esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Rheological and Flocculation Analysis of Microfibrillated Cellulose Suspension Using Optical Coherence Tomography**  
**[PERSON] 1,***2**, [PERSON] 2, [PERSON] 3,4 and [PERSON] 2  
1 VTT Technical Research Centre of Finland Ltd., P.O. Box 1603, FI-40101 Jyvaskyla, Finland  
2 Optoelectronics and Measurement Techniques Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, P.O. Box 4500, FI-90014 Oulu, Finland; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (T.F)  
3 Department of Physics, University of Jyvaskyla, P.O. Box 35, FI-40014 Jyvaskyla, Finland; [EMAIL_ADDRESS]  
4 Spinnova Ltd., Palokarjentie 2-4, 40320 Jyvaskyla, Finland  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +358-20-722-2717  
Received: 23 February 2018; Accepted: 7 May 2018; Published: 10 May 2018  
**Featured Application: Optical coherence tomography brings excellent new possibilities for improving and extending existing rheological measurement devices and methods.**  
**Abstract:** A sub-micron resolution optical coherence tomography device was used together with a pipe rheometer to analyze the rheology and flocculation dynamics of a 0.5% microfibrillated cellulose (MFC) suspension. The bulk behavior of the MFC suspension showed typical shear thinning (power-law) behavior. This was reflected in a monotonously decreasing floc size when the shear stress exceeded the yield stress of the suspension. The quantitative viscous behavior of the MFC suspension changed abruptly at the wall shear stress of 10 Pa, which was reflected in a simultaneous abrupt drop of the floc size. The flocs were strongly elongated with low shear stresses. With the highest shear stresses, the flocs were almost spherical, indicating a good level of fluidization of the suspension.  
**Keywords:** shear viscosity; yield stress; flocculation; velocity profile; microfibrillated cellulose; cellulose microfibrils; optical coherence tomography
## 2 Materials and Methods
### Objects and Devices  
The grape variety selected for this experiment was Shine-Muscat, with a total of 23 bunches of grapes and a total of 345 grape samples. On 9 July, 22 July, 12 August, 30 December 2021, and 11 January 2022, the grapes were picked in the greenhouse of Baiyun Base of Guangdong Academy of Agricultural Sciences. The picked bunches had varying degrees of ripeness, smooth surfaces, and defect-free grapes. Based on the experience of cultivators, 5 bunches were around 13 Brix%, 6 bunches were around 16 Brix%, 7 bunches were around 19 Brix%, and 5 bunches were around 22 Brix%. The relevant testing equipment was as follows:  
1. Portable line-scan hyperspectral imager GaiaField-V10 (Sichuan Dualix Spectral Imaging Technology Co., Ltd., Chengdu, China) with built-in pushbroom (as shown in Figure 3). Response spectrum: 400-1000 nm; maximum resolution: 1392 \(\times\) 1040; number of spectral channels: 256; spectral resolution: 3.5 nm; protocol: USB 2.0. The dedicated light source was HSIA-LS-T-200W, consisting of four halogen lamps. The standard whiteboard was HSIA-CT-150 mm \(\times\) 150 mm, made of PTFE, with nominal reflectance of 99%.  
Figure 2: Flowchart of research object and methods.  
2. Digital refractometer PAL-1 (ATAGO Co., Ltd., Tokyo, Japan): measuring range: Brix0.0-53.0%; measuring accuracy: Brix \(\pm\) 0.2%; measuring temperature: 10-100 \({}^{\circ}\)C; working environment temperature: 10-40 \({}^{\circ}\)C, used to measure the soluble solid content of grapes.
3. Tower server Dell T640 (Dell Inc., Round Rock, USA): OS: Ubuntu 18.04; CPU: Intel Xeon(R) Gold 5218 @ 2.30 GHz; GPU: Nvidia GeForce RTX 3090 \(\times\) 2; RAM 64 GB; ROM 4 TB), used for establishing the grape segmentation model.
