esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The article presents the optimization of supercritical CO2 extraction (SFE-CO2) parameters using response surface methodology (RSM) with central composite design (CCD) in order to produce single variety hop (cv. _Ella_) extracts with high yield and strong in vitro antioxidant properties. Optimized SFE-CO2 (37 MPa, 43 \({}^{\circ}\)C, 80 min) yielded 26.3 g/100 g pellets of lipophilic fraction. This extract was rich in biologically active \(\alpha\)- and \(\beta\)-bitter acids (522.8 and 345.0 mg/g extract, respectively), and exerted 1481 mg TE/g extract in vitro oxygen radical absorbance capacity (ORAC). Up to \(\sim\)3-fold higher extraction yield, antioxidant recovery (389.8 mg TE/g pellets) and exhaustive bitter acid extraction (228.4 mg/g pellets) were achieved under the significantly shorter time compared to the commercially used one-stage SFE-CO2 at 10-15 MPa and 40 \({}^{\circ}\)C. Total carotenoid and chlorophyll content was negligible, amounting to \(<\)0.04% of the total extract mass. Fruity, herbal, spicy and woody odor of extracts could be attributed to the major identified volatiles, namely \(\beta\)-pinene, \(\beta\)-myrcene, \(\beta\)-humulene, \(\alpha\)-humulene, \(\alpha\)-silene and methyl-4-decenoate. Rich in valuable bioactive constituents and flavor compounds, cv. _Ella_ hop SFE-CO2 extracts could find multipurpose applications in food, pharmaceutical, nutraceuticaltical and cosmetics industries.  
hogs; supercritical carbon dioxide extraction; hop bitter acids; flavor compounds; antioxidant activity +
Footnote †: journal: _Antioxidants_
# A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data
## 4 Tool Wear Assessment
### Algorithm Implementation  
In this paper, the wear of the tool is evaluated by building an Elman-Adanoost neural network system. The mean value and variance of the original tool sensor data were input into the neural network as characteristic signals. The training output results were set by setting the threshold value, that is, the three states corresponding to the wear degree: the early wear state: [1,0], the middle wear state: [0,1,0], and the late wear state: [0,0,1]. The evaluation result classification procedure is shown as follows:  
(1) Set thresholds to classify evaluation results, the specific algorithm is shown in Algorithm 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cutting Tool** & **Early** & **Middle** & **Late** \\ \hline First & \begin{tabular}{c} Flute1 \(<\) 87 \\ and \\ and \\ and \\ Flute2 \(<\) 93 \\ Flute3\(<\) 93 \\ \end{tabular} & \begin{tabular}{c} Flute1\(\in\)(87,110) \\ or \\ Flute2\(\in\)(93,110) \\ Flute3\(\in\)(93,120) \\ \end{tabular} & \begin{tabular}{c} Flute1 \(>\) 110 \\ and \\ Flute1 \(>\) 110 \\ Flute2 \(>\) 110 \\ Flute3 \(>\) 120 \\ \end{tabular} \\ \hline Second & \begin{tabular}{c} Flute1 \(<\) 65 \\ and \\ Flute2 \(<\) 65 \\ and \\ Flute3 \(<\) 65 \\ \end{tabular} & \begin{tabular}{c} Flute1\(\in\)(65,105) \\ Flute2 \(\in\) 65,105) \\ Flute3 \(<\) 65 \\ \end{tabular} & \begin{tabular}{c} Flute1 \(>\) 105 \\ Flute2 \(\in\) 65,105) \\ Flute3 \(<\) 65 \\ \end{tabular} \\ \hline Third & \begin{tabular}{c} Flute1 \(<\) 87 \\ and \\ Flute2 \(<\) 97 \\ and \\ Flute3 \(<\) 83 \\ \end{tabular} & \
\begin{tabular}{c} Flute1\(\in\)(87,134) \\ or \\ Flute2\(\in\)(97,134) \\ Flute3 \(<\) 83 \\ \end{tabular} &
\begin{tabular}{c} Flute1 \(>\) 134 \\ Flute2 \(\in\)(97,134) \\ Flute3 \(<\) 83 \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Three tool wear thresholds.  
