esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Fiber Selection  
First, the fiber coating was evaluated in this study because it is one of the most important factors in determining the quality and quantity of detected volatile compounds. Four different types of SPME fibers (PDMS, CAR/PDMS, PDMS/DVB, DVB/CAR/PDMS) were tested using the same sample and analysis conditions. The largest number of identifiable peaks and mass spectrometry (MS) peak areas were used to evaluate the performance of SPME fibers. As shown in Figure 1, it is evident that different fiber coatings have significant effects on the extraction of volatile compounds from sweet potato. Both DVB/CAR/PDMS and PDMS/DVB enabled the detection of a wider range of compounds and produced a higher peak intensity than the other two fibers. PDMS provided the highest responses of large molecular weight compounds captured after 30 min and CAR/PDMS absorbed the largest number of low molecular weight compounds captured before 10 min. In total, 56, 49, 44, and 29 volatile compounds, and 8.75 \(\times\) 10\({}^{7}\), 8.26 \(\times\) 10\({}^{7}\), 3.66 \(\times\) 10\({}^{7}\), and 6.23 \(\times\) 10\({}^{7}\) total chromatographic areas were identified by DVB/CAR/PDMS, PDMS/DVB, PDMS, and CAR/PDMS fibers, respectively. Since the PDMS and CAR/PDMS fibers obtained less volatile compounds and MS peak area, they were not considered for further steps. Though PDMS/DVB fibers captured a similar number of volatiles and total chromatographic area as those of the DVB/CAR/PDMS fiber, more undesired high intensity impurity peaks and lower match factors were detected using PDMS/DVB than the DVB/CAR/PDMS fibers. Thus, the DVB/CAR/PDMS fiber was selected as the best fiber coating for further analysis.  
These results are consistent with the characteristics of fibers. PDMS fiber is a nonpolar fiber designed for volatiles and semi-volatiles with large molecules and CAR/PDMS is a bipolar fiber, which is more suitable for gases and low molecular weight compounds [25]. The PDMS/DVB fiber is inclined to extract semi-volatile compounds and large volatile  
Figure 1: Chromatograms obtained from four different fibers (DVB/CAR/PDMS, divinylbenzene/carboxen/poly dimethylsiloxane; PDMS/DVB, polydimethylsiloxane/divinylbenzene; CAR/PDMS, carboxen/polydimethylsiloxane) using the same sample and with the same optimized condition.  
molecules and it has been reported as the best option for many polar analytes such as pesticide residues [26]. The fiber with the DVB/CAR/PDMS coating combines the characteristics of three different absorbing phases (PDMS, DVB, and CAR), and can trap a wide range of volatile components with different volatility, polarity, structure, and molecular weight. The DVB/CAR/PDMS fiber was identified as the optimal fiber and provided good results in qualitative and quantitative analysis for many food and products such as oranges [27], rice [28], red and white wine [29], and potato products [30; 31]. Consequently, the fiber with a DVB/CAR/PDMS coating was selected as the best fiber coating for the identification of sweet potato volatile compounds.
# Quantum Key Distribution with Post-Processing Driven by Physical Unclonable Functions
###### Abstract  
Quantum key distribution protocols allow two honest distant parties to establish a common truly random secret key in the presence of powerful adversaries, provided that the two users share a short secret key beforehand. This pre-shared secret key is used mainly for authentication purposes in the post-processing of classical data that have been obtained during the quantum communication stage, and it prevents a man-in-the-middle attack. The necessity of a pre-shared key is usually considered to be the main drawback of quantum key distribution protocols, and it becomes even stronger for large networks involving more than two users. Here, we discuss the conditions under which physical unclonable functions can be integrated in currently available quantum key distribution systems in order to facilitate the generation and the distribution of the necessary pre-shared key with the smallest possible cost in the security of the systems. Moreover, the integration of physical unclonable functions in quantum key distribution networks allows for real-time authentication of the devices that are connected to the network.  
