esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The model delivers a fast inference time of just under 23 ms per 640x368 image (equivalent to 44 FPS) on an Intel Core i5-11500 KF. This demonstrates that, while performance may not be on par with the best-performing models, the optimized speed is a significant accomplishment considering the approach taken to address hardware limitations. On the other hand, while [PERSON] and [PERSON] [54] achieved a slightly superior IoU, their model exhibited lower precision, as they predicted foreground regions more liberally, leading to larger segmentation masks. This approach, while improving overall metrics, is less suitable forthe considered application, where maintaining instance separation requires conservative predictions. Consequently, our model prioritizes precision over recall, striking a balance that maintains acceptable IoU while ensuring effective instance separation. Nevertheless, this method is unable to accurately address instances where occlusions divide an object into multiple fragments, and will struggle if objects to detect are of small size relative to the image. This means that the quality of the instance information obtained through this method is application specific and can be temporally inconsistent. Dedicated instance segmentation networks should be used if instance information is crucial for the overall correctness of the algorithm. However, these issues are not present in the current application, making this approach a preferable solution to using more complex and computationally costly instance segmentation networks. While successful in achieving the main goal, it is important to note that instance segmentation in this manner may not be suitable for applications with different constraints or more intricate object boundaries. The approach strikes a balance between semantic and instance segmentation tasks, addressing the challenge of segmenting multiple instances of hands within images effectively. Future research could explore further refinements to enhance boundary delineation and scalability across diverse datasets and object types, thereby extending the applicability of the proposed methodology in broader computer vision tasks. The obtained model has margin for improvements in segmentation performance. Further fine tuning could be performed by testing more modern optimizers like AdamW [62]. Another strategy to be tested is to add a third category (border) other than background and foreground, which was initially rejected for being a discrete version of the presented approach, where the arbitrary definition of the boundary may have an observable effect in the resulting model. Additionally, evaluating the model on additional hand segmentation datasets, such as the Georgia Tech Egocentric Activities (GTEA) dataset [63], would provide further insight into its generalization capabilities across diverse scenarios and environments.
## 2 State of the Art
### Algorithm Performance Evaluation in Other Fields
#### 2.5.3 Speech Recognition  
In speech recognition, the use of corpuses when comparing results is extensive and diverse. The number of corpuses results from one language's heterogeneity compared to another. In languages it is complex to recognize speech, because one language's accentsdiffer from other dialects and phonetics. Nevertheless, despite the variety of results, in this research field, a corpus is a set of selected files seeking representativeness, limiting the number of elements to those necessary, widely available and valuable for developing and evaluating speech recognition techniques.  
The techniques of compression, speech recognition or routing will be useless if applied to data that are not relevant or representative. A corpus is helpful within the intended scope of usability.  
In this section, our research showed that the creation of routing algorithms voids a fair comparison. Furthermore, this section showed that using self-selected files to perform compression algorithm comparisons seems similar to the well-known traces used to compare routing algorithms. The insights obtained from the review of algorithm evaluation and comparison are that using a corpus improves the performance of algorithms throughout standardization.
## 3 Results and Discussion
### Biosynthetic Genes  
The investigation of methylketone synthase proteins showed that putative homologs are encoded in the genomes of the bacterial taxa populating apiaries, such as Acetobacteraceae, _Bacillus, Bartonella, Bombella, Gilliamella_ and _Parasaccharibacter_ and that no significant similarities were found by restricting the search to _A. kunkeei_, _Bifidobacterium_, _Fructobacillus, Leuconostoc_ and _Snodgrassella_. The presence of 2-heptanone in the culture filtrates of _A. kunkeei_ and of _B. asteroides_ in traces suggests that these species may use a different biosynthetic pathway to synthetize this compound yet to be described. After length filtering, 68 sequences were submitted to SSN analysis and resulted distributed in nine SSN clusters and one singleton (Figure 3 and Table 2). The only protein that did not cluster with the others was that of the wild tomato _L. hirsutum_, suggesting that putative methylketone synthase homologs evolved within the bacterial phylum, and a coevolutionary relation between honey bees and its associated microbiota may be hypothesized. Figure 3: Sequence Similarity Network (SSN) showing the relationships among the protein sequences of putative homologs of methylketone synthase in bacterial species inhabiting apiaries. The most related proteins are grouped together in clusters. The singleton was constituted by the sequence of _Lycopersion hirsutum_ included in the dataset as the reference of a methylketone synthase described in plants.