Figure 10: Flute value of tool 1.  
After the forecast is completed, we can make a reasonable assessment of the result. If the predicted result differs too much from the previously predicted amount of wear, we collect the signal again and then predict the degree of wear again. If the error is still too large, we judge whether the cutter has broken or the device to be processed is wrong and give an alarm to remind the construction personnel. If the difference between this result and the previously predicted wear conforms to the wear curve, the current result is reasonable.  
After the training is completed, the tool wear degree evaluation system has been successfully constructed. After collecting various types of data for feature processing, it is used as the input of the system, and the output is the predicted degree of tool wear, which is the estimated tool state.
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 3 Preliminaries
### HMAO Model  
The proposed HMAO model aims to optimize both clustering and routing for FANETs with high dynamics. In FANETs, the frequencies of topology changes are far higher, UAV mobility is very high, and resources are scarce. Therefore, a model has been developed which incorporates two types of optimizations: MGO and AO for specific functions related to network operations. MGO is selected based on the strength of the local optimization for the selection of CHs in dynamic environments. Inspired by the adaptive strategies of mountain gazelles, an MGO can navigate variable conditions such as the selection of stable and energy-efficient CHs in an ever-changing network. Focusing on local metrics such as residual energy and mobility stability ensures that MGO selects the most suitable UAVs as the CH. This ensures maximum energy efficiency and stable intra-cluster communication by reducing the likelihood of re-clustering.  
AO is incorporated because of its strong exploratory power and is used in global-path optimization. The aquila eagle hunting strategy inspires the AO to detect optimal routes across large areas of search and is highly applicable to multi-hop routing in FANET. AO utilizes global information gathered by ferry nodes, such as the UAV position and load. This enables data to be routed along paths that can minimize delays and avoid congestion.  
The HMAO algorithm combines local and global optimization strengths to enhance both the clustering and routing processes. For MGO, local optimization helps stabilize clusters, but with AO on global optimization, data may be effectively routed through the network. These dual-layer approaches can further stabilize a network with minimal energy usage and packet loss. It therefore endures high mobility along with the dynamic network changes observed in the FANET. The block diagram of HMAO is illustrated in Figure 2.
## 5 Conclusions
In this study, the fixed time RM operation scenarios showed better performances than others overall;
2. The sub-scenario 4V3R of the AS-3 was the best in the average queue length reduction;
3. The AS-4 was the best in the average speed improvement. This study presents the most simplified and isolated application of RM. Therefore, future research should focus on the following:  
1. By using simulation, switch the locations of the on-ramp and off-ramp, so that the off-ramp becomes first, followed by the on-ramp, which would reduce the weaving on the freeway as well as giving large storage capacity for queues so traffic at the intersection is unaffected;
2. Coordinate the signal timing in an adjacent intersection with RM operation in an interchange, with the expansion of such a system to different locations of Makkah, Khurais, and King Fahad Roads, which are considered the most congested in Riyadh, based on the information provided by MOT and RDA;
3. Perform an economic analysis with total costs for deploying the RM to freeways as well as the benefits from the RM in terms of the positive return to the country, including pollution, transportation, and safety costs;
4. Contemplate the methods of applying RM and its operation in reality, the extent to which the drivers accept this system and the appropriate ways to guide drivers to comply with this system, especially aggressive drivers;
5. Adopt fusion data collection methods to improve the process of the description of traffic flow conditions. For example, the Automatic Vehicle Location (AVL) system of public transportation can ease the collection of locational data and enhance its data accuracy in the traffic flow modeling process. Recently the Saudi Arabian Ministry of Interior (MOI), which is in charge of the traffic operation and management, began to consider deploying RM to the main urban arterials that chronically suffer from traffic congestions. King Fahad Road is to be one of the targets for deployment. This will be the best chance to continue future research focusing on the five points indicated directly above. We expect there will be more feasibility research about the implementation of RM in Saudi Arabia very soon. Lastly, we believe that this paper will guide their directions and shed light on their future work. [PERSON] and [PERSON] conceived and designed this research and the specific experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the results; [PERSON]-[PERSON] contributed data collection/analysis tools; [PERSON] mainly wrote the paper and [PERSON] assisted it. This research received no external funding except for the support from the KSU. The authors extend their appreciation to the Research Center of College of Engineering and the Deanship of Scientific Research at King Saud University (KSU) for funding this work through the Graduate Research Support Program (GRSP).
