esa-sceva/satcom-chunk-collection huggingface dataset:
# Solid-Phase Microextraction Techniques and Application in Food and Horticultural Crops
## 4 HS-SMPE Arrow Applications in Food Analysis  
The use of a SMPE Arrow for sample preparation in analysis enables much larger amounts of analytes to be collected from the sample matrix. However, there has not been a great deal of research investigating volatile components in food using SMPE Arrow. [PERSON] et al. used SPME Arrow with GC-MS to analyze the volatile components in _Crisium setidens_ Nakai, a Korean herb that is used to flavor steamed rice and is used as a traditional medicine [41]. In developing their method, they compared a four different SMPE Arrows for analyte extraction: (1) a PDMS SMPE Arrow, (2) a PA SMPE Arrow, (3) a DVB/PDMS SMPE Arrow, and (4) a carbon wide-range/PDMS SMPE Arrow. The most effective extraction of volatile components (aldehydes, aliphatic hydrocarbons, furans, nitro and nitrate compounds, and pyrazines and terpen) was performed using the carbon wide-range/PDMS SMPE Arrow with 58 volatile compounds being identified (Figure 6).  
Compared to the other SMPE Arrows, the results obtained using the carbon wide-range/PDMS SMPE Arrow had the lowest relative standard deviation for the analytes measured. The extraction conditions were as follows. Samples were equilibrated for 10 min. SMPE Arrow was then placed into the headspace for 30 min, after which desorption occurred at 250 \({}^{\circ}\)C for 5 min.  
Figure 6: Major volatile organic compounds identified in _Crisium setidens_ Nakai using carbon wide-range/PDMS SMPE Arrow.  
[PERSON] et al. compared the headspace analysis of volatiles in brown rice vinegar using both SPME and a SPME Arrow [42]. They compared four different types of SPME fiber and found that a CAR/PDMS SMPE fiber resulted in the best extraction of volatile organic compounds. GC-MS headspace analyses using a conventional CAR/PDMS SMPE and a CAR/PDMS SMPE Arrow were then compared. Samples were equilibrated at 50 \({}^{\circ}\)C for 30 min for each SPME system. Overall, there was a 1.3- to 2-fold increase in sensitivity, together with lower uncertainty in concentrations determined using the CAR/PDMS SMPE Arrow. Furthermore, more compounds were extracted using the CAR/PDMS SMPE Arrow compared to the conventional CAR/PDMS SMPE fiber (i.e., 12 versus 7 volatile organic compounds).  
More recently, [PERSON] et al. used a SPME Arrow to determine furan content in 31 different food products (oats, malt extract, rice cereal, donut, muffin, grilled beef kebab, chicken nuggets, whole milk, etc.) using HS GC-MS [43]. Furans are an undesirable contaminant arising from heat-processed foods and are considered possible carcinogens [44]. GC-MS headspace analysis was performed using a CAR/PDMS cellulose SPME Arrow with an extraction temperature of 30 \({}^{\circ}\)C. The method enabled ng/g concentrations of furans to be measured. The results showed that the total furan concentration was highest in brewed coffee (around 35,000 ng/g), and that it was the lowest in potato chips and cookies (0.57-1.48 ng/g).
## 3 Methodology
### System Design
#### 3.1.2 Cloud API Services  
API services help to bring all captured images to the server computer for further analysis while keeping the flow of information. This system keeps timestamps and image labels in an ordered manner throughout the processes of the entire system. The API server subsystem passes incoming data flow to the GTI chip-based subsystem to conduct analytics (facial landmark detection for every face image). Additionally, the API services help to store and retrieve meeting data from the database (DB) server while acting as the communication bridge for the entire system.
## 2 Outdoor Positioning in the IoT Ecosystem
### Location-Enabled IoT Technologies
#### 2.3.3 NB-IoT  
Narrow Band IoT (NB-IoT) has introduced by the 3 rd Generation Partnership Project (3 GPP) in 2016. On the contrary to Sigfox and LoRaWAN, NB-IoT operates in licensed spectrum and synchronous communication. Therefore, it provides higher traffic reliability and is preferred for IoT systems that need guaranteed quality of service (QoS). The NB-IoT communication protocol is based on LTE and it reduced its power consumption by reducing LTE protocol functionalities. NB-IoT is compatible with traditional cellular networks. Therefore, it can work in LTE or GSM under licensed frequency bands. NB-IoT has a frequency bandwidth of 200 kHz, and it uses Orthogonal Frequency Division Multiple Access (OFDMA) for downlink and Single Carrier Frequency Division Multiple Access (SC-FDMA) implemented for uplink communication. It leads to a 250-kbps data rate for downlink and a 20 kbps data rate for uplink. NB-IoT uses LTE protocol to let end nodes join the network and connect to one station. Therefore, NB-IoT needs to hand over protocols. As NB-IoT is synchronous communication, end nodes consume more energy than LoRaWAN and Sigfox.