quantum key distribution; physical unclonable functions; authentication Article
# The Viable but Non-Culturable (VBNC) State, a Poorly Explored Aspect of Beneficial Bacteria
## 3 The VBNC State in Beneficial Bacteria
### Gammaproteobacteria and the VBNC State
However, it has been shown that plant-bacteria interaction is a mechanism that allows these bacteria return to the cultivable state. Therefore, their use in the formulation of stable bacterial inoculants that can stimulate plant growth after rehydration [4], or the development of formulations to perform biological control [23], could be an alternative to reduce the overuse of nitrogen fertilizers and pesticides, decreasing the damage caused by these chemical products [88]. _Vibrio fischeri_, also belonging to the Gammaproteobacteria, is a luminous marine bacterium that lives freely or in symbiosis with different species of fish and squid [89]. The most studied interaction in this microorganism is the symbiosis with the Hawaiian squid, _Euprymma scolopes_, inducing bioluminescence that the squid uses to avoid predation during nocturnal activity [90]. In the squid-_Vibrio_ symbiosis, bacteria are found in a ventral tissue called the lumen organ. The relationship between _V. fischeri_ and _E. scolopes_ is characterized by daily rhythmic cycles that control the population dynamics of bacteria. At night, when squid take feed, the light organ fills with bioluminescencet _V. fischeri_. At dawn, the squid bury themselves in the sand and ventilate approximately 90% of the bacterial population to the environment; the remaining bacteria repopulate the crypts and are ready to produce light at dusk [91]. This example of symbiosis shows beneficial microorganisms' important role in their hosts' health and activities. The role of the VBNC state in _V. fischeri_ living freely in the marine environment or in symbiosis is still unknown. It has only been discovered that non-culturable cells lose their luminescence in response to fluctuations in salinity [52]. The loss of luminescence could have relevance in the diagnosis of marine environments, allowing the determination of alterations in salinity, temperature, and the concentrations of nutrients related to the number of luminescent bacteria of _V. fischeri_, with the interpretation that the fewer luminescent bacteria observed, the greater the disturbance present in the marine environment.
## 3 Results and Discussion
### Essential Oil Composition  
The composition of the essential oils is presented in Table 1. Monoterpene hydrocarbons were predominant, accounting for 76-80% of the essential oil, with \(\upalpha\)-pinene (approximately 37%) and \(\upbeta\)-phellandrene (27.5-30.0%) as the two main compounds in all samples. \(\upalpha\)-Pinene is widely occurring in essential oils. _Pinus_ essential oils with \(\upalpha\)-pinene as a major compound are pharmaceutically used in inhalation products, unctions, or as bath additives [17]. Essential oils from _Pinus peuce_, having comparable pinene and \(\upbeta\)-phellandrene contents to the present _P. cembra_ essential oil, showed promising anti-cancer and anti-inflammatory activities [18]. The analytical column used did not separate limonene from \(\upbeta\)-phellandrene. Previous experiences report that _P. cembra_ essential oil contains more \(\upbeta\)-phellandrene than limonene [9; 13]. \(\upbeta\)-Pinene (7.0-9.0%) was a further remarkable monoterpene hydrocarbon, while the percentages of camphene and myrceene remained below 2-3%. Oxidized monoterenes represented, on average, 1.9-2.6% of the essential oil, with the main compounds of thymol methylether (0.5-0.8%), bornyl acetate (0.4-0.7%), and cryptone (0.3-0.5%). Other compounds of this class mostly had percentages of 0.1% or less. Amongst the sesquiterpene hydrocarbons, germacrene D and \(\delta\)-cadinence came to 4.3-7.4% and 2.1-2.8% of the essential oil, respectively. Other compounds from this class were below 1% each. Finally, six oxidized sesquiterpenes could be identified; they occur at low levels, together representing less than 1.4% of the oil, each usually below 0.4%. Diterpenoids were not present in the actual essential oils, in contrast to essential oils obtained in a laboratory scale Clevenger type hydrodistillation device, which after three hours contained small amounts of diterpenes [11; 13].