## 4 Societal Benefits and Impact
### Contributions to Smart City and Community Initiatives
#### 4.3.3 Citizen Engagement and Participatory Safety Management  
Furthermore, a live monitoring system that guarantees privacy promotes community engagement through transparency and communication [98]. Modern smart city initiatives increasingly recognize that technological solutions must be accompanied by robust citizen participation to be truly effective and equitable. Public dashboards, data-driven storytelling, and educational outreach programs can help inform citizens about how their city's infrastructure and services are prepared to respond to emerging risks. Unlike traditional \"black box\" systems, our framework emphasizes accessible visualizations and plain-language interpretations of safety analytics, enabling meaningful public discourse about risk priorities.  
The system can incorporate citizen-reported observations through mobile applications or community portals, creating a hybridized monitoring approach that combines automated sensing with human intelligence. This feature transforms residents from passive subjects of surveillance into active participants in the safety ecosystem. Local residents, businesses, and other advocacy groups could use these insights to call for investments in public health resources or to suggest community-led programs that address local challenges, from forming neighborhood watch groups to creating networks of volunteers trained to help vulnerable populations in times of crisis. The system can even help match community needs with appropriate resources, such as connecting vulnerable elderly residents with nearby volunteer networks during extreme weather events.  
By making safety metrics and response times transparent, the system creates natural accountability for service providers and city departments. Citizens can track progress on safety initiatives and hold officials accountable for addressing identified hazards, while officials gain tools to demonstrate the impact of their investments.
# Functionality of the Search and Rescue Transponder (SART) in Maritime Search and Rescue Actions
## 5 Conclusions
* Performance of circular polarization SARTs. * Use of other radar and SART model devices. All research aims to improve the effectiveness of SAR operations, as the findings are also valuable for educational purposes. Proper training of seafarers ensures better performance in emergency situations. Improving the competence and knowledge of officers eliminates possible mistakes, increasing the chances of saving people at risk. More emphasis should be placed on understanding the operation of rain clutter, sea clutter, and gain, as well as the impact of automatic functions on the radar image. Crews also need to be thoroughly familiar with the use of lifesaving appliances to maximize their effectiveness and increase their chances of distress. Similar studies should be conducted, as the results of such research impact the ability to respond quickly in dangerous situations and provide assistance to people in distress at sea. Conceptualization, M.M., M.W. and B.R. ; methodology, M.W. and M.M. ; software, M.W; validation, M.M. and M.W. ; formal analysis, M.W. and M.M. ; investigation, M.M. and B.R. ; resources, M.M. and B.R. ; data curation, M.W. ; writing--original draft preparation, M.M. and M.W. ; writing--review and editing, M.M. and M.W. ; visualization, M.M., M.W. and B.R. ; supervision, M.W. and M.M. ; project administration, M.M. ; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript. This research outcome has been achieved under the research project 1/S/WiIT/RD/25, financed from a subsidy of the Ministry of Science and Higher Education for statutory activities of the Maritime University of Szczecin. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest.
## 2 Methodology
### Component Degradation Characterisation  
The nature of filter degradation, i.e., clogging over time due to the accumulation of particles or debris [21] (FC1), was emulated on the testbed by using the position of a valve (DPV1) that was proportional to the level of severity. In the case of the pump (FC2), pump failures can result from motor or drive issues, such as electrical faults, mechanical wear, or overheating, or leaks leading to a complete breakdown of the pumping system [22]. A degraded pump with leaks was simulated on the testbed with a change in the opening of a bypass valve (DPV2) connected to the main line (see Figure 1). Sediment build-up [23] was also emulated as the degradation of the valve (FC3) by a gradual closure in DPV3 or a step change in the valve opening. In the case of the nozzle (FC4), represented by DPV4, partial blockage generated by solid particle contaminants present in the fluid [24] was emulated by a gradual closure of the valve. Finally, a leaking pipe [15] faulty condition scenario (FC5) was emulated via a step change in the DPV5 valve opening, which offered the possibility of creating fine leaks or a significant pipe burst. In this paper, only two faulty conditions occurring simultaneously for the MCD case were considered, with each valve associated with each component under consideration set at a specific valve opening.