## 2 Description of the Sandwiched Coal- Rock System
### Coal Element and Rock Element  
The testing elements were taken from massive coal and rock blocks in Datong coal basins, Shanxi Province, China. Coal blocks were selected from the No.9 and No.10 coal seams. Rock blocks were obtained from the roof strata, floor strata, and the interburden strata. The lithology was sandstone. To maintain the original state as much as possible, the natural coal and rock blocks were wrapped with plastic film before the preparation of the testing elements. Three different sizes of cylindrical coal and rock elements were mainly prepared for the uniaxial compression tests. The diameter of all the testing elements was 50 mm. The heights of the testing elements were 20 mm, 33.33 mm, and 50 mm, respectively. The ratios of the height to the diameter for the testing elements were 0.4, 0.67, and 1, respectively.  
According to the methods suggested by the International Society for Rock Mechanics [25], the coal and rock elements were firstly cored along the same orientation to maintain homogeneity. No visible cracks and damages were distributed on the surface of the testing elements. Then, the elements were shaped and polished carefully to satisfy the standards of flatness, verticality, and parallelism. Figure 2 shows a photograph of the experimental coal and rock elements with different heights. For further details of the sizes, coal and rock elements with a height of 25 mm and 100 mm are also presented in Figure 2.  
After the preparation of the testing elements, a series of tests was performed to obtain the basic physical and mechanical parameters. As presented in Table 1, the relevant parameters of the experimental elements were determined averagely from three series of tests. The strength of the rock element (H = 100 mm) was 21.7 MPa, while it was 10.4 MPa for the coal element (H = 100 mm).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Diameter** & **Height** & **Quality** & **Density** & **Uniaxial** & **Elastic** & **Moulus** & **Initial** \\  & **/mm** & **/mm** & **/g** & **/(kg/m3)** & **Strength/(MPa)** & **/(GPa)** & **/(M\(\Omega\)-m)** \\ \hline \multirow{3}{*}{Coal} & 49.78 & 20.06 & 63.24 & 1.24 & 49.23 & 3.28 & 0.48 \\  & 49.86 & 33.42 & 85.30 & 1.31 & 34.24 & 2.15 & 0.82 \\ \cline{1-1}  & 49.92 & 49.86 & 126.24 & 1.29 & 21.42 & 1.54 & 1.18 \\ \cline{1-1}  & 49.78 & 100.11 & 249.14 & 1.27 & 10.42 & 1.34 & 2.43 \\ \hline \multirow{3}{*}{Rock} & 49.91 & 20.08 & 91.43 & 2.33 & 69.85 & 6.24 & 0.17 \\ \cline{1-1}  & 49.86 & 33.26 & 146.57 & 2.36 & 43.22 & 9.03 & 0.22 \\ \cline{1-1}  & 49.95 & 50.05 & 230.94 & 2.43 & 36.73 & 11.18 & 0.44 \\ \cline{1-1}  & 49.88 & 99.97 & 455.30 & 2.31 & 21.72 & 12.99 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of experimental coal and rock elements.  
Figure 2: Experimental coal and rock elements. (**a**) Coal element; (**b**) rock element.
## 4 Results and Discussion
### RQ1: Which Types of CVs Are Reported?  
To accomplish the data extraction process, we initialized with a pre-established classification scheme to group the types of reported CVs. The classification scheme includes Folksonomies, Ontologies, Taxonomies and Thesauri. We only found direct evidence for Ontologies and Taxonomies. Ten selected papers (S12, S16, S29, S31, S33, S52, S73, S74, S86 and S88) reported the use of Thesauri; but they focused on Ontologies, considering the thesaurus only as part of the reported ontology. No direct evidence was found for folksonomies or other terms that can be considered as CVs.  
We identified 79 primary papers related to the use of ontologies in RE and eleven related to taxonomies, as depicted in Table 8. Appendix A shows the full list of selected papers.  
