esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of an Organic Rectorite on the Properties of a Waste Engine Oil-Modified Asphalt
###### Abstract  
Using waste engine oil (WEO) to partially replace petroleum-based asphalt binders offers a promising solution to mitigate the environmental impact of waste WEO while reducing the dependence on non-renewable asphalt resources. To enhance the comprehensive properties of a modified asphalt containing 6 wt% WEO, an organic retorite (OREC) with various contents was introduced to the modified asphalt system. The effect of OREC on the structure, storage stability, rheological properties, and self-healing property of WEO-modified asphalt were evaluated using a Fourier transform infrared spectrometer, softening point instrument, and rheometer. The results demonstrated that that no chemical reaction occurs between OREC and WEO-modified asphalt. The addition of OREC can enhance the storage stability and high-temperature stability while marginally decreasing the low-temperature cracking resistance and self-healing properties of WEO-modified asphalt. When the content of OREC increases to 2 wt%, the 48 h softening point difference value of the OREC + WEO composite-modified asphalt drops to 1.8 \({}^{\circ}\)C, and this composite-modified asphalt exhibits superior high-temperature rutting resistance, low-temperature cracking resistance, and self-healing property compared to the corresponding base asphalt. In summary, the addition of OREC can effectively offset the adverse effects of WEO on the properties of base asphalt.  
organic retorite; waste engine oil; modified asphalt; rheological properties; self-healing property +
Footnote †: journal: Applied Sciences
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 4 Experimental Results
### Blink Detection  
The authors evaluated eye blink detection algorithms, CEAR (EAR with Constant Threshold), AELD (Eyelid Distance with Adaptive Threshold) and AEAR (EAR with Adaptive Threshold), using three datasets: RT-BENE, Eyeblink8 and TalkingFace. Regression-based adaptive thresholds are fitted on the training part of RT-BENE dataset. The evaluations were conducted on the test part of the RT-BENE, and all videos were from Eyeblink8 and TalkingFace. The average performance of each algorithm or model can be found in Table 1.  
Based on the F1 score, the best-performing algorithm was AELD, with an average score of 53.14%. CEAR achieved an average score of 30.49%, while AEAR achieved an average score of 51.65%.  
In terms of accuracy, AELD also achieved the highest average score of 97.59%, which was not significantly different from AEAR, which achieved an average score of 97.53%. CEAR achieved the lowest average score of 89.59%. However, CEAR achieved the highest average AUC-ROC score with 81.59% and the highest average Recall up to 73.27%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **F1** & **Precision** & **Recall** & **AUC-ROC** \\ \hline CEAR & 89.59 & 30.49 & 19.34 & 73.27 & 81.59 \\ \hline AELD & 97.59 & 53.14 & 46.43 & 62.53 & 80.44 \\ \hline AEAR & 97.53 & 51.65 & 45.18 & 60.94 & 79.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average Performance Metrics of Eye Blink Detection Approaches.  
Figure 7: CMU Face Orientation Confusion Matrix.  
Overall, the proposed adaptive threshold with face orientation saw a noticeable and consistent improvement in F1 score and accuracy. It is worth noting that single eyelid distance can perform on par with EAR, given the use of adaptive threshold and landmark standardization. All three models perform below average in terms of F1 score on Eyeblink8 dataset.
## 2 Concept of Nonlinear Cylinder-Shaped Slit Membrane Damper  
The conceptual structure of the slit membrane damper is shown in Figure 1. Like in conventional fluidic dampers, the damping force is obtained as the piston moves in a cylinder filled with viscous fluid. The piston is of axisymmetric structure. It consists of an outer ring, inner circular-shaped flexible membrane with slits, and a piston rod rigidly attached at the center of the membrane. When external excitation is applied to the damper, the membrane deforms causing the slits to open, allowing fluid flow through the slits. The slit opening area is dependent on membrane deformation, which in turn is dependent on external excitation load. The variation in the opening area during a dynamical process results in the condition of nonlinearity of the damping characteristic. Therefore, damping effectiveness depends on the dynamical properties of the membrane.  
