esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 3 Results
### Comparison of the Scanners  
Table 4 shows the mean displacements derived from the ICP transformation matrices for the three profiles and individual axes (\(\Delta\)X, \(\Delta\)Y, \(\Delta\)Z), along with the absolute value of the maximum observed displacement (\(\Delta_{\text{MAX}}\)), the axis of the maximum displacement, and the  
Figure 14: Detail of the central profile after transformations to spherical targets and ICP transformation for (**a**) Leica RTC 360; and (**b**) GeoSLAM ZEB Horizon RT.  
Figure 13: The RMSEs of the FARO Orbis (**a**) after transformation to spherical targets (RMSE\({}_{\text{OVERALL}}\) = 8.2 mm); and (**b**) after ICP transformation (RMSE\({}_{\text{ICP-ALL}}\) = 6.0 mm).  
mean local accuracy of the cloud. The values always show means from all three profiles (and, in the case of SLAM scanners, also from all three measurements).  
In all scanners, the highest displacement (error) was observed along the transverse (X) axis, followed by the vertical (Z) axis, and the least errors were observed in the longitudinal axis (Y). The GeoSLAM ZEB Horizon RT was the only exception, as the longitudinal error of its point cloud was greater than the height error. The best results were achieved by the Leica RTC 360 scanner, the maximum error of which in any of the coordinate axes never exceeded 1.2 mm (approx. 1% of the maximum error of the GeoSLAM ZEB Horizon RT approaching 10 cm).  
With the exception of Emesent Hovermap ST-X, where the global and local error differed by only 0.3 mm (compare Tables 4 and 5), which indicates negligible systematic error and evenly distributed random error, the local accuracies of the other scanners (Table 4, RMSE\({}_{\text{ICP-PROFILE}}\)) were better than global ones. The local accuracies also obviated the technological development of the scanners, especially in the SLAM scanners, where the newer generation (Emesent Hovermap ST-X and FARO Orbis) outperformed the older one (GeoSLAM ZEB Horizon RT).
## 3 Case Studies
### Case Study 1: Monitoring Scenario (Social Distancing)  
The first case study refers to a monitoring scenario. In particular, the IoT system is envisioned as a tool to control the spatiotemporal distribution of the people in a given area and to trigger, automatically, the overcrowding alarm that allows one to respect social distancing. Figure 6 shows two examples of a crowd management plan, where the overcrowded areas are highlighted in red, while white signals indicate which sensing node detected the overcrowding alarm.  
Figure 5: (**a**) IoT system dashboard; (**b**) Emergency plan provided by the NFC module-smartphone connection in the form of a smartphone compatible map.
## 3 Results and Discussion
### Statistics of Measured Drip Loss Values  
The min, max, mean, and standard deviation (SD) of drip loss (DL) values for high-WHC and low-WHC samples, after the elimination of outlier data, are shown in Table 1. For samples with high WHC, values of DL ranged from 0.03 to 0.94 with a mean value of 0.57 and the SD of 0.22. For samples with low WHC, the DL values were as low as 0.97 and as high as 5.90 with mean and SD values of 2.22 and 1.16, respectively. These results showed that there were wide variations of DL values between high-WHC and low-WHC samples, which are important for building robust and accurate models [11].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Min** & **Max** & **Mean** & **SD** \\ \hline High WHC & 0.03 & 0.94 & 0.57 & 0.22 \\ Low WHC & 0.97 & 5.90 & 2.22 & 1.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured drip loss values of chicken breast fillets.  
Figure 2: Spectral features of chicken breast fillets: (**a**) mean original spectra of high-WHC and low-WHC samples, (**b**) scatter plot of PCA with confidence ellipses (95% confidence level) for samples of high WHC and low WHC.
# Cell-Based Target Localization and Tracking with an Active Camera
## 3 Methodology  
The proposed method mainly includes four parts. Firstly, the scene is divided into multiple cells that were calibrated; secondly, YOLOv4 is used to detect the image coordinates of the target and the cells' vertices. Then, the cells' vertices in the image are matched by the proposed matching method; thirdly, the precise world coordinates of the target are calculated by the proposed method; finally, the world coordinates are transformed into angles of the PTZ camera, to track and observe the target. The overall flow chart of the system is shown in Figure 1. The specific method is to change the \(P\) value and \(T\) value of the PTZ camera continuously to ensure that the moving target is always in the center of the FOV (field of view).