Most of the mentions to ontologies in RE were related to domain ontologies for facilitating the specification of requirements, elicitation and requirements reuse. These results are not surprising as the aim of ontologies is to reduce ambiguities and gain deeper insights of a certain domain. Some examples of the frequent uses of domain ontologies reported in the selected studies were: security requirements (S18, S38, S60, S62, S66, S90), reducing ambiguities (S10, S18, S36, S41, S58), improving understanding (S08, S23, S27, S32, S45, S50, S65), detecting and resolving conflicts (S02, S19, S43, S44, S49, S72) and specifying requirements (S04, S78, S79, S89) among other uses.  
The main categories of impacts of using taxonomies in RE were related to detecting faults and defects (S07, S26), scenarios (S14) and privacy goals (S06) for eliciting and specifying software requirements.  
It is worth mentioning that our search strategies did find papers about folksonomies, but they were focused on web applications, tagging, indexing, sentiment analysis and social media. They were not explicitly related to any RE activity, so we discarded those papers because of the exclusion criterion \"Out of scope\".
# Analysis of the Possibility of Using Drill-Type Electrodes for Air Ionization
## 4 Analysis and Interpretation of Results
Comparative characteristics relating to the corona voltage, sparking voltage \(\mathrm{U_{s}}\) and breakdown voltage \(\mathrm{U_{b}}\) occurring between needle electrodes with two different needle angles are shown in Figure 6. Relationships relating to these spark gaps are similar to those shown in Figure 5. In order to compare waveforms recorded in the drill-type and needle spark gaps and to show differences between them, the exact values of \(\mathrm{U_{o}}\), \(\mathrm{U_{s}}\) and \(\mathrm{U_{b}}\) voltages for the three selected distances (8 cm, 12 cm and 16 cm) between electrodes are presented in Figures 7 and 8 for each of the examined systems. For example, for drill-type spark gaps (Figure 7a) with a distance between the electrodes of 8 cm, the highest value of breakdown voltage \(\mathrm{U_{b}}\) is 59.9 kV (for electrodes with 0.8 mm diameter), the lowest value is 55.0 kV (for electrodes with 1.2 mm diameter), the maximum scatter of results for the eleven tested pairs is approximately 6%. At a distance of 12 cm between the drill-type electrodes (Figure 7b), the voltage \(\mathrm{U_{b}}\) values are, respectively: highest 79.04 kV (electrode diameters 0.9 mm) and lowest 67.0 kV (electrode diameters 1.5 mm), and the maximum scatter of results is 9%. For a distance of 16 cm between the drill-type electrodes (Figure 7c) the highest \(\mathrm{U_{b}}\) value is 93.72 kV (electrode diameters 0.7 mm), the lowest value is 81.68 kV (electrode diameters 1.0 mm) and the scatter of results is 7%. In a similar way, it is possible to trace the variability of each of the characteristic voltages, both for drill-type electrode systems (Figure 7) and for the needle electrode systems (Figure 8). In order to facilitate the analysis and formulation of general conclusions from the bar graphs shown in Figures 7 and 8, mean values and standard deviations were calculated for each characteristic voltage and for each electrode system. Results are presented in Table 1.  
In Figures 9 and 10, the maximum values are represented by red and the zero values by blue. The presented three-dimensional distributions effectively visualize the areas where the highest and lowest characteristic voltage values have been recorded. The analysis of results presented in the form of three-dimensional distributions confirm conclusions drawn when interpreting the collective waveforms of characteristic voltages (Figures 5-8).