Figure 1: Conceptual structure of slit membrane damper: 1—rod, 2—cylinder, 3—viscous fluid, 4—piston’s ring, 5—slit membrane.
# Application of Modeling and Simulation in a Self-Reprogrammable Prototype of a Manufacturing System
## 4 Project Architecture
### Virtual Environment
#### 4.2.1 Communication Between Real and Virtual Environments  
To enable real-time communication, sensors installed in the physical system capture data, which is then transmitted to the simulation model. In this study, the variables identified for real-time monitoring were Cycle Time and Number of Parts Transported. These variables are sent to and processed within the Arduino database. Consequently, all parameters required for input into the simulation model are managed through defined gateways and linked to the simulation model.  
In the Plant Simulation V.16 software, the tool responsible for identifying and reading data from the gateways is OPC (Open Platform Communications). OPC establishes communication between controllers and automation systems to facilitate real-time data exchange, allowing the simulation model to utilize the production system's operating parameters. Additionally, OPC enables data transmission from the virtual environment back to the manufacturing system. Figure 5 illustrates the application of the OPC tool in facilitating communication between the simulation model and the real system.  
Data is acquired from the real environment through sensors and transmitted to the Arduino database, which maintains continuous communication with the simulation model's OPC tool via a port called Gate. At each work cycle, the simulation model computes the optimal operating speed of the conveyor and transmits the corresponding value to the OPC. Through the same Gate port, this information is relayed to the Arduino and subsequently transmitted to the conveyor via the ESP32 board, thereby adjusting the conveyor motor speed accordingly.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Results and Analysis on Our Deep Waterline Detection Approach
#### 4.3.2 Investigating the Moving Stride of WLpeephole  
After setting empirically the \((r,s)\) pair in Algorithm 1 as suggested above, we further test the impact of the moving stride of WLpeephole (denoted \(h\)) on the detection accuracy. Specifically, we carry out our algorithm, respectively, on the same optical image with three different strides \(h\).  
Figure 7 presents the visual results on the three cases, which also demonstrate the effectiveness of our waterline detection algorithm intuitively. Meanwhile, Table 4 shows the quantitative results corresponding to Figure 7, among which the \(F1\)-_score_ (i.e., 0.906) is the best when \(h\) is set to 10 pixels. Actually, in Figure 7, the blue line that represents the case of \(h=10\) is closest to the real waterline intuitively.  
\begin{table}
\begin{tabular}{c c c} \hline \(s\) & \(h\) & \(F1\) \\ \hline \(3h\) & 10 & 0.906 \\ \(2h\) & 15 & 0.869 \\ \(h\) & 30 & 0.752 \\ \hline \end{tabular}
\end{table}
Table 4: Quantitative comparisons on \(F1\)-_score_ by setting three different moving strides of WLpeephole, respectively (here, \(r=60\) pixels, \(s=30\) pixels and \(\lambda=10\) pixels).  
Figure 6: Visual comparisons on detection results by performing Algorithm 1 on a single image with two different scales of WLpeephole (here, blue line shows the estimated waterline, and red line acts as the subline for marking manually the ground truth): (**a**) \(r=30\) and \(s=10\), (**b**) \(r=60\) and \(s=30\).  