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 4 Results and Discussion
### Classification Accuracy on a Generic Dataset  
An additional experiment was conducted to evaluate the classification performance on analog modulation signals, and a GUN radio generated dataset (RML2016b) was used [16]. In the test, the dataset was divided into a training set, validation set and test set. We used the training set to train StudentNet, and used the validation set to evaluate the performance of the current classifier and select the best classifier for testing. For the MentorNet classifier, the trained MentorNet was used to supervise the training of StudentNet. For the Baseline classifier, the StudentNet was trained without MentorNet. As shown in Figure 13, the comparison of classification accuracy was made among MentorNet classifier and some classical methods such as the Baseline, ResNet and CLDNN [29] classifiers. When the SNR was greater than 0 dB, our proposed MentorNet classifier could achieve the overall classification accuracy up to 85.5%, which was better than the Baseline (82.2%), CLDNN (83.1%) and ResNet (80.5%). The comparison results indicate that the proposed MentorNet classifier could also deal with the analog modulation signals with better versatility and classification accuracy.  
Figure 12: Classification accuracy with (**a**) different carrier frequency offsets and (**b**) different phase offsets.  
Figure 13: Classification accuracy of various classifiers on dataset RML2016b.
# Structural, Thermal and Mechanical Assessment of Green Compounds with Natural Rubber
## 3 Results  
### Mechanical Properties  
#### 3.1.1 Compounds including Non-Devulcanized GTR  
The stress-strain curves for NR vulcanizates with different amounts (10, 30 and 50 phr) of non-devulcanized GTRcar (GTRc) and GTRtruck (GTRt) are shown in Figures 1 and 2. When compared to pure NR vulcanizates, the modulus at 300% of elongation decreases in all compositions of NR/GTRcar and NR/GTRtruck.  
Moreover, the deformation at break decreased with the increase of GTR (non-devulcanized) content, especially in the case of GTR from trucks. For 100 NR the maximum elongation at break was 854% with a tensile strength of 26.1 MPa, but when NR was filled with 50 phr of GTR, the elongation at break and tensile strength decreased to 695% and 15.5 MPa for GTRcar and 675% elongation, 14.1 MPa tensile strength for GTRtruck. The decreasing of tensile properties in all NR/GTR blends is attributed to the lack of bonding between GTR and NR. The GTR does not disperse well in the continuous elastomeric matrix, which creates weak sites on stress transmission, resulting in a decrease of tensile properties.  
Figure 1: Stress–strain curves for NR vulcanizates with different amounts (0,10, 30 and 50 phr) of non-devulcanized GTRcar (GTRc).
## 5 Discussion  
Figure 4 shows the seasonal variation of CO\({}_{2}\) concentration and the differences between TVP-modeled and TANSO-observed CO\({}_{2}\) concentration. The TANSO XCO\({}_{2}\) time series showed a more pronounced seasonal variation and the extreme values appeared in different months on different continents. In Africa, the observed CO\({}_{2}\) concentration ranged between a maximum of 384.13 ppm and a minimum of 376.00 ppm between June 2009 and November 2011. The maximum and minimum appeared in March and September, respectively. The CO\({}_{2}\) concentration estimated by TVP model were overestimated before September 2010 and underestimated in 2011. The largest difference between the modeled and observed CO\({}_{2}\) concentration was \(-\)2.43 ppm, which occurred in September 2009. In Australia and South America, the maximum value of CO\({}_{2}\) concentration was usually in June or July and the minimum value appeared around December. Moreover, Figure 4 shows that the fluctuation of CO\({}_{2}\) concentration from June 2009 to December 2011 was very mild in Australia, where the difference between minimum and maximum value of TANSO XCO\({}_{2}\) was 4.17 ppm. This low fluctuation mainly results from the land-cover types in Australia, where bare areas covered 41.79%. The amount of CO\({}_{2}\) sequestered by desert soils is lower than by other soils [77]. Lack of plant photosynthetic is the main reason for the mildly seasonal change of CO\({}_{2}\) concentrations. The largest differences between TVP-modeled and TANSO-observed CO\({}_{2}\) concentration was 1.24 ppm in Australia and the average value is 0.62 and 0.95 ppm in Australia and South America, respectively. In Eurasia and North America, the minimum values of CO\({}_{2}\) concentration appeared in August and the maximum values appeared in April or March. The difference between minimum and maximum value of TANSO XCO\({}_{2}\) was 9.46 and 8.39 ppm in Eurasia and North America, respectively. In Eurasia, overestimating of CO\({}_{2}\) concentration by TVP model was found from June 2009 to April 2010 and underestimating appeared after December 2010. In North America, the overestimating happened from June 2009 to April 2010 (except December 2009) and from then on underestimating happened (except December 2010). The average difference between TANSO-observed and TVP-modeled CO\({}_{2}\) concentration was 0.97 and 0.84 ppm in Eurasia and North America, respectively. In Africa, the maximum overestimating of TVP model was 2.43 ppm in September 2009 and the maximum underestimating value was 1.86 ppm in February 2011.  