# Social Internet of Vehicles for Smart Cities
## 1 Introduction
In this article, we will present a comprehensive review of the research and analysis of such systems. Social Internet of Vehicles (SIoV) describes both the social interactions among vehicles [22] and among drivers [23]. As described in [23], a vehicular social network is created when a driver enters an area where other people with common interests or relevant content exist. Contrary to this, [PERSON] _et al._[22] describe a vehicular social network as social interactions among cars, which communicate autonomously to look for services (automaker patches or updates) and exchange information relevant to traffic. Given that vehicles are becoming more and more autonomous [24] and that applications supporting social interactions among drivers and passengers are already being developed [25, 26] we strongly believe that SIoV will eventually be a network of both drivers, passengers and cars. This new interconnection among entities on different levels (vehicles, drivers, passengers) creates new capabilities, poses new challenges and exposes the network to new threats. New applications that are based on the social concept of vehicular networking were recently developed. For example, RoadSpeak [27] is a voice chatting system that allow commuters to dynamically enter vehicular social networks on the fly and exchange messages. NaviTweet [26] incorporates the driver's preferences into the navigator's route calculation using traffic voice tweets. Caravan Track [25] is a similar application that allows drivers to share mobility data among a cluster of cars. This application, supported by Ford, can be used to filter incoming information to suit the needs and desires of the driver. Porsche also unveiled its new concept electric car that has the ability to post updates to social media [28]. As we move to the era of the next generation of vehicles, smart vehicles with sensing, communication and sociability capabilities, more applications and technologies are going to be developed that will materialize the idea of SIoV. This article provides an comprehensive survey of the Social Internet of Vehicles. We first review components and technologies of the SIoV in Section 2. We then discuss context awareness in Section 3. In Section 4, we present how social network analysis methods can be applied in SIoVs. Next, in Section 5, we review security and trust issues related to SIoVs. Finally, we discuss current challenges and open issues regarding driver privacy in Section 6.
## 3 Results and Discussion
### MAA Correlation with the Environmental Conditions
A specular trend was instead found considering the mean difference of relative humidity. Furthermore, considering the mean difference of air velocity and of air pressure, an opposite trend for the two case studies was also highlighted; in fact, when MAA indicator increases for one case study, it decreases for the other one. For instance, considering the difference of the air pressure, for case study 1, the MAA increases in CD_OW and OD_CW configurations and decreases in OD_OW and CD_CW ones; the same indicator decreases in CD_OW and OD_CW and increases in OD_OW and CD_CW for the case study 2. The same trend can be highlighted considering the difference of the air velocity. This important divergence can be due to the different characteristics of environments (volume), to the dimensions of the openings (opening surfaces), and to the window airtightness class. Figure 7: MAA (Mean Age of the Air) calculated for each experimental campaign and configuration, and MAA average values obtained for the two case studies. In order to study the results of the two different case studies in depth, MAA was also compared with the air change (G), calculated as shown in Equation (2) considering the monitored environmental  
Figure 8: Correlation between MAA and environmental conditions—OD_OW and CD_OW configurations. Figure 9: Correlation between MAA and environmental conditions—CD_OW and CD_CW configurations. conditions, the total opening area (window and door of rooms when they are open), and the room volume. All the data is reported in Table 1.  