## 2 State-of-the-Art Technologies
Overall, the existing systems are capable of operating with stable distributed systems and large loads for long durations (in hours) [4; 28; 29; 30]. However, these studies have not fully optimized power distribution in the multi-vector energy (mechanical, electrical, and chemical forms). Furthermore, it is more challenging to operate a micro distributed power system for village loads where the load can change from hundreds of watts to over tens of kilowatts rapidly. This paper is focused on a wind-hydrogen-FC powered multi-vector energy system for household applications. The novelty of this study lies in a new operational method which was developed to maximize the local usage of wind energy. The power flow among different components were analyzed and optimized for the whole system, based on a genetic algorithm (GA) optimization method. System state variables, operational variables, and the objective functions were defined under the state space model (SSM). A GA was developed to solve the optimization problem so as to control power flow between different components in the system. By doing so, the majority of wind power is used to supply the load and generate hydrogen/oxygen while there is only limited wind energy over a certain period which is fed into the grid. The rest of the paper is arranged as follows: Section 2 presents the system configuration, the energy flow within the system, and the analytical models with a set of equivalent functions. Section 3 provides the methodology for operational strategies in this investigation. Section 4 discusses and analyzes the test results using real-world data. Section 5 summarizes the key findings and suggests some future work.
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 4 Hybrid Sensor Fusion Algorithm Overview
### Object Classification and Road Segmentation using Deep Learning
#### 4.1.1 Camera-LiDAR Raw Data Fusion  
Before diving into the FCN architecture; we need to prepare the data that feeds into it. The camera and LiDAR sensors provide the input data for the FCN Figure 7. The camera provides 2-dimensional (2D) color images of three RGB channels (Height \(\times\) Width \(\times\) 3). The LiDAR provides a high-resolution depth map in addition to the point cloud data. We combine the unprocessed raw data of LiDAR and camera (early fusion). The resulting RGBD image will have four channels (H \(\times\) W \(\times\) 4). The RGBD image contains the 2D appearance features of the camera image with 3D depth features of LiDAR to give us a rich illumination-invariant spatial image. This image is fed to the FCN to extract road features for semantic segmentation and a CNN for object detection and localization depending on the application.
# Extraction of Antioxidants from Grape and Apple Pomace: Solvent Selection and Process Kinetics
## 3 Results and Discussion
### Kinetic Modeling of Extraction Process
To extract phenolic compounds from AP or GP, several methods have been used (leaching with reflux using a Soxhlet apparatus, direct contact leaching with agitation or by arranging the material to be extracted in a fixed bed, macerating or homogenizing the solid in the solvent, electric pulses, pressure pulses, ultrasonic cavitation, microwaves, and pressurized liquid extraction), even combining various technologies and solvents (MeOH, EtOH, acetone, water, ionic liquids, DEPs, and supercritical fluids) [6,21]. For apple residues, TPC values vary between 3 and 200 mg GAE/gds, with most values between 3 and 7 mg GAE/gds [6], while these values have been reported to be between 6.2 and 196 mg GAE/gds for GP, with most values in the 30-80 mg GAE/gds range [3]. In this work, these values are around 13-26 mg GAE/gds for AP, and near 120 mg GAE/gds for GP. Most classic extraction procedures seem to take between 60 min and 24 h to achieve high TPC values in the case of AP, while operation time reduces to 5-20 min for intensified processing (ultrasounds, microwaves, pressurized liquid extraction) [6]. The same can be said for GP, with optimal time values around 15 min for intensified methods and in the range 60-120 min for classic solid-liquid extraction [3,22]. These values are confirmed here, observing, in addition, that a very fast extraction, mostly boosted by ethanol-water mixtures at medium-high temperatures, results in a high percentage of total polyphenols being extracted in the first 2-3 min. In recent years, some kinetic modeling studies showed that second-order kinetic models can better explain extraction from AP [23] and GP [24], showing in this latter case that biphasic models explain better the lixiviation kinetics. As indicated in this work, activation energy values for relevant kinetic constants show what type of phenomenon is controlling the extraction process, either mass-transfer (low E\({}_{\text{a}}\)) or chemical phenomena (desorption followed by solvation on the solid pore inner surface, also known as solubilization or thermal deactivation) (high E\({}_{\text{a}}\)). While second-order apparent behavior in mass-transfer driven processes can be explained by a sum of first-order phenomena [24], real second-order behavior can be expected when slow chemical phenomena such as site-specific desorption take place on the pore surface [14].