_applied sciences_  
_Article_  
**Comparative Study of Raw and Dehydrated _Boletus edulis_**  
**Mushrooms by Hot Air and Centrifugal Vacuum Processes: Functional Properties and Fatty Acid and Aroma Profiles**  
**[PERSON] 1,*\({}^{\copyright}\)**, [PERSON] 1\({}^{\copyright}\)**, [PERSON] 2\({}^{\copyright}\)** and [PERSON] 3  
1 Department of Agricultural Sciences and Food Engineering \"[PERSON]\" University of Sibiu,  
7-9 Dr. [PERSON], 550024 Sibiu, Romania  
2 Department of Food Science, University of Agricultural Sciences and Veterinary Medicine,  
3-5 Mansufur Street, 400372 Cluj-Napoca, Romania  
3 Department of Environmental and Plant Protection, University of Agricultural Sciences and Veterinary Medicine,  
3-5 Mansufur Street, 400372 Cluj-Napoca, Romania  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +40-2-6923-6441  
**Abstract** A study on _Boletus edulis_ mushrooms subjected to either hot air drying (HAD) or centrifugal vacuum drying (CVD) was performed to evaluate and compare their functional properties, fatty acids, and aroma compounds. Better flowability and a higher rehydration ratio were observed for HAD powders, while enhanced indices of water solubility, emulsifying activity, and stability were noticed for CVD ones. The composition of 21 identified fatty acids varied between raw and dried samples, the most relevant being the decrease of oleic acid and the increase of linoleic acid during drying. The PUFAs/SFAs ratio was >3.3 in all samples, thus meeting the requirements for healthy lipids. Of the 15 aroma compounds identified in raw mushrooms, only hexanal, 1-octen-3-ol, and (Z)-2-octen-l-ol were also found in dried samples, to different extents; 1-octen-3-ol was the major volatile constituent in all samples. Low amounts of new alcohols, aldehydes and ketones, D-limonene, and carophyllene were detected in HAD powders, while dimethyl disulfide and 2-_n_-pentyl-furan were detected in CVD ones. The drying of mushrooms resulted in a total loss of 2-methyl-2-butenal, (E)-2-octenal, and 1-octen-3-one. These findings become important milestones for food manufacturers and researchers in selecting the desired drying technique of _B. edulis_ based on the powder/emulsifying properties and preservation of fatty acids and aroma molecules.  
_Boletus edulis_; hot air drying; centrifugal vacuum drying; powder; emulsifying properties; fatty acids; aroma compounds
## 2 Methods
### Preprocessing Methods for ECG Signals  
The Pan-Tompkins algorithm is used to detect QRS complexes in ECG signals. The Pan-Tompkins algorithm consists of a series of steps designed to effectively detect QRS complexes in ECG signals. Each step aims to enhance the quality of the ECG signal and accurately identify QRS complexes. It starts with low-pass filtering to reduce noise and high-pass filtering to remove baseline wander and low-frequency noise. Differentiation is then used to highlight the rapid rise and fall of the QRS complex, improving peak detection. A moving average filter is applied to the squared signal to further clarify the QRS locations and reduce noise. Subsequently, QRS locations are detected based on a specific threshold. Finally, verification is performed based on the positions and intervals of the QRS complexes to minimize false detections [44].  
The Savitzky-Golay Filter (SGF) [45] is used to suppress high-frequency noise in ECG signals. It generalizes the concept of moving average filters by fitting an \(n\)th-order polynomial to the signal values within a window using least-squares and then using the value of the fitted polynomial at the central point of the window as the new smoothed data point.  