Due to our simplistic marking strategy to approximate potential waterlines in Algorithm 1, we suggest that \(h\) should be set as small as possible to attain desirable detection effects, e.g., \(h=10\) pixels. Nevertheless, it is worth noting that a too small stride also impacts the efficiency of our algorithm.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
###### Abstract  
Terrestrial laser scanning (TLS) can provide accurate and detailed three-dimensional (3D) structure information of the forest understory. Segmenting individual trees from disordered, discrete, and high-density TLS point clouds is the premise for obtaining accurate individual tree structure parameters of forest understory, pest control and fine modeling. In this study, we propose a bottom-up method to segment individual trees from TLS forest data based on density-based spatial clustering of applications with noise (DBSCAN). In addition, we also improve the DBSCAN based on the distance distribution matrix (DDM) to automatically and adaptively determine the optimal cluster number and the corresponding input parameters. Firstly, the proposed method is based on the improved DBSCAN to detect the trunks and obtain the initial clustering results. Then, the Hough circle fitting method is used to modify the trunk detection results. Finally, individual tree segmentation is realized based on regional growth layer-by-layer clustering. In this paper, we use TLS multi-station scanning data from Chinese artificial forest and German mixed forest, and then evaluate the efficiency of the method from three aspects: overall segmentation, trunk detection and small tree segmentation. Furthermore, the proposed method is compared with three existing individual tree segmentation methods. The results show that the total recall, precision, and F1-score of the proposed method are 90.84%, 95.38% and 0.93, respectively. Compared with traditional DBSCAN, recall, accuracy and F1-score are increased by 6.96%, 4.14% and 0.06, respectively. The individual tree segmentation result of the proposed method is comparable to those of the existing methods, and the optimal parameters can be automatically extracted and the small trees under tall trees can be accurately segmented.  
Keywords:individual tree segmentation; TLS point clouds; DBSCAN; distance distribution matrix; region growth +
Footnote †: journal: _forests_
# Flavor Profile Evolution of Bottle Aged Rose and White Wines Sealed with Different Closures
## 3 Materials and Methods
### Flavor Compounds Detection  
Phenolic compounds were detected via high-performance liquid chromatography/triple-quadrupole tandem mass spectrometry (HPLC-QqQ-MS/MS) method using an Agilent series 1200 instrument fitted with a Poroshell 120 EC-C18 column (150 \(\times\) 2.1 mm, 2.7 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA). The method was established and validated in our earlier work, and is reliable for detecting and quantifying 45 non-anthocyanin and 95 anthocyanin compounds [29; 30]. Volatile compounds were detected using headspace solid phase micro-extraction (HS-SPME) with a 2 cm DVB/CAR/PDMS 50/30 \(\upmu\)m SPME fiber (Supelco, Bellefonte, PA, USA) and an Agilent 7890 gas chromatography kit equipped with an Agilent 5975 mass spectrometer (GC-MS). The details were given in our previous study [31]. Retention indices (RI) [adjected by C7-C24 n-alkane series (Supelco)] were compared with those in the NIST11 database via an Automatic Mass Spectral Deconvolution & Identification System (AMDIS) for identification. The ChemStation software (Agilent Technologies, Inc.) was used to calculated the peak areas and quantification was conducted on the basis of the calibration curve of volatile compound standards purchased from Sigma-Aldrich (St. Louis, MO, USA).  
Three bottles of wine samples from each closure treatment were collected randomly every six months for flavor compound detection. Each sample was analyzed in duplicate.
## 2 Materials and Methods
### Datasets
#### 2.2.3 Vegetation Distribution Map  
This study used the global MODIS International Geosphere-Biosphere Projects (IGBP) majority land-cover classification at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) to identify global vegetated areas and vegetation classes (Figure 1). We reclassified the plant function types into eight categories: evergreen needle-leaf forest (ENF), evergreen broad-leaf forest (EBF), deciduous needle-leaf forest (DNF), deciduous broad-leaf forest (DBF), mixed forest (MF), grassland (GRA), cropland (CRO), and shrub (SHR).