# Observational Analysis of Variation Characteristics of GPS-Based TEC Fluctuation over China
## 3 Results and Discussion  
In this section, we analyze and discuss the characteristics of TEC fluctuations over China during a solar cycle--the 11-year pattern from 2002 to 2012. First, 8 October 2003 (when there is an obvious ionospheric scintillation phenomenon) was selected as an example for demonstrating in detail the application of ROTI indices, and the spatial distribution characteristics of the ionospheric pierce point (IPP) were further analyzed according to the ROTI value of this day. Then, the temporal analysis results of ROTI at the QION and WUHN sites were taken as typical examples to illustrate the temporal variation characteristics of different latitudinal regions over China. Finally, to further investigate the characteristics of TEC fluctuations related to irregular ionospheric activities over China, the daily occurrence ratios of the TEC fluctuations of the 21 selected sites were further analyzed day by day from 2002 to 2012.
# Surfactant-Modified Construction Waste Bricks for the Removal of Diclofenac from Aqueous Solutions
## 3 Results and Discussion
### Adsorption Kinetics  
Figure 7 illustrates the adsorption kinetics of the adsorption of DCF onto the PRBPs and the CCBPs. The initial adsorption rate of DCF onto the PRBPs was rapid, reaching equilibrium at around 6 h, while the CCBPs reached equilibrium faster, at approximately 2 h. The rapid adsorption process of the CCBPs is attributed to the plentiful and efficient adsorption sites provided by the structure of the waste bricks. Furthermore, the adsorption capacity and rate of the CCBPs were higher than those of the PRBPs, implying that the introduction of CTAB enhanced the process of the adsorption of DCF onto the waste bricks.  
To investigate the adsorption kinetics of the adsorption of DCF onto the PRBPs and CCBPs further, the pseudo-first-order and pseudo-second-order equations were used to fit the kinetics data, and the models are expressed as Equations (1) and (2) ([PERSON] et al. 2016):  
\[Q_{t}=Q_{e}\Big{(}1-e^{-k_{1}t}\Big{)} \tag{1}\]  
\[Q_{t}=k_{2}tQ_{e}^{2}/(1+k_{2}Q_{e}) \tag{2}\]  
where \(Q_{e}\) (mg/g) represents the adsorbed amounts of DCF at the equilibrium time and \(Q_{t}\) (mg/g) represents it at any time t (min), and \(k_{1}\) (mg/h) and \(k_{2}\) (mg/h) are the rate constants for the pseudo-first-order and pseudo-second-order equations, respectively.  
The results of the calculated parameters and the correlations of the adsorption kinetics are presented in Table 3. It is evident that both models fit the adsorption process of DCF onto waste bricks well. However, the pseudo-second-order model is more suitable than the pseudo-first-order model in describing the adsorption of DCF onto the CCBPs, indicating that the adsorption process may be primarily controlled by chemical adsorption.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbents**} & \multicolumn{3}{c}{**Pseudo-First-Order Model**} & \multicolumn{3}{c}{**Pseudo-Second-Order Model**} \\ \cline{2-7}  & **K\({}_{1}\) (mg/h)** & \(q_{e}\) (mg/g) & **\(R^{2}\)** & **K\({}_{2}\) (mg/h)** & \(q_{e}\) (mg/g) & **\(R^{2}\)** \\ \hline PRBP & 0.46 & 1.12 & 0.99 & 0.30 & 1.40 & 0.99 \\ CCBP & 1.56 & 5.51 & 0.93 & 0.31 & 6.12 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters and correlation coefficients of adsorption kinetic models.  
Figure 7: Adsorption kinetics of DCF onto PRBPs and CCBPs (experimental conditions: [DCF]\({}_{0}\) = 20 mg/L, dosage = 2 g/L, T = 298 K, pH= 4.0 \(\pm\) 0.1).
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 3 Experimental Platform for Underwater Polarization Imaging  
### Polarization Imaging of Split Focus Plane  
The subfocal plane imaging system developed in this paper consists of two subsystems. They are the polarization detection subsystem and the image acquisition and processing subsystem. Figure 2 shows the schematic diagram of the imaging system.  
According to the polarization imaging theory, the Stokes vector can only be obtained through four polarization images. At present, polarization imaging methods can be divided into time division, amplitude division, aperture division and focal plane division. The experimental platform built in this paper uses the color polarization camera TRIO50S-QC, produced by the Canadian LUCID company, and the 75 mm fixed focus industrial lens VM7528 MP5, produced by Beijing Zhonglian Kechuang Trade Co., Ltd (Beijing, China). The real object of the split-focus plane polarization camera is shown in Figure 3.  
Figure 2: Schematic diagram of focusing plane imaging system.  
Based on the camera SDK, the host computer software is developed using the QT framework based on C++ language and OpenCV library. The software implementation process is shown in Figure 4. First, the camera connection and parameter setting are completed, and the polarization image of the target is collected; the image stream is turned on to start the image stream transmission engine of the camera. The subsequently acquired polarization images will be sent to the output buffer queue in turn. Then, they are read by the user and sent to the input buffer queue.  