The Baseline Wandering Path Finding (BWPF) algorithm [46] is used to remove baseline wandering. It uses piecewise polynomial fitting. The ECG signal is divided into multiple segments. The number of segments is specified by the user. The polynomial fitting order is self-assessed based on a threshold value. The algorithm starts with order 1, fits the ECG signal, and computes the fitting error compared to the threshold. If the error exceeds the threshold, the process is repeated with an incremented order until the error is within the acceptable range.
# Use of Social Media Data in Disaster Management: A Survey
## 4 Data Sources for Social Media Data Analysis
### Social Media Users  
Messages originating from different accounts on social media have different qualities and trustworthiness [20]. For instance, official accounts used by government agencies are likely to have more trustworthiness than public users with personal accounts. However, although government agencies that are responsible for the management of disasters use social media to disseminate disaster-related information, they still play a limited role in the communities. Instead, it is the public users that play a significant role in contributing to information networks during disaster events. The authors of [21] showed different distributions of Twitter users participating in various disaster events, with public users having a clearly greater percentage of participation. To summarise, different types of social media users play different roles in disaster management, each providing different context, quality, and trustworthiness of social media data. In this paper, we classify types of social media users as government authorities, research/academic institutions, non-governmental organisations (NGO), and the public, as shown in Figure 2.  
* Government Authority--refers to government organisations involved in disaster response and support. These organisations are authorised to: (i) disseminate official announcement and actionable warning information to people in a disaster risk area, e.g., National Disaster Management Authority (NDMA), and (ii) provide supporting information for disaster management, e.g., Geological Survey of India (GSI), British Geological Survey (BGS), and national meteorological offices.
* Research/Academic Institution--refers to institutions or research groups who are conducting research on disaster management.
* Non-Governmental Organisation (NGO)--refers to private-sector organisations that are disseminating disaster-related information on social media. This user type contributes a greater percentage of information than the government authorities and provides a higher quality of information compared to the information provided by individual users. Examples of NGOs include Save the Hills, Cable News Network (CNN), and Asian News International (ANI).  
Figure 2: Social media users.  
* Public--refers to individual users with personal social media accounts. This user type makes the greatest contribution to social media by sharing disaster-related information. With a huge number of users in this category, it constitutes the greatest percentage in information networks compared to other types of users. Most research [22; 23; 24; 25; 26; 27] relies on information contributed by public users, even though the information may be of uncertain quality and trustworthiness. As a consequence, social media data preparation techniques (e.g., data filtering, data classification, and data extraction) to improve data quality and enhance the accuracy of social media data analysis prove challenging.
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
## 4 Performance Evaluation
### Results and Discussions
#### 4.2.1 URLLC Slice  
Figure 11 shows the average packet loss ratio versus time for the URLLC slice. Figure 11a-c display the results for 100, 250, and 500 mMTC devices, respectively. The first flow path (i.e., S1-S3-S5-S6) in the URLLC slice with 5 Mbps of initial URLLC traffic becomes overloaded at 3 s when 17 Mbps of URLLC traffic are transmitted through the flow path. Figure 11 reveals that the average packet loss ratios with SDNPS (blue curves) always reduce to near zero more quickly after the third second than with the conventional SDN with either static (grey curves) or dynamic routing (orange curves), regardless of the total load of mMTC devices. This result occurs because with SDNPS, the INT data for each switch are carried by a data packet every 0.5 s, extracted at the egress switch, and sent to the network slice controller, enabling a rapid response to network congestion. The NS controller thus quickly decides to reroute the 17 Mbps URLLC flow to the second path (i.e., S1-S3-S4-S6) in the URLLC slice. By contrast, for the conventional SDN with dynamic routing, updating the forwarding rules for the switches in the data plane requires substantial information exchange between the SDN controller and every OpenFlow switch; thus, rerouting requires more time. For the conventional SDN with static routing, the average packet loss ratio is increased continuously after the third second because the forwarding rules do not change in the static routing unless the current path fails.  
Figure 11: _Cont._  
Figures 12-14 show the average throughput, packet delay, and packet loss ratio in the URLLC slice for 100, 250, and 500 mMTC devices. Because URLLC and mMTC are differentiated by NS, the number of mMTC devices does not affect the performance in any of the scenarios. Thus, the behavior of the mMTC slice does not affect that of the URLLC slice because the network slices are inherently independent of each other. However, in all scenarios, SDNPS exhibits considerably better performance in all metrics than do the other two schemes.  