## 3 Results and Discussion
### Antioxidant Capacities of Porcine Organ Hydrolyzates
#### 3.3.2 ABTS Radical Scavenging Activity  
Regarding ABTS radical scavenging activity (Figure 4a-e), a concentration-dependent increase in inhibitory activity was observed, similar to the results for DPPH radical scavenging activity. At the highest concentration of 10 mg/mL, no dramatic differences in ABTS radical scavenging activity were observed for any porcine organ, regardless of the type of protease used. In all tissues, the hydrolyzates exhibited significantly lower  
Figure 2: A representative photo of protein SDS-PAGE (5% stacking gel and 12% separating gel) of Alcalase- and trypsin-mediated protein hydrolyzates from pork organs. PM, standard protein market; H, heart; K, kidney; S, spleen; V, liver; L, lung; A-, Alcalase-hydrolyzed; T-, trypsin-hydrolyzed. The arrows indicate the intact protein bands after hydrolysis.  
IC\({}_{50}\) values than the non-hydrolyzed control (Figure 4f), indicating that enzymatic hydrolysis improved ABTS radical scavenging activity. In particular, the Alcalase-mediated porcine kidney hydrolyzate had the lowest ABTS IC\({}_{50}\) (2.06 mg/mL) (\(p<0.05\)). Previously, [PERSON] et al. [25] observed no difference in ABTS radical scavenging activity (41.0-44.6%) in Alcalase- and Protamex-hydrolyzed bovine lung, bovine kidney, and porcine heart. Furthermore, they suggested that the distribution of low-molecular-weight peptides had no connection to the ABTS radical scavenging activity [9]. Likewise, in our study, no clear correlation between the degree of hydrolysis and ABTS radical scavenging activity could be identified, which supported the previous theory.
# Dynamic Load Redistribution of Power CPS Based on Comprehensive Index of Coupling Node Pairs
## 1 Introduction  
The smart grid is a typical cyber-physical system (CPS) [1] with a large number of measurements, control units, advanced information, and communication technologies. These technologies make the power system operate more stably and safely, but also exacerbate the system's vulnerability. Due to cyber and physical network coupling, cross-space cascade failures can carry the risk from one side to the other. For example, a major cause of the North American blackout in 2003 was a state estimation failure in the information system, when an unexposed failure occurred on the physical side at the same time [2].  
The currently available techniques for preventing and mitigating the propagation of cascading failures can be divided into three categories [3]: attack prevention techniques, attack detection techniques, and intrusion tolerance techniques. Since the currently available detection and defense means are not perfect and the system is inevitably compromised, the application of the third approach is gaining attention in order to maintain the observability and controllability of the system as much as possible [4]. Ref. [5] investigated the resistance of power systems to attack under an average redistribution strategy. Ref. [6] used the WPA-PFRA approach to redistribute the currents in the network. However, the above two papers consider only the power system and do not address the cyber side. Ref. [7] described the redundancy characteristics of nodes based on the \"load-capacity\" model using overload states and failure probabilities for load redistribution, but this method is only applied in single-layer networks. Ref. [8] used a local nearest neighbor weighted assignment strategy to analyze cascading failures in complex networks. A load redistribution model with an adjustable redistribution range and redistribution uniformity is proposed in [9]. Ref. [10] aimed to reduce the critical threshold and effectively improve the robustness of the network, allocating based on the maximum available capacity of the remaining nodes. However,there are many problems in the above papers, such as not considering both the structural and functional characteristics of the system, or not considering the cyber side and the physical side organically.  
Therefore, in this paper, based on the third intrusion-tolerant solution, load redistribution under cascade failure is carried out in two steps as follows:  
1. The integrated importance and vulnerability indexes of node pairs are established by combining the functional characteristics of the system.
2. Based on the update of indicators, dynamic load redistribution of information flow and power flow of failed nodes is carried out in the process of cascading failure, so that the fault can be terminated quickly.
## 4 Discussion
### Experimental Results Analysis
However, high-resolution DEMs are difficult to obtain, as they typically need to be generated from the original low-resolution DEM combined with panchromatic stereo images of optical images, which involves relatively high thresholds and costs.