Figure 4 also shows that in the study time scale, TANSO XCO\({}_{2}\) clearly increased in all the study areas. The maximum slope was in Eurasia (slope = 0.13) and the minimum slope was in Australia (slope = 0.09).  
Figure 4: The seasonal variations of CO\({}_{2}\) concentrations on each continent and the differences of TVP-estimated CO\({}_{2}\) concentration compared with the TANSO-observed values. Hollow points are the TVP-modeled CO\({}_{2}\) concentration and solid points are the TANSO-observed values. The lines are the trend line of TANSO XCO\({}_{2}\).
## 1 Supplementary 2020  
Visible Light Communication (VLC) technology is an emerging technology using visible light modulation that, in the modern world, will mainly facilitate high-speed internet connectivity. VLC provides tremendous advantages compared to conventional radio frequency, such as a higher transmission rate, high bandwidth, low-power consumption, no health hazards, less interference, etc., which make it more prominent in recent days. Due to their outstanding features, including low cost, low power consumption, etc., \(\mu\)-light-emitting diodes (LEDs) have gained considerable attention for VLC implementation, but mostly for the ability to be used for lighting as well as communications. In this review paper, we will focus mainly on recent developments in VLC applications and various factors affecting the modulation bandwidth of VLC devices. Numerous factors, such as quantum confined stark effect (QCSE), carrier lifetime, carrier recombination time, crystal orientation, etc. affect the modulation bandwidth of LEDs, and more information will be discussed in the following sections. This paper will focus on VLC applications based on LEDs but mainly on semipolar \(\mu\)-LEDs and \(\mu\)-LED-based arrays with high bandwidths. Another important application of VLC is underwater optical wireless communication (UOWC), which has drawn a huge interest in marine exploration and underwater connectivity, but still faces some challenges because visible light is being used. In addition, this paper will focus on how the current VLC system modulation bandwidth can be enhanced. Many methods have been introduced, such as decreasing the active layer thickness or effective active area or using doping, but the bandwidth is restricted by the recombination time when the system configuration reaches its limit. Therefore, it is important to find alternative ways such as optimizing the system, using the blue filter or using the equalization technology, which will be addressed later. Overall, this review paper provides a brief overview of the VLC-based system performance and some of its potential prospects.  
**Keywords:** visible light communication (VLC); semipolar; \(\mu\)-LEDs; modulation bandwidth; QCSE; UOWC
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 6 Echocardiography
### Segmentation  
Deep learning provides new possibilities for echocardiography procedures to produce an accurate and automated interpretation of echos, thus potentially decreasing the risk of human error. [PERSON] et al. (2018) presented a large publicly available dataset, namely CAMUS, consisting of echocardiography images and video sequences from 500 patients. They applied several deep learning and non-deep-learning techniques for the segmentation of the LV endocardium and myocardium. The techniques include UNet, UNet++, anatomically constrained NN, encoder-decoder, stacked hourglasses, B-Spline explicit, the active surface model (BEASM) framework, and structured RF. They implemented two UNet architectures and referred to them as UNet-1 and UNet-2. Both had different feature maps, resolutions, upsampling schemes, etc. According to their research, the best accuracy was achieved by the Unit-2 architecture. [PERSON] et al. (2018) also applied UNet and feature pyramid network (FPN) ([PERSON] et al., 2018) on the CAMUS ([PERSON] et al., 2018) dataset for LV segmentation. FPN was applied for feature extraction and used a pyramid concept with improved accuracy and speed. The FPN generated multiple feature maps with certain quality information than other feature pyramids for object detection. In the UNet architecture, two more downsampling layers were added for extracting the global information of the image. Due to the low resolution of images, a few encoding-decoding layers were used, which had an impact on overall accuracy. [PERSON] et al. (2018) proposed an encoder-decoder approach along with FPN for the left ventricle segmentation. They achieved a Dice similarity coefficient of 93.10.  
