esa-sceva/satcom-chunk-collection huggingface dataset:
## POSTER Session 2
### GC-MS and LC-QTOF-HRMS for Volatile and Phenolic Analysis of Strawberry Tree Honey from Greece  
M. Xagoraris **1**, P. K. Revelou **1**, E. Savvidaki **2**, E. Lazarou **1**, E. Alissandrakis **2,3,* P. A. Tarantilis **1** and C. S. Pappas **1**  
1 Laboratory of Chemistry, Department of Food Science and Human Nutrition, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece  
2 Laboratory of Quality and Safety of Agricultural Products, Landscape and Environment, Department of Agriculture, Hellenic Mediterranean University, Stavromenos, 71410 Heraklion, Greece  
3 Institute of Agri-Food and Life Sciences Agro-Health, Hellenic Mediterranean University Research Center, Stavromenos, 71410 Heraklion, Greece  
* Correspondence: ealiss@hmu.gr  
Strawberry tree (_Arbutus unedo_ L.) honey is one typical Mediterranean honey with bitter taste unique aroma profile and biological activity. In Greece, strawberry tree honey is usually not harvested, but left for the bees, as it is without much commercial interest. However, in recent years this type of honey has received a notable interest from a specific group of consumers due to its distinct and rare organoleptic characteristics. Additionally, a remarkable economic importance at local markets is reported, with 4 to 8 times higher prices than other honey types [1]. Despite its economic interest, research on the chemical properties of strawberry tree honey is scarce. In the present work, the volatile and phenolic compounds of four samples were studied. Two volatile fractions were received, using isopropanol and hexane, and analyzed by gas chromatography coupled with mass spectrometer (GC-MS). The phenolic compounds were extracted with acetonitrile and analyzed by liquid chromatography combined with time-of-flight high-resolution mass spectrometry (LC/Q-TOF/HRMS). The main volatile compounds, mainly norisoprenoids, which were identified in isopropanol fraction, were 1,3,3-trimethyl-7-oxabicyclo[4.1.0]heptane-2,5-dione (40.84%), 5-(hydroxymethyl)furan-2-carbaldehyde (9.49%), 6-butyl-3-methoxycyclohex-2-en-1-one (3.12%), and 3,5-dihydroxy-6-methyl-2,3-dihydro-4H-pyran-4-one (1.84%). Respectively, the main volatiles of hexane extract were 3-hydroperoxyhexane (3.60%), 2-hydropyhexane (3.52%), 3,4,5-trimethylphenol (2.56%), 3,5,5-trimethylcyclohex-2-en-1-one (2.32%), and (_E_)-3,5,5-trimethyl-4-(3-oxobut-1-en-1-yl)-cyclohex-2-en-1-one (2.17%). In addition, 30 phenolic compounds were identified, with homogenistic acid, abscisic acid, and unedone being the most characteristic. In conclusion, strawberry tree honey has a special chemical composition and organoleptic characteristics, while it presents biological activity. Greek legislation does not clearly define physicochemical and melisospalynological characteristics for this honey variety, so further study is needed towards the definition of its monofloral characteristics.  
This research was financed by the Emblematic Action \"The Honeybee Roads\" of the Greek Public Investments Program (P.I.P.) of General Secretariat for Research & Technology (GSRT) (project code: 2018\(\Sigma\)E01300000).
# Experimental Quantum Message Authentication with Single Qubit Unitary Operation
###### Abstract  
We have developed a quantum message authentication protocol that provides authentication and integrity of an original message using single qubit unitary operations. Our protocol mainly consists of two parts: quantum encryption and a correspondence check. The quantum encryption part is implemented using linear combinations of wave plates, and the correspondence check is performed using Hong-Ou-Mandel interference. By analyzing the coincidence counts of the Hong-Ou-Mandel interference, we have successfully proven the proposed protocol experimentally, and also showed its robustness against an existential forgery.  
quantum message authentication; quantum three-pass protocol; Gao's forgery; swap test +
Footnote †: journal: Applied sciences
# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 4 Present Digital Twin Manipulations in Marine Industry
### Defence & Military Assets  
DT is widely implemented on naval assets such as warships, submarines, maritime aircraft, etc. HIL testing is performed for the Naval Ship Combat Survivability Testbed (NSCST) [82], which is a power-generating system redundancy checking simulation platform in warships of the US Navy. These DT-based simulators will be subjected to Individual Subsystem Actuators Control Tests, Multi Subsystem Integration Tests, Main Supervisory Control Tests and User Control Tests [83]. Further, new naval air assets are demonstrated using their DT, which is used as a tool by prospective contractors to showcase their products to the Naval Air Command [84]. The \"Spiral development project\" at Pearl Harbor and Portsmouth naval shipyards in the USA has optimized vessel trials using a new DT-based approach [85]. New F110 class frigates developed by Navantia, Spain, used DT as an efficient life cycle management tool to address condition monitoring, maintenance, and casualty/damage/fault reaction [86].  