\[\mathrm{G}=\frac{\mathit{v}\mathit{-OA}\ +\ \rho\cdot L_{\mathit{opening-perimiter}}}{ \mathrm{V}} \tag{2}\]  
where:  
1. \(\mathrm{v}\) is the monitored indoor or outdoor air velocity (m/h);
2. OA is the opening surface (window and door when they are open) of the room (m\({}^{2}\));
3. \(\mathrm{\rho}\) is the permeability class of the window, according to [41] (m\({}^{3}\)/hm);
4. L is the perimeter of the considered opening surface (m);
5. V is the volume of the room (m\({}^{3}\)). According to Equation (2), \(\mathrm{G}\) represents the air volume changed in one hour within the room. In Figure 10, it is possible to notice a significant difference in behavior of the regression lines drawn for the two case studies. The slope of the lines (case study 1 in black and case study 2 in the red) mainly depends on the airtightness of the windows; in fact, \(\mathrm{G}\) tends to increase with decreasing of the window airtightness class (Class 1-2 for the case study 1), due to the higher air permeability. In particular, as expected, it is worth noting that the opening surfaces influence the MAA indicator especially in a room with windows with higher airtightness class; it means that in the case study 2, the opening surface area is more effective than case study 1, despite the difference of total opening area of just 1 m\({}^{2}\).
## 1 Introduction
These examples demonstrate the superiority of M-CGP over CGP as the results achieved with M-CGP would not have been possible using traditional CGP alone and without the mechanisms proposed in M-CGP. Therefore, a direct quantitative comparison of the effectiveness of the M-CGP and CGP in the case of the system description being adopted in this work is not possible because CGP cannot be used. At the same time, M-CGP can benefit from all the advantages of CGP. In the case of combinational circuits, M-CGP with a set of functions composed of gates works exactly the same as CGP because these circuits are much simpler. The paper presents examples of the obtained sequential systems created by using the created system. The adopted solutions allow for the selection of the tables that describe the layout in such a way that it is not too extensive on the one hand, and, on the other hand, it forces the algorithm to ensure that the designed system works as expected. Proposed here is a genetic programming model that enables the creation of sequential systems, based on many data sets. For any given data set, the system operation starts from the beginning (from the RESET signal). The system created should work in such a way that it simultaneously satisfies the entire table, describing the operation of the system for all data sets adopted for a given system. The presented genetic programming model also introduces the possibility of determining a table for each row, or output values in a given line, which is generated as a result of the activation signal appearing on the clk inputs or, perhaps, without this signal. In this way, it is possible to determine that the output change should occur when the inputs are changed without an activation signal given to clk. Consequently, it can also be determined that the output states should not change despite the changing input states. This approach also makes it possible to create circuits, which use various sets of flip-flops and gates, starting with a basic one composed of a JK flip-flop and AND, OR, and NOT gates and ending with a set of eight elements: JK, D, T flip-flops and AND, OR, NOT, NAND, and NOR gates.
# Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers
## 2 Sensors for Quantification of HbA1c
### Electrochemical Immunosensors for HbA1c Detection Using Antibodies
Further fine-tuning of the fabrication parameters are required for reducing the response time to under a minute for applications as a point-of-care diagnostic device.
## 6 Conclusions  
This paper fully describes a WSN suitable for inclusion in photovoltaic plants in order to improve efficiency and optimize energy production. This is achieved by developing smart sensor nodes attached to each PV panel in the plant and defining a communication protocol that succeeds in taking all the information related to the panels to a _Monitoring Center_, where it is displayed and conveniently analyzed. A start-up procedure to identify all the nodes in the WSN has also been presented.  
The sensors attached to each panel are supplied power from the panels themselves. Thus, a power supply that obtains energy from the panel and stores it during the night so that information can still be gathered and sent to the _Monitoring Center_ (for safety purposes, mainly) has been designed. Supercapacitors have been used as energy storage devices instead of the traditional batteries in order to take advantage of their high cycling capability and their fast charge process. Such a power supply can be used by any other sensors that get their energy from PV panels and must still operate during the night.  
Preliminary prototypes demonstrating the feasibility of the system described have been implemented and tested. A MATLAB application has been developed to define the preliminary configuration of the communication network used to collect the data from each PV panel.  
Figure 12: Matlab monitoring environment.  
Future work is related to cost-optimization of the _Smart Modules_ so that the system becomes more attractive to developers of PV plants. Also, and even though authors preferred defining a self-contained system that did not depend on third-party products, a system based on storing information in the cloud might be considered.  
This paper has been supported by the Spanish Government through its National Research Plan by means of project MICINN-10-MAT2010-20601-C02-02.  