# Digital Health Technology for Stroke Rehabilitation in Canada: A Scoping Review
## 3 Results  
The scoping review included 14 studies published between 2010 and 2022, exploring various use of DHTs for stroke rehabilitation specifically in Canada (Table 1). The studies encompassed multiple study designs. Of the 14 studies, 4 (28.6%) were qualitative studies [25; 26; 27; 28], 4 (28.6%) were RCTs [29; 30; 31; 32], 2 (14.3%) were mixed methods [33; 34], 1 (7.2%) was a pilot RCT [35], 1 (7.2%) was a feasibility clinical trial [36], 1 (7.2%) was a multiple case study design [37], and 1 (7.2%) was a pre-post double baseline repeated measures design [38].  
The studies included in this review varied in sample size, which can be categorized as 8 studies with small sample studies (1-10 participants) [25; 26; 27; 28; 32; 34; 37; 38], 3 studies with medium sample studies (11-50 participants) [29; 33; 36] and 3 studies large sample studies (>50 participants) [30; 31; 35].  
10 studies focused on chronic stroke phase [25; 26; 27; 29; 30; 32; 33; 34; 36; 37], while acute phase was explored in 2 studies [31; 35]. One study considered both phase [38] and one not specified the phase of stroke [28].  
Twelve studies focused on UE function [25; 26; 27; 28; 34; 35; 36; 37; 38; 29; 30; 32; 33; 34; 38], while one study examined sitting balance [31], and another study focused on walking speed [33].  
Studies were conducted in various settings, including 7 studies performed home-based [32; 33; 34; 36; 37; 38], 6 studies was in institutional settings [26; 28; 30; 31; 35; 26], and one study considered both home and institution [25].  
Study main goals varied, with 3 studies assessing feasibility [34; 36; 38], while 5 studies investigated intervention effectiveness [29; 30; 31; 32; 35]. 6 studies explored clinician and client perception about technology [25; 26; 27; 28; 33; 37].  
The types of technologies including 6 studies focus on VR [29; 31; 34; 35; 36; 37], 3 study on VR called her work as telerehabilitation [34; 36; 37] 2 studies on robotic devices [25; 30], 4 studies on gaming systems [27; 28; 32; 35], and 3 studies on mobile and sensor-based training [26; 33; 38]. This categorization groups technologies based on their applications, with relevant studies supporting each category.
## 7 Conclusions  
This paper analyzes the performance of a cooperative virtual MIMO system using different modulation techniques in the context of WSNs. In terms of BER performance, BPSK-16 WOFDM is found to outperform other evaluated modulation techniques by up to 95% for a given \(\frac{E_{b}}{N_{o}}\), and in terms of energy efficiency by up to a factor of two for a transmission distance \(d^{L}=100\) m. On the other hand, DQPSK based system performs better in terms of total time delay by up to almost 23%. Thus, DQPSK based system can be a suitable option for WSN applications with less time delay requirement. Virtual MIMO system is 98% more energy efficient as compared to SISO system, which performs better in terms of total time delay by 35%. Overall, BPSK-WOFDM when combined with a cooperative virtual MIMO system architecture shows great potential as a solution for WSNs due to its simpler RF section, lower PAPR and better BER performance.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 5 Support Vector Machines  
A Support Vector Machine (SVM) algorithm is used to classify unlabeled data points into two labeled categories. It learns to separate a cluster of data points into distinct binary groups [34]. Figure 5 shows a cluster of data points classified into class 1 (green) and class 2 (blue). In brief, SVM performs complex data transformations depending on the selected kernel function, and, based on these transformations, it tries to maximize the separation boundaries between data points depending on the labels or classes that we have defined.  
As we can see in Figure 5, SVM tries to find a line that maximizes the separation between a two-class data set of two-dimensional space points. To generalize, the objective is to find a hyperplane that maximizes the separation of the data points to their potential classes in an \(n\)-dimensional space. The data points with the minimum distance to the hyperplane (closest points) are called Support Vectors. The computations of data points' separation depend on a kernel function. There are different kernel functions: Linear, Polynomial, Gaussian, Radial Basis Function (RBF), and Sigmoid.