Figure 11: Average packet loss ratio versus time in the URLLC slice for (**a**) 100; (**b**) 250; (**c**) 500 mMTC devices.  
Figure 12: Average throughput in the URLLC slice.  
Figure 13: Average packet delay in the URLLC slice.
## 3 Results and Discussion
### Sensory Properties  
Table 6 shows the effect of MOLP on the sensory analysis of raw ground beef stored at 4 \(\pm\) 1 \({}^{\circ}\)C for 5 days and cooked in the oven for 30 min at 130 \({}^{\circ}\)C. Sensory properties such as colour, taste, springiness, and overall acceptability significantly decreased with the inclusion of MOLP, except for tenderness and juiciness. Similar results of decrease in sensory values of low-fat ground pork patties treated with carrageenan were reported by [PERSON] and [PERSON] [78] and were attributed to the decrease in moisture loss and surface dehydration during storage. The increase in moisture retention of the treated ground beef during cooking might be attributed to higher tenderness and juiciness. However, the control sample received a higher overall acceptability score, although there was no significant difference (\(p>0.05\)) between samples F1, F2, and the control sample in all  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Storage Days**} \\ \hline
**Cooking Properties** & **Formulations (\%)** & **0** & **5** & **10** & **15** \\ \hline Cooking yield (\%) & F0 & 51.56 \(\pm\) 1.83 \({}^{\text{\text{\text{\text{\text{\text{\text{\textasci  
sensory attributes. This is attributed to the inclusion of only a small amount of MOLP (0.2 and 0.4%).  
Low values of colour scores in treated ground beef may be associated with the green colour of MOLP, which arises from its chlorophyll content. The significantly lower taste acceptability of treated ground beef (F3 and F4) samples may be attributed to the bitter taste of MOLP. Bitterness in MOLP is due to the presence of the glucosinolate-myrosinase system, which is responsible for the bitter tastes in Brussels sprouts, kale, and collard greens (2019). Glucomorringrin and glucagonoinain have been identified as the principal glucosinolates in MOLP, and therefore the activity and specificity of myrosinase is responsible for the bitterness (2019). The results suggest that the bitter taste is retained with the increase in MOLP concentrations. Moreover, inclusion of MOLP in ground beef resulted in unfamiliar odours. Our findings are similar to those reported by [PERSON] et al. (2018), whereby consumers preferred the appearance, colour, odour, and taste of control sample chicken sausages added with 0.04% Butylated hydroxytoluene and 0.25% or 0.50 MOLP, with concentrations above 0.50% negatively affecting the sensory attributes.
## 3 Brief Introduction to Polaris  
Polaris is the product of an industry-academia partnership between Dragon Prince Hydro-Survey Enterprise Co. Kaohsiung City 800, Taiwan and National Kaohsiung University of Science and Technology (Nanzi Campus), and it is Taiwan's first nongovernmental oceanographic research and survey vessel. It was built by Shing Sheng Fa Boat Building Co, Ltd. Kaohsiung City 80544, Taiwan. It weighs 260 tons has a top speed of 11 knots, and is 36.98 m long by 6.80 m wide. Since 2008, Polaris has been stationed at the Innovation Incubation Center of National Kaohsiung University of Science and Technology, and it is typically parked at the jetty of the Cijin Campus. Polaris has completed many underwater exploration and rescue operations. For example, various parties were involved in the search for Malaysia Airlines flight MH370, which mysteriously disappeared on 8 March 2014. The Indian Ocean is approximately 3900 m deep on average, and approximately 8000 m deep at its deepest. The search operations required advanced equipment, and Polaris was invited to Australia to use its underwater detection and deep tow system to help in salvation operations. This demonstrated the international recognition of the high-quality system on Polaris. Photographs and diagrams of the vessel are presented in Figure 8a-c.  