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 2 Hybrid FSO/RF Link System
### Atmospheric Attenuation for Hybrid Links  
Atmospheric attenuation signifies the reduction in signal power through the atmosphere, rendering it a crucial factor for consideration. Suppose the attenuation coefficient for the FSO link in foggy weather is \(\alpha_{1,fog}\), which can be derived from [PERSON]'s model as [14]  
\[\alpha_{1,fog}=\frac{3.91}{V(\mathrm{km})}\bigg{(}\frac{\lambda(m)}{550\times 1 0^{-9}}\bigg{)}^{-q}(\mathrm{dB/km}), \tag{6}\]  
where \(V\) is the visibility, \(\lambda\) is the wavelength, and the parameter \(q\) varies with visibility as  
\[q=\begin{cases}1.6,&V>50\;\mathrm{km},\\ 1.3,&6\;\mathrm{km}<V<50\;\mathrm{km},\\ 0.16\times V+0.34,&1\;\mathrm{km}<V<6\;\mathrm{km},\\ V-0.5,&0.5\;\mathrm{km}<V<1\;\mathrm{km},\\ 0,&V<0.5\;\mathrm{km}.\end{cases} \tag{7}\]  
If the attenuation coefficient during rainfall is \(\alpha_{1,rain}\), which is related to rain intensity \(R\), the expression can be described as  
\[\alpha_{1,rain}=1.076R^{0.67}(\mathrm{dB/km}). \tag{8}\]  
When the frequency of the RF link is below 10 GHz, the rain attenuation is calculated as follows [16]:  
\[\alpha_{2,rain}=kR^{\alpha}(\mathrm{dB/km}), \tag{9}\]  
where the coefficients \(k\) and \(\alpha\) are the signal's frequency and the elevation angle. The attenuation of the RF link caused by fog can be expressed as  
\[\alpha_{2,fog}=K_{l}M(\mathrm{dB/km}), \tag{10}\]  
where \(M\) is the liquid water density in fog. \(K_{l}\) is the specific attenuation coefficient.
# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
## 4 Results and Analysis  
### Comparison of Kriging Interpolation Coverage of Three Products  
Firstly, we resampled the XCO\({}_{2}\) data of the OCO-2, GOSAT, and CT models to obtain \(1^{\circ}\times 1^{\circ}\) half-monthly and monthly global XCO\({}_{2}\) datasets. In order to understand the effect of the Kriging model used for interpolating XCO\({}_{2}\) in this paper in more detail, we spatially display the data coverage of OCO-2 and GOSAT datasets before and after Kriging interpolation in Figure 1. At each \(1^{\circ}\times 1^{\circ}\) grid, if it contains an XCO\({}_{2}\) observation meeting the quality requirements, it is filled with a color (the data before interpolation is dark blue, while the data after interpolation is light blue). The results show that the coverage changes of half-monthly and monthly XCO\({}_{2}\) datasets are basically the same before and after interpolation. The coverage of half-monthly XCO\({}_{2}\) products in 2018 before and after interpolation is shown in Figure 2. In July, August, and December 2018, the coverage of GOSAT data after interpolation does not increase much, because the coverage of GOSAT raw data in these months is low. Overall, before interpolation, the average coverages of OCO-2, GOSAT, and CT products are 18.01%, 3.95%, and 100%, respectively. After interpolation, the average coverages of the OCO-2 and GOSAT datasets are 71.66% and 52.45%, increased by 53.65% and 48.5%, respectively. It can be seen that this method can effectively improve the temporal and spatial resolution of XCO\({}_{2}\) products. We believe that the interpolation results of OCO-2 and GOSAT XCO\({}_{2}\) are mainly affected by the spatial distribution, total number, and sampling interval distribution of observation points. If there is XCO\({}_{2}\) observation in each \(1^{\circ}\times 1^{\circ}\) grid, or it is evenly distributed in space, this method can obtain XCO\({}_{2}\) data through wider coverage. The coverage of CT reaches 100%, which is due to the time resolution of CT model data being 1 day and the spatial resolution being \(3^{\circ}\times 2^{\circ}\); the coverage range is \(-180^{\circ}\)-\(180^{\circ}\) longitude and \(-90^{\circ}\)-\(90^{\circ}\) latitude, and the distribution is uniform. According to the search range of Kriging interpolation, there are data affecting the points to be interpolated. This also paves the way for creating a global seamless \(1^{\circ}\times 1^{\circ}\) for the later generation XCO\({}_{2}\) dataset, as shown below.