One of the important aspects of training a segmentation model is having a relatively rich initial training set. This implies that the model outperforms when it uses 100 labeled videos from different patients rather than one hundred videos from the same patient. Due to the nature of the echocardiographic modality, augmentation methods can generate a limited number of training data. Therefore, increasing the training data is achievable by using more videos from more instances, which is sometimes difficult due to privacy laws. Moreover, the number of decoding and encoding levels in the segmentation network depends on the resolution of the input frame. More powerful hardware is required to perform segmentation on input frames with higher resolutions, which will increase the number of semantic strengths.
# SAWIT--Security Awareness Improvement Tool in the Workplace
## 6 Related Work and Discussion  
There are many materials available for security awareness in different forms, such as textbooks, infographics, video-lectures, games, and online courses (e.g., cybrary.it, cyberaces.org, edx.org), but most of them are focused on technical users. In addition, they are usually time-consuming, and very few have a user-centric approach to provide the end user with what he/she actually needs to know. The complexity and overwhelmingly technical aspects of information security awareness programs often present obstacles to self-learning for non-technical employees. On the other hand, although there is awareness training for end users, it is often limited to one specific threat such as ransomware [40], or phishing [41; 42; 43]. Besides that, most training programs fail to expand the learning content and offer no possibilities for collaboration or the ability for an ontological search.  
[PERSON] et al. presented the prototype for an information security toolkit for raising awareness [17]. Although their prototype has a similar concept to SAWIT, as they have both a pre-test and post-test to evaluate learning outcomes, there is no available collaboration between colleagues or a semantic search.  
[PERSON] et al. proposed InCAT (Intelligence-based Cybersecurity Awareness Training), which connects the domain of cyber security awareness training with that of cyber threat intelligence [44]. The system is powered by the Watson Knowledge Studio platform and is still in the experimental phase. Although this is very promising, there is no collaboration between users, and the user is not able to add learning objects.  
Work presented in [45] shows the system that collaboratively combines information from traditional and nontraditional sensors to produce new, relevant signatures. They also used the collaborative approach, but this approach is directed to detecting malicious traffic. On the other hand, the SAWIT collaborative approach is focused on education of employees in the area of cyber security in order to improve security awareness.  
There is a good attempt to gather the knowledge from heterogeneous data sources [46]. This approach proposed the use of data fusion to enhance the defensive capabilities of the network and aid in the development of situational awareness for the security analyst. Unlike SAWIT, this approach does not support the collaborative learning since the analyst has only a limited (human) capacity to observe and interpret cyber security details.  
Very interesting work is presented in [47] exploring the important conceptual issue of how to manage uncertainty and risk in cyber situational awareness. This approach described by two case studies is focused on individual system administrator, unlike the SAWIT tool, which proposes a collaborative learning approach.  
To the best of our knowledge, other tools for security awareness training offer no possibilities for semantic annotations and collaborative learning, which is valuable in enhancing information retrieval and improving interoperability.
# A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
## 3 Proposed Method
### Heuristic Algorithm for Line-to-Line Fault Location
#### 3.2.4 Threshold Determination
It is acknowledged that the proposed heuristic algorithm assumes relatively stable module voltage distributions under fault conditions. While minor environmental variations and measurement noise are mitigated through the use of module group voltage averages, significant fluctuations may affect accuracy. Robustness testing under various non-ideal conditions and incorporation of filtering or adaptive thresholding techniques are important future research directions to enhance practical applicability.