esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Study of the Degree of Grinding and Screw Rotation Speed on the Temperature of Seed Cake  
The obtained data revealed the temperature of the cake after pressing at different screw rotation speeds. At a screw rotation speed of 30 rpm, the cake temperature ranged from 92 \({}^{\circ}\)C to 105 \({}^{\circ}\)C, depending on the grinding degree. Increasing the screw rotation speed to 60 rpm led to slightly higher temperatures, ranging from 93 \({}^{\circ}\)C to 106 \({}^{\circ}\)C. Further increasing the speed to 120 rpm and 250 rpm resulted in temperatures ranging from 95 \({}^{\circ}\)C to 107 \({}^{\circ}\)C and 108 \({}^{\circ}\)C, respectively (Figure 17).  
The findings indicate that increasing the screw rotation speed during the pressing process leads to an elevation in the temperature of the cake. Moreover, the installation of grinding sieves with smaller hole diameters also contributes to an increase in the cake temperature compared to the oil press without such grinding mechanisms.  
These results offer valuable insights for optimizing oil pressing operations. It is important to consider the desired temperature range for the cake during the pressing process, as temperature can influence the quality and characteristics of the extracted oil. Depending on the specific requirements, operators can adjust the screw rotation speed and choose appropriate grinding sieves to achieve the desired temperature conditions for the cake.  
The degree of grinding significantly affects the temperature of the cake after pressing. Thus, if the temperature of the cake after pressing without preliminary grinding was 92 \({}^{\circ}\)C, then grinding the cake through a 2 mm grating raises the temperature at the output to 105 \({}^{\circ}\)C. At the same time, the increase in the number of revolutions insignificantly affects the temperature of the cake after pressing.  
The main factors of the cake temperature increase are mechanical friction due to the screw rotation speed, the work of grinding elements (knife and grid), intensive compression of the cake, and energy conversion into heat.  
Figure 16: Dependence of cake density on the degree of sunflower seed grinding and screw rotation speed (different letters above the lines indicate significant differences between samples, \(p<0.05\)).
# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
###### Abstract  
Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm's high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without deprecating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.  
moving points; HDBSCAN; crowd clustering; unsupervised learning +
Footnote †: journal:  
doi:10.3390/telecom4010001
# Temporomandibularibular Disorders and Serological Tests in Patients with Rheumatoid Arthritis
###### Abstract  
Background: Rheumatoid Arthritis (RA) is a systemic and chronic autoimmune disease with inflammation at the synovial joints. The purposes of this study were to evaluate the correlation between serological variables and temporomandibular disorders (TMDs) in patients diagnosed with RA, evaluated through diagnostic criteria for temporomandibularibular disorders (DC/TMD), and to check the influence of comorbidities on the different TMD signs and symptoms, or any serological variables. Methods: This observational cohort research study included seventeen patients affected by RA. The comorbidities and some variables from the initial serological analyses were collected from the clinical rheumatological charts. Then, the presence of any of the following TMD signs/symptoms, temporal myalgia, temporomandibular joint arthralgia, click or crepitus, was evaluated through the symptom questionnaire of the DC/TMD during clinical evaluation following the DC/TMD examination form. Results: Rheumatoid factor (RF), anticitrullinated protein antibodies (ACPA), and anti-nuclear antibody (ANA) positivity were present in 82.4%, 52.9%, and 41.2% of patients, respectively. Indicators of tissue inflammation were evaluated with median values of 21 mm/h for erythrocyte sedimentation rate (ESR) and 0.50 mg/dL for C-reactive protein (CRP). The RA patients presented comorbidities such as hypertension in 70.6% and diabetes in 23.5%. Negative ACPA had a significant association with myalgia (\(p\) = 0.03), and positive ANA was significantly associated with crepitus (\(p\) = 0.05). Conclusion: ANA and ACPA evaluation can be considered predictive serological tests associated with specific TMDs. Conversely, no influence of any comorbidities was found between different TMD signs and symptoms, or any serological variables.  
