantum dots (CQDs) with better size tunability of optical features and reduction of fabrication cost.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 1 Introduction
### Related Study  
Fake news detection has been an essential research topic, and many techniques have been employed. However, most of the methods are based on text content. The classification is based on text, sentiment, or user profile analysis. When fake news started creating a nuisance through fake images and videos, a constant effort was made to detect counterfeit images on social media platforms. Multiple types of research proposing diverse solutions were discussed. There has been a continuous effort to review those various image detection techniques from time to time, and a study has been conducted to compare and further guide more research toward fake image detection. [PERSON] and [PERSON] [11] studied various passive techniques. Still, those were more specific to forensic techniques, while later, GAN and deep learning picked up. Later, [PERSON] [12] performed a comparative study of different techniques based on image tampering methods. It also discussed various new techniques and the usage of classifiers. In a media-rich fake news detection, [PERSON] and [PERSON] [13] discuss both techniques through text and visual and share information over specific datasets. However, it needed to compare which method is better for text or visual. [PERSON] et al. [14] have targeted only deepfakes. It discussed different deepfake tampering types and their detection methods in detail. It also compared various deepfake detection techniques. This survey paper exhausts studies of multiple conventional to modern neural network-based techniques and provides a comparison. It also discusses the issues within each technique.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 1 Introduction
This paper proposes two new observables for estimating wind speed by analyzing the complex waveform, which describes the variation of energy distribution of reflected signals. In Section 2, the model of GNSS-R waveform is reviewed and the discussion on the energy distribution of complex waveform is presented through simulation. Two new observables related to energy distribution of complex waveform are proposed to retrieve wind speed. In Section 3, this paper summarizes the main scenarios of the coastal experiment in the Yangjiang site and the raw data preprocessing chain from the Beidou GEO satellite. Then, the analysis of the results is presented in Section 4. In addition, the influences of the threshold and coherent integration time on wind speed retrieval are discussed to obtain an optimal retrieval result. Finally, the conclusions and future work are presented.
## 3 Results  
### Thermal Comfort  
#### 3.1.1 Internal Operational Temperature  
Operational temperature measurements were conducted during the interim period from 7 to 17 March 2020. The test apparatus for measuring thermal comfort is shown in Figure 8. The microclimate (thermal comfort) testing device, also depicted in Figure 8, records the following parameters: temperature, humidity, and air velocity. Based on the recorded parameters, the thermal comfort index PMV [-] and the percentage of people dissatisfied with the prevailing thermal conditions were determined.  
Microclimate meters, namely EHA MM101 by EKOHIGIENA (Pleszew, Poland), were used for thermal comfort measurements. These devices meet the requirements of the EN ISO 7726 standard (ergonomics of the thermal environment--instruments for measuring physical quantities). The measured parameters were recorded every 10 min. The metrological properties of the sensors are presented in Table 4.  
The results of the operating temperature measurements are shown in Figure 9.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type of Sensor** & **Measurement Range** & **Scale** & **Accuracy** \\ \hline Temperature & -20 \({}^{\circ}\)C + 50 \({}^{\circ}\)C (wet thermometer 0 \({}^{\circ}\)C + 5 \({}^{\circ}\)C) & 0.01 \({}^{\circ}\)C & \(\pm\)0.4 \({}^{\circ}\)C \\ \hline Humidity & 0-100\% & 0.1 RH (relative humidity) & \(\pm\)2\% RH (relative humidity) \\ \hline Air flow velocity & 0-5 m/s & 0.01 m/s & For 0-1 m/s: \\  & & & \(\pm\)0.05 + 0.05\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%%\%\%\%%\%\%\%%\%\%%\%\%%\%%\%%\%%\%%\%%\%%%\  
Due to the lack of blinds, the measurements showed significant variations in temperature. The minimum temperature recorded was 20.15 \({}^{\circ}\)C, and the maximum temperature was 28.55 \({}^{\circ}\)C. The average internal temperature for the review period was 21.03 \({}^{\circ}\)C. The survey was conducted in the winter-spring month. Large temperature fluctuations were observed.
## 3 Results and Discussion
### Challenges and Future Work  
Thus, based on the discussion in the above subsections, for the method proposed in this study, our method indeed gained an effective improvement compared to machine learning methods. However, the performance of our predictor was inferior to them compared to the latest deep learning methods. Additionally, the generalization performance of the predictors for _G. pickeringi_ species and _G. subterruneus_ species was also inferior to the generalization performance of the predictors for the other four species. We speculate that this may be due to the weakness of our manually extracted sequence features compared to those automatically extracted by deep learning techniques, and it was found that _G. pickeringii_ species and _G. subterruneus_ species have nucleotide sequence preferences by analyzing nucleotide preferences. Compared with other species, the sequence preference is more complicated, resulting in a weaker generalization ability of these two models. Therefore, we believe that the model we constructed still has much room for improvement, especially in terms of feature extraction and algorithmic learning techniques. In our subsequent work, we will also think about how to use artificial features combined with deep learning techniques for modeling and try to model the DNA 4 mC site identification directly across species instead of modeling each species separately to build predictors.