All the authors were involved in the research project mentioned in the Acknowledgments. Although all of them contributed to the final result by sharing their ideas and opinions, [PERSON] and [PERSON] developed the software in the system nodes, whereas [PERSON], [PERSON] and [PERSON] worked out the supercapacitor-based power supply.  
The authors declare no conflict of interest.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 2 Gnss Measurements Pre-Processing and Time Synchronization
The following equations show how the carrier and code measurements at time \(t_{j}\) from receiver \(j\) are synchronized to receiver \(i\) at time \(t_{i}\) using Doppler frequency measurements. \[\phi^{k}_{i,f} =r^{k}_{i}-I^{k}_{f}+T^{k}+\left(b_{u,i}-b^{k}_{s}\right)+\lambda^{k }_{f}N^{k}_{i,f}+\xi^{k}_{\phi} \tag{1}\]3 \[\phi^{k}_{i,f} =r^{k}_{i}-I^{k}_{f}+T^{k}+\left(b_{u,i}-b^{k}_{s}\right)+\lambda^{k }_{f}N^{k}_{i,f}+\xi^{k}_{\phi} \tag{1}\]4  
The times \(t_{i}\) and \(t_{j}\) are computed by subtracting each receiver clock bias from the GNSS measurement observation time. In cases where Doppler frequency measurements  
\[\phi^{k}_{i,f} =r^{k}_{i}-I^{k}_{f}+T^{k}+\left(b_{u,i}-b^{k}_{s}\right)+\lambda^{k }_{f}N^{k}_{i,f}+\xi^{k}_{\phi} \tag{1}\]5 Box plot of time differences between two GNSS receivers’ (Ublox ZED-F9P) measurements from the flight test data used in this paper. The box plot graphically displays information on the minimum, first quartile, median, third quartile, and maximum values of the given data. are unavailable, the time-differenced value of the cycle-slip-free carrier phase measurements can be used as an alternative.
# Shadow Hunter: Low-Illumination Object-Detection Algorithm
## 5 Application  
To investigate the real-world performance of our improvement, we captured two sets of authentic low-illumination scene images using Xiaomi 12 and iPhone 11, respectively. We conducted object detection using both the baseline model and our improved model. As depicted in Figure 10, we can clearly observe that our improved model demonstrates superior performance in real-world scenarios, outperforming the baseline model significantly.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**LEA** & **FFN** & **CSDH** & **Bicycle** & **Boot** & **Bottle** & **Bus** & **Car** & **Cat** & **Chair** & **Cup** & **Dog** & **Motorbike** & **People** & **Table** \\ \hline ✓ & & & 80.3 & 55.2 & 53.1 & 77.3 & 74.4 & 54.1 & 54.3 & 59.4 & 61.3 & 70.7 & 70.9 & 49.5 \\  & ✓ & & 80.2 & 56.0 & 52.0 & 77.6 & 73.8 & 53.2 & 54.9 & 59.9 & 60.7 & 70.7 & 71.3 & 49.9 \\  & & ✓ & 80.1 & 55.5 & 52.0 & 77.3 & 73.9 & 53.7 & 54.8 & 60.1 & 61.1 & 70.6 & 71.2 & 49.9 \\ ✓ & & ✓ & 80.4 & 52.4 & 54.2 & 77.1 & 74.3 & 55.1 & 55.3 & 59.2 & 63.2 & 72.2 & 71.9 & 50.1 \\  & ✓ & ✓ & 80.7 & 52.0 & 53.0 & 78.5 & 74.5 & 56.0 & 51.9 & 60.2 & 63.5 & 71.1 & 70.3 & 49.9 \\ ✓ & ✓ & ✓ & 79.6 & 56.2 & 53.3 & 79.7 & 75.0 & 60.7 & 55.5 & 59.6 & 61.3 & 69.4 & 70.0 & 51.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments of different modules on all objects, using the same evaluation mAP0.5/%.  
Figure 10: Using Xiaomi 12 and iPhone 11, we captured two sets of images in low-illumination conditions. Comparing the results between the baseline model and our improved model, we observed a significant increase in accuracy with the improved model during practical usage. Moreover, the improved model successfully detected objects that the baseline model failed to identify.