BAE Systems use DT technology in their latest Archerfish Mine Neutralization System [87], which uses an AUV operated in a DT-based architecture to deploy mine-countermeasure action for underwater mooring and sea bottom mines with an unparallel Maritime Domain Awareness. It is further capable of developing a virtual 3D space with real ocean data such as currents, waves, and topography with real-time sensor data, which can lead to value-based reinforced learning followed by autonomous path planning [88]. With the support of DT technology, Maritime Domain Awareness (MDA), mission readiness, Fleet Management and sustainment of a single Naval Force or even multinational task forces can be easily strengthened and augmented.
## 2 Materials and Methods
### Combination of Spectral and Textural Data  
Data fusion is a process dealing with data from two or more data sources to achieve improved information by using a certain algorithm. This process can be performed at three different processing levels: pixel level fusion, feature level fusion, and decision level fusion [41]. Pixel level fusion is the lowest processing level that merges the original data directly, but this method requires a large amount of calculations; feature level fusion requires the extraction of feature variables recognized from multiple data sources and fuses for further assessment using statistical approaches; decision level fusion is a method that integrates the extracted data by applying predefined decision rules [22; 41]. To avoid immense data calculation and potential information losses, feature level fusion was applied to integrate spectral and textural data in this study. The vector of combined features from each sample was built by placing the extracted spectral data and texture data consecutively one after another. A potential problem with this preprocessing method is that parameters with large values may have the hidden trouble of reducing the predicting power of smaller value parameters, which may be very important for the measured samples. Therefore, a classical normalization was carried out to overcome this drawback of feature-level data fusion by using the following equation:  
\[Y_{Ni}=Y_{i}/\overline{Y} \tag{10}\]  
where \(Y_{Ni}\) is the normalized parameter for sample i, \(Y_{i}\) is the original parameter for sample i, and \(\overline{Y}\) is the mean value. Data fusion treated by mean normalization was used to develop classification models.
## 4 Comprehensive Performance Evaluation
### Outdoor Environment
#### 4.2.1 Localization  
Figure 9 displays the ground truth of the robot poses obtained from one of the outdoor datasets (O4). Other datasets start at slightly different locations but have similar journeys. The results of the metrics absolute trajectory error (with SE(3) Umeyama alignment) are shown in Table 2. In outdoor environments, SC-LeGO-LOAM demonstrates better positioning accuracy, namely lower errors highlighted in bold font, particularly in scenarios with richer textures compared to indoor environments. Moreover, SC-LeGO-LOAM also demonstrates smaller standard deviation, indicating higher stability for localization.  
As shown in Figure 10, we conduct a comparison between RPE in an outdoor environment (O4), revealing the discernible impact of loopback on ORB-SLAM3 and SC-LeGO-LOAM, respectively. This further underscores the beneficial impact of loop closure detection in error elimination. However, unlike the indoor environment discussed in the preceding section, the outdoor environment possesses richer texture information. Consequently, the role of loop closure in LiDAR SLAM is mitigated and the operator's influence on the SLAM results is not as substantial.
## 2 Materials and Methods
### Protocols
#### 2.3.2 Reliability of Human Pulse Wave Measurements at Standard Conditions  
The reliability of human DVP measurements was assessed by measuring test-retest variability by performing consecutive measurements on healthy individuals under standardized conditions, in which physiological fluctuations of cardiovascular functioning are supposed to be minimal. We performed 10 repeated 2-min-long measurements on 10 young healthy volunteers (M/F: 5/5, Age: 19-35, Mean \(\pm\) SD: \(25.3\pm 4.3\)) under standard conditions. The course of successive measurements took approximately 30 min. We defined'standard condition' as the set of measurement conditions which we recommend our users to maintain when they perform their daily morning measurements during follow-up. The criteria for standard conditions are as follows: measurement takes place in a quiet room at room temperature; in the morning hours at least two hours after the last meal and coffee; and in a sitting, resting position, with hands held quietly on a table. Moreover, consumption of energy drinks and alcoholic beverages and intensive physical activity on the day of the measurement were avoided in this study. For these measurements, the pulse oximeter was placed on the left index finger.