rheumatoid arthritis; arthritis; temporomandibular joint disorders; DC/TMD; serology; rheumatoid factor; anticitrullinated protein antibodies; anti-nuclear antibody
## 4 Computational Experiments
### Optimal Solutions from HGA for PDVRP of BSS
Figure 7 shows a preliminary run with 4000 generations. As this is a multi-objective problem, we define a weighted sum of the two objective values to represent each solution in the figure. The weighted sum is a simple method for balancing the scale between the travel distance and the sum of the absolute net deviations. Based on preliminary runs, we set the terminating condition of the HGA for this problem to 2000 generations. Although the solution continues to improve after 2000 generations, this number represents a reasonable compromise between solution quality and computation time. The processing time for running 2000 generations of the HGA takes around 8518.66 s (approximately 2 h and 22 min) on a Mac M1, which is four times longer than the sample case with 1000 generations. The computational burden of this approach arises from finding the optimal bike allocation plan for each chromosome, which consists of two routing paths of different lengths. Although we maintain a memory to store the optimal allocation solutions for chromosomes that have appeared during the evolution process of the genetic algorithm, the large number of possible chromosomes in a large-scale problem increases the computational burden. The Pareto frontiers from the HGA are displayed in Figure 8. The traveling distance (maximum distance between the two travel paths) ranges from 13.4 km to 20.9 km, and the sum of absolute net deviations (SAND) ranges from 3391 to 3415 bikes. The SAND is the sum of 40 clusters encompassing 126 bike stations. Thus, for each bike station, the net  
Figure 7: Convergence of HGA within 4000 generations. deviation is approximately 27 bikes per bike station during three hours after rebalancing work with the management vehicle that carries up to 40 bikes. The solutions from (a) to (b) are acceptable in terms of the travel path. The paths of the solutions to the right of (b) are ineffective because there is too much overlap between the travel paths of the two vehicles. Figure 9 shows the travel paths of two management vehicles for each selected result. The left figure (marked as (a)) shows the two paths with the shortest travel distance, whereas (b) represents the smallest SAND among the effective solutions from the frontiers. As the solution changes from (a) to (b), the travel distance increases 3.83 km (13.40 km to 17.23 km), while the SAND decreases by 18 bikes (3415 ea to 3397 ea). The Pareto frontiers offer options from the decision-makers' perspective. If the decision prioritizes minimizing travel distances, then the solution with the most realistic shortest distance is selected. However, if the decision must consider a certain threshold for the minimum number of bikes to be served, the solution can be determined by drawing a horizontal line on the frontier at an appropriate level and selecting the solution with the shortest distance from solutions below the horizontal line.
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
###### Abstract  
The emergence of super-resolution imaging makes it possible to display the microvasculatures clearly using ultrasound imaging, which is of great importance in the early diagnosis of cancer. At present, the super-resolution performance can only be achieved when the sampling signal is long enough (usually more than 10,000 frames). Thus, the imaging time resolution is not suitable for clinical use. In this paper, we proposed a novel super-resolution reconstruction method, which is proved to have a satisfactory resolution using shorter sampling signal sequences. In the microbubble localization step, the integrated form of the 2D Gaussian function is innovatively adopted for image deconvolution in our method, which enhances the accuracy of microbubble positioning. In the trajectory tracking step, for the first time the averaged shifted histogram technique is presented for the visualization, which greatly improves the precision of reconstruction. In vivo experiments on rabbits were conducted to verify the effectiveness of the proposed method. Compared to the conventional reconstruction method, our method significantly reduces the Full-Width-at-Half-Maximum (FWHM) by 50% using only 400-frame signals. Besides, there is no significant increase in the running time using the proposed method. Considering its imaging performance and used frame number, the conclusion can be drawn that the proposed method advances the application of super-resolution imaging to the clinical use with a much higher time resolution.  
ultrasound imaging; super-resolution; microbubble; reconstruction; time resolution +
Footnote †: journal: Medical Imaging  
17 June 2018; Accepted: 5 July 2018; Published: 13 July 2018
# Handover Management for Drones in Future Mobile Networks--A Survey
## 2 Drone Networks
### Drones' Connectivity  
Ensuring smooth, reliable, and continuous connectivity for drones is one of the major challenges that faces the implementation of drones over mobile wireless networks. To provide reliable connectivity of connected drones, numerous solutions have been proposed for connectivity and networks' management. For example, the authors in [37; 38] proposed a machine learning method and massive Multiple-Input Multiple-Output (MIMO) design to enhance connectivity and security of connected drones, respectively. Ericsson has also introduced a control scheme, as demonstrated in Figure 3. The system manages drone flight administration while simultaneously coordinating with a manned aircraft  
Figure 1: Drone system architecture, solutions, and integration in future mobile networks.  
Figure 2: Drones and cellular network integration opportunities.  
management scheme. Additional information such as weather forecasts is fed to the drone's control system.  
A drone identification mechanism is implemented to identify, track, command, and control drones and drone fleet operators. Authentication and authorization processes are crucial for providing a secure communication. The system enables vehicle-to-vehicle (V2V) communication while avoiding collision. Wireless connection is necessary to establish the V2V communication and simultaneously provide network management. Connectivity can be accomplished over licensed or unlicensed spectrums. The former can be established via satellite communication or by utilizing ground cellular networks, which is generally more preferable.  