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### AI, ML and Network Security
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Applications** & **Description** & **Use Cases** & **Strengths** & **Weaknesses** \\ \hline \multirow{4}{*}{**Anomaly Detection**} & Network Traffic Analysis & A decentralized one-class support & \multirow{4}{*}{Rapid detection of abnormal activities} & \multirow{4}{*}{Susceptible to false positives/ negatives} \\  & User Behaviour Analysis & & vector machine & \\  & Analysis & analyses virtual nodes for anomalies, using canonical correlation to & & \\  & & measure neighbour correlations [75]. & & \\ \hline \multirow{4}{*}{**Intrusion Detection**} & ML-driven intrusion detection systems & Intrusion detection systems can & \multirow{4}{*}{High accuracy in identifying threats} & Resource intensive may impact efficiency \\  & & identity intrders in a network using neural networks and ML techniques [76]. & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Threat Response** \\ \end{tabular} } & \multirow{4}{*}{Rapid response to potential threats} & Optimization models can mitigate DDoS attacks through slice isolation, enhanced by AI optimization techniques [77] & Swift mitigation of security breaches & May require human oversight for validation \\ \cline{1-1} \cline{3-5}  & & Reinforcement Learning models can manage slicing & \multirow{4}{*}{Proactive historical data for predictions} \\
**Predictive Analytics** & Anticipating security breaches & resources and predict threats based on past data and network changes [78]. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Application of AI in network security.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 5 Experimental Verification and Results  
### Experimental Setup  
In this study, we selected a region characterized by a complex and dynamically changing wireless signal environment as the experimental scenario. The experimental setup is shown in Figure 4. During the experiments, we deployed multiple mobile edge computing (MEC) servers near user devices to process uploaded data, provide environmental sensing services, and construct and update fingerprint maps. The study involved numerous participants using mobile devices for crowdsourced data collection. By installing a custom-developed mobile application, participants uploaded various wireless data features to the MEC servers, including environmental sensing information, wireless signal characteristics, and device metadata.  
The wireless signal features encompassed RSSI (received signal strength indicator), CSI (channel state information), ToA (time of arrival), and AP topological relationships. Environmental sensing information incorporated data from built-in device sensors, such as barometric pressure (with an accuracy of 0.2 hPa), geomagnetic intensity (0.1 uT accuracy), ambient illuminance (ranging from 0 to 100,000 lux), and acoustic features (16-20 kHz frequency range). Additionally, devices equipped with GPS were required to upload precise ranging information to support fingerprint map construction and updates.  
In validation experiments, we tested target devices without GPS functionality. These devices had known environmental sensing parameters and wireless signal characteristics, but unknown precise signal ranging. By placing these devices at predefined environmental sensing points with pre-recorded ground-truth coordinates, we applied the proposed signal ranging scheme and compared the perception results with actual measurements to evaluate the system's accuracy and performance. The experiment workflow of our framework is depicted in Figure 5, which illustrates the overall workflow of our framework, consisting  
Figure 4: Experiment structure of our framework.  
of an offline fingerprint map training phase and an online target matching phase. In the offline phase, location data, wireless signal characteristics, and device information from UEs are preprocessed to build a fingerprint map compatible with various machine learning algorithms. In the online phase, the system matches incoming signals from target UEs with the fingerprint map to output positioning results in real time.
# Behavior and Damage Characterization of Impulsively Loaded Cross-Laminated Timber Panels
## 2 Materials and Methods
### Impulsive Center-Point Testing System Design
The clamping force provided by each assembly limits the rotation of the specimen at the support, thereby setting a control on rotational rigidity. The clamping force is introduced via torque applied to the nut securing the top plate. The torque force is measured using a torque wrench, and a conversion is applied to the torque reading to account for the use of an open ended tight-clearance offset socket, or \"crowfoot wrench\" (Figure 7). The torque equation is given as Equation (1). For the experiment, three levels of torque (65 in-lbs, 130 in-lbs, and 200 in-lbs) were selected to provide data for a wide range of rotational rigidities at the specimens' boundary conditions. \[T_{W}=\frac{T_{A}\cdot L}{L+A} \tag{1}\]  
where  
\(T_{A}=\) Torque exerted at the end of the adapter  
\(L=\) Distance between square drive and hand position  
\(T_{W}=\) Wrench scale reading  
\(A=\) Length of adapter or extension  
The impactor assembly, shown in Figures 8 and 9, was fabricated with a rounded face to reduce high-stress concentrations caused by a concentrated center-point load. Concentrated loads have been shown to affect the shear deformation of CLT in the area of the load, thereby changing the longitudinal strain [21,29]. The impactor is half of a large diameter, ASTM A500 Gr.B, steel structural tube section, HSS10.75\(\times\)0.5, fastened to ASTM A36 plates. A 0.5 in thick firm felt type F5 programmer is adhered to the impactor's face. The radius of the impactor was selected to achieve a radius-to-CLT-lamella-thickness ratio of 4 (i.e., 5.375 in/1.375 in). The radius of the impactor conforms to the recommendations of ASTM D198, radius of curvature 2-to-4 times the depth, and Hou [30], radius of curvature 2.5-to-3 times the depth. The impactor mass is 111.52 lbs (50.46 kg). Four piezoelectric dynamic force sensors were mounted to the impactor plate. The impactor and load cells were connected to the actuator pusher plate using four through-bolts with thin-walled tubing to reduce friction and _catching_ of the threads. The thin-walled tubing extended the length of the bolt shank and threading, so as the impactor struck a specimen, the load cells were compressed between the impactor plate and the pusher plate, causing each of the plates to slide along the bolt shank or threading. The force sensors were preloaded with approximately 2000 lbf (8.9 kN) to 6500 lbf (29 kN) on average. Preloading ensured that each load cell received equal loading from an impact and eliminated delay in load cell response due to excess space. Preloading also served to maintain the impactor level and motion rigid. Figure 7: Torque wrench.