# Durability Investigation of Fiber-Reinforced Functionally Graded Concretes in Cold Regions
## 2 Test Methods and Proportioning Model
### Freezing Resistance Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Test Items**} & \multicolumn{5}{c}{**Number of Freeze–Thaw Cycles**} \\ \cline{3-6}  & & **0** & **100** & **200** & **300** & **400** \\ \hline \multirow{2}{*}{\#1} & Relative dynamic modulus of elasticity/\% & 100 & 93.4 & 82.3 & 61.4 & - \\  & Quality loss rate/\% & 0 & 0.7 & 1.5 & 3.4 & - \\ \hline \multirow{2}{*}{\#2} & Relative dynamic modulus of elasticity/\% & 100 & 94.1 & 85.3 & 71.6 & - \\  & Quality loss rate/\% & 0 & 0.5 & 1.2 & 2.3 & - \\ \hline \multirow{2}{*}{\#3} & Relative dynamic modulus of elasticity/\% & 100 & 94.5 & 86.2 & 78.6 & - \\  & Quality loss rate/\% & 0 & 0.4 & 1.1 & 2.2 & - \\ \hline \multirow{2}{*}{\#4} & Relative dynamic modulus of elasticity/\% & 100 & 94.3 & 89.2 & 78.4 & 70.8 \\  & Quality loss rate/\% & 0 & 0.5 & 1.3 & 2.1 & 2.9 \\ \hline \multirow{2}{*}{\#5} & Relative dynamic modulus of elasticity/\% & 100 & 96.2 & 90.1 & 81.6 & 76.8 \\  & Quality loss rate/\% & 0 & 0.4 & 1.2 & 1.9 & 2.6 \\ \hline \multirow{2}{*}{\#6} & Relative dynamic modulus of elasticity/\% & 100 & 96.7 & 91.0 & 85.2 & 81.6 \\  & Quality loss rate/\% & 0 & 0.4 & 1.1 & 1.7 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Concrete frost resistance test results. Figure 8: Relationship between the dynamic elastic modulus of concrete and the number of freeze–thaw cycles. #3, #5, and #6 was smaller than that of #1 and #4 under the same number of freeze-thaw cycles. Since concretes #2 and #5 include the composite anti-freezing and shrinkage-reducing polycarboxylic acid water reducer, the air-entraining component can introduce tiny, uniformly distributed pores, improving the frost resistance of each strength grade of concrete [31]. In addition, the monofilament polypropylene and imitation steel fibers incorporated in #3 and #6 improved the microporous structure to a certain extent, making the concrete denser and, thus, improving the frost resistance.
# Non-Cooperative Target Ranging Based on High-Orbit Single-Star Temporal-Spatial Characteristics
## 5 Conclusions  
No prior model is available for distance measurement in the continuous tracking process of high-orbit non-cooperative targets. Additionally, the real-time computational resources on the satellite hardware platform are limited. This necessitates the design of a simple yet effective computational model.  
This model assumes that the high-orbit satellite is relatively stationary with a low eccentricity and minimal orbital inclination. The designed model was verified using both simulation validation and actual in-orbit testing. Within an inclination range of 0.0\({}^{\circ}\) to 0.4\({}^{\circ}\), the ranging accuracy within a 600 km range surpasses the detection distance by 5%, whereas within an inclination range of 0.0\({}^{\circ}\) to 1.0\({}^{\circ}\), the ranging accuracy within a 100 km range is guaranteed to exceed the detection distance by 5%.  
Methodology, D.Z.; software, D.Z.; validation, Q.Z.; formal analysis, D.Z.; investigation, H.W.; resources, H.W.; data curation, D.Z.; writing--original draft preparation, D.Z.; writing--review and editing, Q.Z.; visualization, Q.Z.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
We are grateful for the support of the Shaanxi Province Key Research and Development Plan.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Two-Stage Dimensionality Reduction for Social Media Engagement Classification
## 3 Proposed Algorithm  
This section details the proposed method, breaking it down into two stages of dimensionality reduction, hereinafter referred to as TSDR-1 and TSDR-2. Additionally, an optional discriminator for classification is also described. Figure 1 illustrates how the proposed algorithm may be used.  