## 4 Simulation Case Study
### Simulation Models and Scenarios  
Simulation models were developed based on the conceptual model, i.e., based on the different historical states of manufacturing organizations based on their functions evolution. For this study, the following simulation models (SMs) were defined as:  
* Base model (BM);* 10. Production technology (PT);
* 11. Operative production management (OPM);
* 12. Control and prevention of technical limitations (TL);
* 13. Global logistics and systems expansion towards TQM (LSQ);
* 14. Industry 4.0 ([4.0);
* 15. Human factor malfunction in the strategic, tactical, operative levels (HF3);
* 16. Human factor malfunction in the tactical, operative levels (HF2);
* 17. Human factor malfunction in the operative level (HF1).  
The simulation models were presented as small incremental cases studies along the historical process of organizational functions evolution in the different functional areas of an organization. This meant that the PT effects were added to the OPM effects, and, consequently, the TL and LSQ effects were added to the I4.0 implications. Moreover, the simulation case study begins with an initial situation, BM, in which an organization presents capacity, technical bottlenecks, a push production strategy, neither quality nor maintenance management, as well as a lack of information and logistics management other than information delays and the classical operative logistics functions of TTS logistics. Based on these arguments, the following Table 1 presents the logic of the simulation models.  
Based on the logic described, the simulation models were developed. The main inputs for the model were the market demand for the products considered, and the capacities for suppliers, production processes, and distribution as well as the specific settings for the different models depending on the measures previously described. The output values were the key indicators that were used to evaluate the results.  
There was one simulation scenario that was read from an Excel sheet as the input demand for all the simulation models. In the demand scenario simulated, it is composed of one product with a stationary demand pattern and one product initiating with a trend demand pattern during its launch process and then followed by a stationarity demand pattern. Therefore, the demand pattern simulated tried to simulate a manufacturing organization with an existing production level in a mature stage while trying to introduce a new product to the market. The demand read by Vensim software is shown in Figure 9.  
Figure 9: Demand level scenario for all simulation models (the authors’ own elaboration).
# Experimental Analysis of a Novel GFRP Stiffened Pipe
## 3 Stiffness Investigation of the GFRP Pipe
### Theoretical Analysis of Initial Ring Stiffness of the GFRP Pipes  
According to ISO7865-2019, the stiffness (\(S\)) of a plain GFRP pipe (simplified as: N) can be defined as a measure of resistance to ring deflection under external load, which can be calculated as follows:  
\[S=E\,I/D_{m}{}^{3}, \tag{3}\]  
where \(E\) is the apparent modulus of elasticity as determined in the ring stiffness test, in newtons per square meter; \(D_{m}\) is the mean diameter of the pipe, in meters; \(I\) is the second moment of area in the longitudinal direction per meter length, expressed in meters to the fourth power per meter, i.e.,  
\[I=e^{3}/12, \tag{4}\]  
where \(e\) is the wall thickness of the test pipe, in meters.  
According to the above equations, the theoretical stiffness for the plain type of pipes can be calculated as listed in Table 4.
# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 2 State of the Art
### The Spatial and Temporal Structure of Road Traffic Noise in Large Urban Zones
#### 2.3.1 Classification of Roads in Large Urban Zones by Their Road Traffic Noise
As a reference level, we considered for each site \(s_{i}\) the daily equivalent level calculated over the period (6:00-10:00 p.m.) [68]. Once the noise profiles have been obtained, a visual analysis shows that strong similarities among profiles occur, suggesting an underlying organization into groups of sites. This organization is different from the standard legislative categorization of roads, as analyzed in some studies [13; 14]. For this reason, an unsupervised clustering algorithm was applied to disclose the internal structure of the measurement sites in terms of their normalized noise profiles. The clustering analysis is described in detail in [65; 68], where a description in terms of only two clusters is found to be satisfactory. The clusters are labeled with the index \(k=1,2\), and the mean hourly cluster profiles denoted as \(\overline{\delta_{k}}(h)\). The results are shown in Figure 3.  