## 6 Conclusions  
A method to estimate a conservative value of the maximum field level that can be radiated by a 5G base station has been introduced and experimentally validated.  
The method is conceived to be simple and low-cost. It requires the broadcast and the traffic Envelop Radiation Patterns and the measurement of a reference value transmitted through the broadcast beams.  
In the experimental example, a standard and relatively low-cost scalar spectrum analyzer was used. It is understood that other solutions can be adopted, for example, directly accessing reference signals, but such solutions would require more expensive devices.  
With reference to the technology currently used in 5G systems, the main limitation of the proposed procedure is that it gives only a conservative value of the field level. If the limit of the standard is exceeded, it is, therefore, necessary to use other procedures that allow a more precise estimation of the field level.  
As noted in the introduction, this paper is closely linked to the two papers [24; 26], with which it shares the experimental set-up. The three papers together cover a large number of possible solutions for 5G MPE, giving at the same time a clear idea of the sheer complexity of MPE in 5G. This great complexity is due to the high flexibility of 5G and the limited information on the system status from the UE side, in particular with reference to the AAS configuration during the measurement session.  
The enormous difficulties encountered in defining robust MPE 5G extrapolation techniques are a lesson that should be considered in setting the standard for the sixth generation of cellular communications systems.  
**Author Contributions:** Conceptualization, M.D.M.; Formal analysis, M.L. and G.C.; Funding acquisition, F.S. and M.L.; Methodology, F.S. and M.D.M.; Supervision, F.S.; Validation, D.P.; Writing--original draft, M.D.M.; Writing--review and editing, C.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is part of the European Union's Horizon Europe research and innovation program under grant agreement No. 101057527 (NextGEM) funded by the European Union. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The author thanks [PERSON] (Arpa Lazio), [PERSON] (Arpa Lazio), [PERSON] (Arpa Lazio), [PERSON] (Arpa Piemonte), [PERSON] (Vodafone Italia SpA), [PERSON] (Vodafone Italia SpA), [PERSON] (Rohde Schwarz company), and [PERSON] (Rohde Schwarz company) to provide the experimental data and for their invaluable help in analyzing the measured data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Functional Properties Evaluation
#### 3.2.2 Filtration Efficiency
Therefore, the increase in filtration efficiency of PCL membranes functionalized with TiO\({}_{2}\) and MgO NPs could be due to the increase in surface roughness and specific surface area enhancing the trapping capacity of the fibers on the particles. Although membranes with higher thickness and basis weight are associated with higher filtration efficiency, they are also associated with higher breathing resistance [6; 21]. Thus, a balance should be achieved to obtain the greatest filtration performance and breathability at the same time. In this case, the PCL/MgO NPs membranes were the ones that presented the highest filtration efficiency and the lowest thickness and basis weight, showing the advantages in using these membranes to obtain a high filtration performance without adding extra weight while maintaining the comfort. [PERSON] et al. developed PAN/MgO NPs electrospun membranes, which demonstrated high filtration efficiency and low air resistance [54]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Thickness** & **Basis Weight** & **Filtration Efficiency** \\  & **(mm)** & **(g/cm\({}^{2}\))** & **(\%)** \\ \hline PCL & 0.376 & 0.005 & 84.9 \\ PCL/Ag NPs & 0.600 & 0.010 & 70.8 \\ PCL/TiO\({}_{2}\) NPs & 0.506 & 0.008 & 86.0 \\ PCL/MgO NPs & 0.147 & 0.003 & 99.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thickness, basis weight and filtration efficiency (%) of the different electrospun membranes (PCL, PCL/Ag NPs, PCL/TiO\({}_{2}\) NPs and PCL/MgO NPs). Combining the results of antibacterial and filtration tests, the PCL/MgO NPs were demonstrated to be the most promising membrane, and the one selected for further tests.