To conduct a comprehensive benchmark of the proposed method, we compare the performance indicators of classification methods applied to the original datasets with those obtained from reduced datasets generated through TSDR. Consider a dataset with \(N\) features and \(L\) labels. Initially, the dataset undergoes classification using conventional methods. Subsequently, the same dataset undergoes TSDR-1, where it is reduced to \(d\) dimensions, which is an adjustable parameter optimized for maximum method accuracy. This reduced subset of \(d\) dimensions then undergoes TSDR-2, employing multiobjective optimization to transform it into a final subset of \(L\) dimensions, equivalent to the number of classes in the original dataset. The ultimate reduced subset is not only classified using conventional methods, but also with the proposed TSDR discriminator embedded in the process.  
Figure 1: Proposed algorithm breakdown.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 4 Experiments
### Experimentation Scenarios
#### 4.2.2 Scenario 2: IoT Sensors from Smart Santander Transmitting to Existing TestBeds
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sensors/Senders**} & \multirow{2}{*}{**SensorId**} & \multirow{2}{*}{**Receivers**} & \multirow{2}{*}{**Distance** **(in km)**} & \multirow{2}{*}{**Start\_Time**} & \multirow{2}{*}{**Start\_Time** **(h:min)**} & \multirow{2}{*}{**Last\_Time**} & \multirow{2}{*}{**Sim\_Time** (h)} \\ \hline Sensor[0] & 9 & MCC[20] & 840.84 & 2 & 1:00 & 23:55 \\ \hline Sensor[1] & 24 & MCC[9] & 2232.52 & 1 & 0:59 & 23:59 \\ \hline Sensor[2] & 53 & MCC[14] & 947.06 & 2 & 1:00 & 23:5 \\ \hline Sensor[3] & 90 & MCC[17] & 1029.30 & 2 & 1:00 & 23:54 \\ \hline Sensor[4] & 110 & MCC[13] & 543.94 & 0 & 0:58 & 23:56 \\ \hline Sensor[5] & 213 & MCC[5] & 5320.82 & 506 & 9:24 & 17:57 \\ \hline Sensor[6] & 146 & MCC[18] & 935.35 & 714 & 12:52 & 23:32 \\ \hline Sensor[7] & 19 & MCC[21] & 1196.81 & 242 & 5:00 & 18:42 \\ \hline Sensor[8] & 1 & MCC[22] & 1794.49 & 5 & 1:03 & 17:41 \\ \hline Sensor[9] & 135 & MCC[24] & 874.42 & 358 & 6:58 & 23:58 \\ \hline \end{tabular}
\end{table} 
Table 5: Characteristics of sensors and simulation variables for Scenario 2. Furthermore, the number of the distinct ground stations was increased to 26 by incorporating MCCs that mirror certain real testbeds located in Europe. Their names and coordinates (longitude and latitude) are outlined in Table 6, while the actual distances between the sending Sensor and its corresponding MCC destination are presented in Table 5. We should note that MCC[5] and MCC[9] do not correspond to actual testbeds but are included for experimental purposes.
## 4 The Quadrature Chaos Shift Keying Communication System's Performance Analysis
### Experimental Study  
This study aims to assess the noise immunity of the prototype. The investigation involves examining the QCSK data transmission system prototype within an emulated AWGN channel, accomplished by incorporating an attenuator in the transmission path. Following the attenuation, the impact of noises on the QCSK communication system's  
\begin{table}
\begin{tabular}{c c c c} \hline \({}^{\prime}\)**’, \(X\)** & \({}^{\prime}\)**’, \(Y\)** & **Synchronization Signal, \(Z\)** & **Cross-Correlation Coefficient, \(X\) and \(Y\)** \\ \hline \(V_{C2}\) & \(V_{RL}\) & \(V_{C1}\) & \(0.11\) \\ \(V_{C1}\) & \(V_{RL}\) & \(V_{C2}\) & \(-0.49\) \\ \hline \end{tabular}
\end{table}
Table 1: State Variables and Cross-Correlation Coefficient Between Information-Forming Signals.  
Figure 10: QCSK communication system simulation block-scheme, where \(b(t)\) is information bit stream, \(t_{end}\) is length of the drive chaos oscillator simulation, \(X(t)\), \(Y(t)\), and \(Z(t)\) are drive chaos oscillator state variables, \(Z^{\prime}(t)\) is synchronization signal restored after signal processing in receiver, \(X^{\prime}(t)\) and \(Y^{\prime}(t)\) are state variables generated in response chaos oscillator, and \(b^{\prime}(t)\) is detected information stream.  
performance within the receiver unit became noticeable, prompting a thorough analysis of the QCSK data transmission system's noise immunity. The hardware study methodology scheme is shown in Figure 11.  