The authors in [39] introduced a massive MIMO based on conjugate beamforming to provide more reliable connectivity to cellular networks. In [40], a system was designed with directional antennas for the drone BS to lessen the aerial UE's LoS path. However, the works of [39; 40] only assumed static hovering drones without considering the integration of cooperative communication, even though it leads to reduced levels of inter-cell interference. The framework presented in [29] utilized the Coordinated Multi-Point (CoMP) of maximum ratio transmission to enhance the Signal-to-Noise Ratio (SNR), thereby improving the cellular connectivity of aerial UEs. The network consists of several separate clusters in which BSs collectively provide services to one of the aerial UEs by utilizing CoMP communication. Two different situations are present: hovering and mobile drones.
# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 3 System Introduction
### Dynamic Filtering Strategy
#### 3.2.2 Object Detection Thread
To further improve feature utilization efficiency, this paper introduces a structural modification, as shown in Figure 5b. The improved C3 module adds three feature branches to the original structure. Two branches are still responsible for extracting information at different scales or depths, while the new branch retains the unprocessed features and performs an element-wise addition with the main path results. This structure enhances the interaction between the paths before fusion and creates a more expressive composite feature map after fusion. Following the identification of potential dynamic regions and generation of the corresponding dynamic mask, the localization system employs this mask to perform preliminary filtering of extracted ORB features during the initial camera pose estimation phase. Subsequently, the remaining static features are utilized to compute a coarse initial estimate of the camera's pose. During the tracking phase, the system further leverages this dynamic mask in combination with multi-view geometric constraints to jointly identify and eliminate dynamic features from the estimation process.
# Nanoelectrode Ensembles Consisting of Carbon Nanotubes
## 3 Results and Discussion
### Lead Detection in Acetate Buffer  
The CNT-NEE was applied to the detection of Pb\({}^{2+}\) in 0.1 M acetate buffer (pH 4.5, 40 mL) using square-wave anodic stripping voltammetry (SWASV). The deposition potential (\(E_{\text{dep}}\)) was \(-\)2.0 V, and the deposition time (\(t_{\text{dep}}\)) was 300 s. After the deposition of Pb\({}^{2+}\) on the CNT-NEE, it was stripped off in the potential window of \(-\)2.0 V to +0.3 V with a potential step (\(E_{\text{step}}\)) of 0.010 V, an amplitude of 0.05 V, and a frequency of 30 Hz. The SWASV response of the CNT-NEE increased as the concentration of Pb\({}^{2+}\) in acetate buffer (pH 4.5) increased, as shown in Figure 8a, and a linear range of 10-35 ppb Pb\({}^{2+}\) was observed. A calibration curve was constructed by plotting the peak height against Pb\({}^{2+}\) concentration (Figure 8b).  
\[i_{p}(\upmu\text{A})=(0.065\pm 0.001)C(\text{ppb})-(0.555\pm 0.017) \tag{6}\]  
\[\text{R}^{2}=0.999\]  
Figure 7: SECCM characterization of the CNT-NEE. (**a**) Illustration of the SECCM contact area, where the CNT-NEE is the working electrode and an Ag/AgCl wire inserted in the micropipette is the QRCE. (**b**) Sigmoidal voltammogram collected using SECCM.  
The limit of detection (_LOD_) was determined to be 0.57 ppb, using \(LOD=3S_{a}/b\), where \(S_{a}\) is the standard deviation of the blank solution, and \(b\) is the slope of the calibration curve. The relative standard deviation (RSD) for the reproducibility of the CNT-NEE electrodes was 18.3%, while the RSD for repeatability was 14.7%, (see the SI for more information). Controlled potential electrolysis (CPE) was performed as a cleaning method between runs for 120 s at +0.1 V, which has shown to be effective to achieve good reproducibility and repeatability. Previously, [PERSON] et al. reported the detection of Pb\({}^{2+}\) in 0.1 M NaNO\({}_{3}\) at concentrations as low as 2 ppb Pb\({}^{2+}\) using a CNT-NEA modified with a mercury film [4]. [PERSON] et al. used a CNT-NEA coated with a bismuth film to detect heavy metals in 0.1 M acetate buffer (pH 4.5), and an _LOD_ of 40 ppt was achieved for both lead and cadmium [5]. Both mercury and bismuth are known to aid in the detection of lead in solution. In contrast, the CNT-NEE reported here is capable of detecting the World Health Organization maximum contamination level of 10 ppb Pb\({}^{2+}\) without using any additional metals.