Polaris is equipped with a differential satellite positioning system, underwater remotely operated vehicle, single-beam and multibeam echo sounders, subbottom profiler, side-scan sonar system, underwater positioning system, sediment corer, marine magnetometer, and penetrometer.
# Development of Potato Mass Estimation System Based on Deep Learning
## 3 Results and Discussion
### Performance Analysis Result of Potato Mass Estimation System  
The result of comparing the actual and estimated values of potatoes for the performance evaluation of the potato mass estimation system developed in the present study is shown in Figure 12. As a result of comparing the actual mass and the estimated mass of 200 potatoes, R\({}^{2}\), which represents the explanatory power of the model applied in the system, was found to be 0.90. The RMSE (root mean square error), which is one of the indicators that show the mass error of each object, was analyzed to be 26.59 g. The errors in the individual potato mass were found to be 13.09% on average, 0.03% at minimum, and 55.43% at maximum.  
The result of putting in potatoes at a speed of three potatoes per second, taking into account the actual harvesting speed, is presented in Table 5. The total weight of the potatoes actually put in was 60.20 kg, 380.66 potatoes were detected on average as a result of making three measurements, and the error in the total mass estimation was found to be 9.0%. The reason why the detection rate was not 100% when three potatoes were put in per second was presumed to be because several potatoes were detected by the system at the same time, and an instantaneous frame drop occurred due to an increase in the computation load as a result of the calculation. To solve this phenomenon, the computation performance needs to be enhanced by improving the performance of the computer currently in use, and the location of the C.L. at the bottom needs to be readjusted to widen the detection area, thus preventing the frame drop phenomenon.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variety**} & \multirow{2}{*}{**RMSD (g)**} & \multicolumn{3}{c}{**Error (\%)**} \\ \cline{3-5}  & & **Max.** & **Min.** & **Ave.** \\ \hline Superior & 23.36 & 47.16 & 0.001 & 14.90 \\ Atlantic & 20.53 & 48.07 & 0.065 & 14.23 \\ Jopung & 20.48 & 51.22 & 0.104 & 12.67 \\ Mixed & 21.92 & 49.14 & 0.018 & 14.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Deviation and errors between actual and estimated mass of each potato variety.  
Figure 12: Results of linearity (**a**) and cumulative mass (**b**) of the potato mass estimation system.
## 2 Materials and Methods
### System Description
#### 2.2.1 IEEE Agent
For this purpose, we used the 64 Mb mobile-SDRAM memory (MT4 RBM16 LF by Micron). We checked the HR value to store the data; if it was non-zero, we stored it, as well as the lead status, the timestamp, the previous five R-R intervals, and the ECG sample related to the lead II. Based on the analysis of these data, the system decides if constants are out of range and an alarm must be generated. Given this situation, and given that devices are not already paired, the communication is established in a similar way as in the case of the SpO2 system, except that the DSP has to implement the state machine for the connection for the \begin{table}
\begin{tabular}{c c} \hline \hline
**Command** & **Description** \\ \hline AGENT UPDATE & Oxygen saturation is 97\% \\ FFFF 1 A4C 97E0 & Time stamp of the measurement \\ AGENT UPDATE & Pulse rate is 74 bpm \\ FFFF A 4C 74E0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of iWrap syntax to send pulse oximeter data. ECG. In this case, two data channels are opened, one reliable (for the main information) and one streaming (for the waveform samples). At that time, the system reads data from the SDRAM (from 5 min before the alarm if it is produced later than 5 min, or from the start of the monitoring session if it is produced before a 5 min monitoring session). The system reads all the values between two stored HR values. Then, it combines the data packet to accomplish the 11073-10406 standard. The system implements the object classes with mandatory and recommended attributes included in the specialization (Figure 5). One data packet is sent with HR, lead-off status, five R-R intervals in ms, and the timestamp through the reliable data channel. After that, packets with 20 samples from lead II are sent through the streaming data channel until there are 20 or fewer samples to be sent. When this happens, the DSP reads from the SDRAM as before and sends a new data packet. In the same way as in the SpO2 system, data are sent through the UART (similarly configured). While the DSP is sending packets, it is acquiring and storing new data.