Although both clusters display similar mean profiles, they are statistically different [65; 68]. Roughly speaking, they differ during the night, as manifested by the larger drop in \(\overline{\delta_{1}}(h)\) with respect to \(\overline{\delta_{2}}(h)\) in Figure 3. Qualitatively, this is due to the fact that traffic flow on road stretches belonging to cluster 1 diminishes much more than on road stretches in cluster 2 during night hours. In other words, cluster 1 roads are lower traffic stretches whose flows decrease more prominently during night periods. On the contrary, road stretches in cluster 2 possess a relatively large traffic flow even during the night. The former represent secondary streets in predominantly residential areas, while the latter  
Figure 3: Mean normalized hourly cluster profiles, \(\overline{\delta_{k}}(h)\) [dB], vs. day hour \(h\), for the two clusters of index \(k=1,2\), obtained from the 93 preliminary measurements over the whole city of Milan (see e.g., [68]). The standard deviations of the profiles are indicated by the colored bands. (With permission from Elsevier). main city arteries. In order to characterize the traffic flow of roads, we need to address the issue of vehicles flow within the urban zone. This will require us to define a non-acoustic parameter associated with each road stretch.
## 4 Simulation Setup
### Dataset Parameter Interpretation  
The dataset used in the proposed model constitutes the following UAV parameters:  
* **Carrier-to-Noise Ratio (CN0):** the ratio of the carrier signal power to the noise power within the signal bandwidth, indicating the quality of the GPS signal.
* **Pseudo-Range (PD):** the measured distance from the satellite to the receiver, calculated based on the time it takes for a GPS signal to travel from the satellite to the receiver.
* **Time of the Week (TOW):** the number of seconds elapsed since the start of the GPS week, crucial for time-stamping data in synchronization with GPS time.
* **Carrier Doppler (DO):** the Doppler shift of the carrier frequency due to the relative motion between the satellite and the receiver, providing information about the satellite's speed.
* **Receiver Time (RX):** the reference time used by the receiver, which helps align the internal receiver clock with the GPS system timing for accurate signal processing.
* **Carrier Phase Cycles (CPs):** the accumulated number of cycles that indicate the difference in frequency between the receiver's generated carrier and the satellite's carrier.
* **Early Correlators (ECs):** set one or two chip spacings before the prompt correlator, used for fine-tuning the GPS signal tracking and timing adjustments.
* **Late Correlators (LCs):** set one or two chip spacings after the prompt correlator, similarly used for adjustments in GPS signal tracking, particularly for correcting timing errors.  
* **Prompt Correlator (PC):** the code-tracking process that multiplies the local pseudo-random code with the incoming GPS signal to synchronize with it.
* **Prompt Quadrature Component (PQP):** the quadrature component of the prompt correlator amplitude, which complements the PIP in forming a complete signal representation.
* **Tracking Carrier Doppler (TCD):** the Doppler shift measured specifically during the correlation phase, used to define the acceptable range of the carrier Doppler, adjusting for satellite motion effects.
* **Pseudo-Random Noise (PRN):** the satellite identification number, which helps in distinguishing signals from different satellites in a constellation.  
To summarize, these parameters provide a comprehensive view of the signal's spatial and temporal characteristics, including carrier-to-noise ratios, Doppler shifts, and time references. Such diversity in features is essential for capturing different manifestations of GPS spoofing, laying the groundwork for the subsequent feature importance analysis and model evaluation.
# Quantum Game Application to Recovery Problem in Mobile Database
## 4 Discussion
### Simulation Results
#### 4.2.3 Experiment Three  
Aim: The final set of experiments examined the connection between the likelihood of recovery and the handoff threshold. There is a favorable correlation between increasing the handoff rate and the likelihood of completing the recovery process. Handoff involves moving a MS from one base station to another. Algorithms with set parameters do not work well in varying system configurations. Handoff algorithms should take into consideration the communication system's peculiarities. If the preparation time of rapid handoff is longer than the WLAN sojourn time linked to mobile node speed, the handoff fails and packets are lost. If the mobile node speed is too slow, handoffs are initiated too late, reducing WLAN service duration. The handoff cost comprises the checkpoint status, message log, and acknowledgement.  
Main Results: Figure 6 plots the completion of the recovery process against increases in the hand-off threshold rate. The recovery likelihood is significantly reduced when using the log management technique, but the hand-off threshold rate is raised. Alternatively, some techniques decreased gradually when the threshold value was increased.  
Discussion: The results demonstrated that the log technique is applicable only in small work settings. Whereas the hybrid approach and the agent-based method both performed well for areas with multiple regions or regions located farther from the site of retrieval. As anticipated, the suggested model has a higher recovery probability in the long term when compared to the other methods in the various simulated settings.