## 3 LoRaWAN Background
### LoRaWAN Regional Parameters  
The LoRaWAN regional parameters document [53] provides, among other things, the transmissions management strategies and the channel frequencies to be used for LoRaWAN communications. The document also recommends that, to obtain authoritative information, one has to refer to the specific laws and regulations of the country or region in which the LoRaWAN EDs operate.  
For instance, in Europe the operation in the EU868 band is regulated by the ETSI regulations EN 300 220 [59; 60], in the sub-bands specified in the ERC Recommendation 70-03. For the medium access, the ETSI regulations allow to choose either a Pure ALOHA MAC protocol with duty-cycle limitation or a polite medium access based on LBT AFA. However, these MAC protocols [61] are not suitable for real-time communications, as they lack predictability and do not provide time or frequency reservation mechanisms. Consequently, no end-to-end delay bound can be calculated, either exactly or stochastically [75; 76; 77].
## 1 Introduction
It considered the interdependence between cyber and physical systems at the facility, topology, and functional layers and the coupling relationship between grid integration and the recovery process. Literature [17] proposed a reliability modeling and assessment method for power information systems (i.e., cyberspace in the power system), in which a composite Markov model coupled physical properties and information flow performance in a two-layer model. Literature [18] deeply analyzed the communication system requirements to support the operation of a distribution network. The study demonstrated the high reliability and economic advantages of wireless communication technology in CPDSs. From the perspective of multi-layer networks, literature [19] proposed an evaluation method for the importance of communication nodes. The study focused on the operational characteristics of nodes in communication systems and the dynamic propagation of faults between homogeneous communication nodes. However, the impact of communication system reliability on physical system reliability was not thoroughly analyzed. Literature [20] studied the interdependence between information systems and physical systems in a multi-microgrid CPDS. It proposed a reliability evaluation framework based on Monte Carlo simulation. Research has shown that information systems have a significant impact on the reliability of physical systems, but there is no systematic study on the reliability assessment of a CPDS under wireless access network conditions. The above literature emphasizes the impact of the integration of information systems and physical systems on the reliability of distribution networks. They all take wired access networks as the research object and establish corresponding CPDS reliability evaluation models. At present, research on CPDSs under wireless network access is still in its infancy. Based on the findings of existing research, our work aimed to fill the gap in the existing research and propose a new evaluation method to more accurately simulate and evaluate the reliability of a CPDS under wireless communication network conditions. As stated above, the key contributions of this paper are as follows. (1) An analytical method was used to model the reliability of the information system components, communication channel reliability, and communication delay reliability of the 5G communication network, and the factors affecting the reliability of wireless communication networks are expressed analytically. (2) This paper establishes a reliability model of multiple switches in the physical system based on the analysis of the indirect impact of information failure on the physical system. (3) The reliability model took into account the self-healing treatment of faults, islanding operation, contact lines, load transfer, and the impact of DG access. The reliability assessment was carried out by utilizing the improved least square path method. The results of this study are close to the operation of a CPDS in practice.
## 2 Results and Discussion
### Cluster Analysis (CA)  
Cluster Analysis (CA) is often used to classify large samples into clusters with high similarities within the class and high dissimilarity between different classes. In this study, a CA was applied to 26 selected compounds with Relative Percent Area (RPA) quality above 80% for the pattern recognition of 10 batches of the authentic Harumanis mangoes and 13 batches of the unauthentic Harumanis, comprising 7 batches of Tong Dam and 6 batches of Susu mangoes, respectively, using between-groups linkages as an amalgamation rule (similarities) and Euclidean distance metric (dissimilarities). These methods had been successfully used in classifying and clustering samples such as tongkat ali roots [14], the Alphonso mango [15], and the Nam Dok Mai mango [16].  
The dendrogram (Figure 5) visibly showed that all samples were divided into two major clusters, suggesting that the samples in different clusters have significant difference [17]. As expected, all the authentic Harumanis mangoes were grouped in Cluster I suggesting that all the samples contained high similarity of the VOCs. In Cluster I, there were three different mini clusters which corresponded to different sources of Harumanis. [PERSON] et al. [18] stated that the mini clusters in the main cluster showed only slight differences but higher similarities, as compared with main different clusters. In addition, the dendogram showed that the unauthentic Harumanis mangoes were in a different cluster than the real Harumanis, confirming that both authentic and unauthentic Harumanis had high chemical dissimilarity. On the other hand, below 0.5 dissimilarity, both Tong Dam and Susu mangoes were grouped in the same cluster (Cluster II).  
Figure 4: Chromatograms of Harumanis, Susu, and Tong Dam mangoes with three chemical compounds appearing in Harumanis: (a) _beta_-ocimene, (b) _trans-beta_ occimene and (c) _allo_-ocimene.