Figure 4: Flow chart of polarization imaging software system.  
Figure 3: Physical figure of focusing plane polarized camera.  
The software of the split-focus plane polarization imaging system is shown in Figure 5. It is mainly composed of human-computer interaction module and real-time display module. The human-computer interaction part includes the functions of connecting equipment, image acquisition, camera parameters and image processing; the real-time display module can display \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\) polarization images and I, DOP, AOP result images.
## 2 Method
### Evaluation Metric  
The approximate solution provided by the PINN needs to be evaluated and compared with the analytical solution/finite modelling result using some metric. The error between the ground truth and the approximation can be challenging to quantify. We use relative \(L_{2}\) norm error (see Equation (9)).  
It is used in linear algebra and defines the relative \(L_{2}\) norm error. We take a discrete sample of data points from the solution space and the approximation space and store them as vectors \(x\) and \(b\), respectively. We then find the Euclidean distance of the difference between the solution and the approximation vectors relative to the Euclidean distance of the solution.  
\[e_{L}=\frac{\sqrt{\sum_{i=1}^{N_{k}}(x_{i}-b_{i})^{2}}}{\sqrt{\sum_{i=1}^{N_{k} }x_{i}^{2}}}=\frac{||x-b||_{2}}{||x||_{2}} \tag{9}\]
# Reducing Videoconferencing Fatigue through Facial Emotion Recognition
###### Abstract  
In the last 14 months, COVID-19 made face-to-face meetings impossible and this has led to rapid growth in videoconferencing. As highly social features, humans strive for direct interpersonal interaction, which means that in most of these video meetings the webcam is switched on and people are \"looking each other in the eyes\". However, it is far from clear what the psychological consequences of this shift to virtual face-to-face communication are and if there are methods to alleviate \"videoconferencing fatigue\". We have studied the influence of emotions of meeting participants on the perceived outcome of video meetings. Our experimental setting consisted of 35 participants collaborating in eight teams over Zoom in a one semester course on Collaborative Innovation Networks in bi-weekly video meetings, where each team presented its progress. Emotion was tracked through Zoom face video snapshots using facial emotion recognition that recognized six emotions (happy, sad, fear, anger, neutral, and surprise). Our dependent variable was a score given after each presentation by all participants except the presenter. We found that the happier the speaker is, the happier and less neutral the audience is. More importantly, we found that the presentations that triggered wide swings in \"fear\" and \"joy\" among the participants are correlated with a higher rating. Our findings provide valuable input for online video presenters on how to conduct better and less triing meetings; this will lead to a decrease in \"videoconferencing fatigue\".  
facial emotion recognition; social network analysis; video meetings +
Footnote †: journal:  
1
Footnote †: journal:  
1 Cologne Institute for Information Systems, University of Cologne, 50923 Cologne, Germany; [EMAIL_ADDRESS]  
2 School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China; [EMAIL_ADDRESS]  
3 MIT Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, MA 02142, USA 3
###### Abstract
In contrast, geostatistical approaches often require the explicit modeling of spatial relationships and may struggle with high-dimensional data, leading to a reduced accuracy and interpretability [14]. Environmental covariates are crucial in digital soil mapping as they provide relevant information about the soil-landscape relationships, enabling the creation of more accurate predictive models [15]. These covariates can be topographic, climatic, biophysical, or anthropogenic factors that indirectly influence soil properties through their effects on soil-forming processes [16]. Remote sensing has a significant role in digital soil mapping by providing consistent, large-scale, and high-resolution data on various environmental covariates, facilitating the accurate prediction of soil properties in data-scarce regions and enabling the identification of spatial patterns and trends that may be difficult to detect using conventional ground-based measurements alone [17]. The integration of remote sensing data with machine learning algorithms has the potential to further enhance the predictive accuracy and interpretability of digital soil mapping models, ultimately contributing to better-informed land use and climate action strategies. While the evaluation of machine learning algorithms for digital SOC mapping has been a focus of several previous studies, there is a predominant conclusion that the optimal machine learning algorithm and its hyperparameters are heavily dependent on the input soil sample properties [10]. To ensure the optimal selection of predictive approaches, it is essential to evaluate several machine learning algorithms in each study [18]. However, a significant research gap exists regarding which specific algorithms should be prioritized for evaluation. Selecting from the vast array of available algorithms can be computationally inefficient, especially when conducting national-scale digital SOC mapping, a process that is often utilized in land management. The aim of this study is to address this research gap by providing a comprehensive assessment of the prediction accuracy of 15 commonly used machine learning algorithms in digital SOC mapping. This assessment is based on studies indexed in the Web of ScienceCore Collection (WoSCC), thereby establishing a foundation for algorithm selection in future research.