The measurements were conducted using Analog Discovery Pro (ADPro), which serves the dual function of generating the information signal and capturing the information-carrying signal, and both signals from the response chaos oscillator correspond to bits '1' and '0'. Controlling the ADPro was achieved through Python code from a connected computer. The process of detecting the information signal was executed using custom MATLAB code. Subsequently, BER values were obtained from these experiments.
## 7 Applications of HAR in Healthcare
### Early Detection of Diseases  
HAR, especially combined with physiological data, can be used for the early detection of diseases. For instance, peripheral neuropathy can be detected early using gait analysis. Diabetic patients who are at risk for peripheral neuropathy can be monitored using the HAR system with smart shoe sensors or other wearable devices.  
Biomedical sensors such as ECG and EMG sensors can be used together with inertial sensors to detect and monitor diseases, which can be helpful for caregivers and help patients receive prompt help where necessary. This can also be applied in the detection of strokes, pneumoconiosis and other diseases [118; 119]. In the same vein, heart attacks can be detected based on the patient's health vitals and motions detected by a HAR system. Healthcare practitioners can use a HAR-based system to detect and monitor some of these diseases especially for people who are generally at risk due to genetics, age, underlying health conditions, or lifestyle.  
Most biomedical sensors like ECG sensors use probes or straps which are uncomfortable to wear, especially for everyday tasks. Although there has been progress in the development of wearable biomedical sensors such as over-the-ear ECG sensors, there is still a concern about the accuracy and adoption of these wearable biomedical sensors. There are also limited datasets with these sensors due to the rarity of their use in HAR. More research needs to be performed in this application area to fully realise the potential of HAR in the early detection of diseases.
## 4 Experimental Evaluation
### An Obstruction Situation  
The studies mentioned above take into account the scenario in which the target space contains no obstacles; however, this is rarely the case in real life. In this research, we present a sensor coverage approach using one corridor of Daxing Airport Terminal as an example, which is a big target space containing obstacles.  
Daxing Airport Terminal is the second largest airport terminal in the world, with a total floor area of 1.03 million square meters. And its architecture is unique, offering travelers wide and comfortable waiting spaces as well as business and service facilities. However, the area is densely populated and the passenger flow is large, which brings huge pressure to security work. Therefore, it is of great significance to optimize camera coverage in this area.  
Figure 8 shows the optimization effect of camera coverage in the corridor. In this experiment, the sampling interval is 15 m, and the green area represents the coverage of the camera, where the \(dof\) of the camera is 60 m and the \(fov\) is 37\({}^{\circ}\). By adjusting the layout of the camera, full coverage of the target space with obstacles can be achieved. We can adjust the sampling interval and the \(dof\) and \(fov\) of the camera according to the actual needs. We will conduct a more thorough investigation on this topic in the future; this is merely an overview of the situation with obstacles.  
Figure 8: The optimization effect of camera coverage in one corridor of Daxing Airport Terminal. The various white irregular boxes in the figure represent the business and office area and other public facilities. The five-pointed stars in the figure represent sampling points. Solid black dots represent cameras, and the green sector represents the coverage area of the cameras.  
Figure 7: Experimental results for a comparison of the different sampling parameters. (**a**): The influence of sensor position sample interval \(\Delta_{b}\) on the results; (**b**) the influence of the sampling number of sensor azimuth angle \(s_{\varphi}\) on the results.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 5 Experimental Study
### Impact of Factors
#### 5.4.3 Sensing Frequency  
Another impact factor of the event detection is the sensing frequency of nodes. In Figures 10-12, we varied the sensing interval to study its impact on the recall rate, message transmissions and the length of detection time. When the sensing frequency increases, or the interval of sensing decreases, more sensing data are generated and uploaded to the RSUs for further processing, so the recall rate and the message transmission grow (Figures 10 and 11), and the average detection time decreases (Figure 12) for all the schemes. TPEG has a similar recall rate as PROPHET and ESSMD, yet has a much lower cost of message transmissions. For all the schemes except NAIVE, the recall grows from 0.82 to 0.97 when the interval of sensing decreases from 100\(\sim\)120 s to 5\(\sim\)10 s. For the PROPHET and ESSMD scheme, the message transmissions grow about 1.96\(\sim\)2.34-times more, while the TPEG only increases about 69 percent, from \(291\times 10^{4}\) to \(492\times 10^{4}\). This is largely due to the low cost of LCS monitoring mode in the TPEG scheme. The proposed scheme scales well with the sensing frequency.  
Figure 11: Impact of sensing interval on the number of message transmissions.  
Figure 10: Impact of sensing interval on the recall rate.