esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Extraction and Detection of Volatile Compounds  
In total, 2.0 g of tea power was weighed and immediately transferred to a 20 mL headspace vial; then, the volatile compounds were extracted via the HS-SPME (headspace solid-phase microextraction) method and detected via GC-TOF-MS (gas chromatography-time-of-flight-tandem mass spectrometry), using ethyl decylate (Sigma, St. Louis, MO, USA) as an internal standard. The SPME conditions were: the extraction needle PDMS/DVB9 was set with an incubation temperature of 80 \({}^{\circ}\)C, incubation time of 31 min, extraction time of 60 min, and resolution time of 3.5 min. The chromatograph conditions were: A 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m chromatographic column (Rai-SsilMS) was used, the temperature of the sample inlet was controlled at 250 \({}^{\circ}\)C, the temperature of the transmission line was controlled at 275 \({}^{\circ}\)C, helium was used as the carrier gas, and the helium flow rate was set to 1 mL/min. The programmed heating process was maintained at 50 \({}^{\circ}\)C for 5 min, which rose to 210 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min for 3 min, and then rose again to 230 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C/min with non-shunt injection. The mass spectrometry conditions were: the solvent delay time was controlled within 300 s, the scanning range was controlled between 30 and 500 amu, the collection rate was 10 specs/s, the detector voltage was 1530 V, the EI ionization energy was 70 eV, and the ion source temperature was 250 \({}^{\circ}\)C. The data of the measured volatile metabolites were analyzed and processed using the instrument's built-in software and then compared to the mass spectrometry data found in the National Institute of Standards and Technology (NIST) database, searching for relevant mass spectrometry data, and analyzing the base peaks, mass-to-nucleus ratios, and relative kurtosis. Finally, each peak based on the retention time and mass spectrometry was confirmed, VB/VP components were identified using peak areas of more than 1% as effective observation values, and the percentage content of aromatic compounds in each sample using the peak area normalization method was determined [22; 24].  
The OAV (odor activity value) is usually applied to measure the contribution degree of aroma compounds. Compounds with an OAV of more than 1 are considered key aroma compounds in oolong tea production. The OAV is calculated using the following formula:  
\[\text{OAV}=\frac{Cx}{OTx}\]  
where \(Cx\) is the concentration of each volatile compound (\(\upmu\)g\(\cdot\)L\({}^{-1}\)), and \(OTx\) is the aroma threshold of the volatile compounds in water (\(\upmu\)g\(\cdot\)L\({}^{-1}\)) [35].
## 3 Implementation and Results
### Envisat
#### 3.1.2 Case 1: Maneuver Detection Threshold Magnified to 2-Sigma Relative to Baseline 1-Sigma Case  
Figure 4 demonstrates the consequence of increasing the maneuver detection parameter to 2-sigma from 1-sigma. Given this increase, four of the nine maneuvers were undetected, whereas one false detection was noted. This result was expected because an increase in the detection threshold leads to less maneuver detection.  
Figure 3: Baseline case: Perfect detection and no false detection. Vertical green lines represent the maneuvers and the red circles represent potential maneuvers detected by this method.
# Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading
## 6 Results and Discussion
An exception is noticeable in Scenario A because the optimization is performed using just one diameter \(\Phi\), which is optimal for a few parts of the structure, while others are \"over-fitted\", resulting in a decrease of the performance ratios \(-28.4\%\) and an increase of structural mass (+173 kg), as shown in Figure 13.  
Similarly, a monotonic increment of the structural mass at the end of the optimization process is evident from Figure 13. In this case, the tonnage decreases with the increasing of the parameter's number. There is an overall mass reduction of about \(-67.5\%\) (\(-972\) kg) from scenario D to H. In scenarios A, B, and C, the thickness t of structural members is not considered. Therefore, the mass loss is not satisfactory, at about \(-28.4\%\) (\(-409\) kg). The choice of the best scenario should depend on one of the five situations described above (from D to H) related to the better PRs gain and mass loss.
## 5 A Note on Orbit Fitting and Orbit Extrapolation Accuracies  
Before analyzing the performance of orbit determination from ISR and GSR measurements, this section focuses first on assessing the intrinsic accuracy of the broadcast orbit fitted to simulated dynamic orbits as well as on orbit accuracy when extrapolating outside the fit interval.
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 4 Performance Results
### Indoor Experiment  
The indoor passive detection performance of single and multiple low-altitude signal sources is evaluated in this section. Two methods are used to analyze the false alarm probability and  
Figure 16: The results for the CFAR method for two UAVs outdoors at distances of (**a**) 500 m, (**b**) 1500 m, and (**c**) 2500 m.  
Figure 14: The results for the improved cross-correlation method for two UAVs outdoors at distances of (**a**) 500 m, (**b**) 1500 m, and (**c**) 2500 m.  
Figure 15: The results for the HOC method with two UAVs outdoors at distances of (**a**) 500 m, (**b**) 1500 m, and (**c**) 2500 m.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**500 m**} & \multicolumn{2}{c}{**1500 m**} & \multicolumn{2}{c}{**2500 m**} \\ \cline{3-8}  & \(u\)**1** & \(u\)**2** & \(u\)**1** & \(u\)**2** & \(u\)**1** & \(u\)**2** \\ \hline \multirow{2}{*}{CFAR} & Error (MHz) & 2.732 & 0.605 & 0.489 & 5.450 & 13.67 & 0.061 \\  & Error (\({}^{\circ}\)) & 1.050 & 2.250 & 1.430 & 1.550 & 47.71 & 2.520 \\ \hline \multirow{2}{*}{HOC} & Error (MHz) & 3.613 & 5.781 & 3.711 & 5.924 & 19.04 & 47.05 \\  & Error (\({}^{\circ}\)) & 2.400 & 1.330 & 2.270 & 4.960 & 57.15 & 95.78 \\ \hline \multirow{2}{*}{Proposed} & Error (MHz) & 0.039 & 0.057 & 0.058 & 0.078 & 0.049 & 0.044 \\  & Error (\({}^{\circ}\)) & 0.442 & 1.045 & 1.122 & 0.553 & 1.148 & 0.585 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter estimation errors for two UAVs using three methods in an outdoor environment.  
missing alarm probability, and a comparison of three algorithms for indoor parameter estimation is also presented.
## 1 Introduction
In all cases, these methods result in significant setup costs for general users and lead to loss of real-time performance capability due to increased computation time. In this study, we propose an indoor location estimation approach using only mobile devices and perform trajectory estimation based on multiple data types obtained from the posture and sensors of the mobile device while the user is walking. The proposed approach has low deployment cost for general users. It does not use indoor location fingerprinting or machine learning, allowing for real-time estimation. In addition, it implements an algorithm that is insensitive to geomagnetic effects from other devices, meaning that it can be used in any environment. Based on the positional relationship between a pedestrian and a mobile terminal, we propose a walking trajectory and location estimation system that divides the walking method into walking while looking at a screen (hand-held walking) and walking while holding a mobile device by hand (hand-swinging walking). In particular, we implement the proposed algorithm for estimating the walking trajectory during walking, then evaluate the trajectory and errors by conducting actual experiments.
## 2 Detailed Model of MMC-Controlled Wind Turbine Generator
### MMC System
#### 2.3.5 Phase Shifted Pulse Width Modulation (PS-PWM)  
In general, modulation techniques for multilevel converters can be summarized in three categories: Multi Carrier Pulse Wave Modulation (MC-PWM), Nearest Level Modulation (NLM), and Space Vector Modulation (SVM) [29; 30]. The multi-carrier Phase Shifted Pulse Wave Modulation (PS-PWM) technique is considered in this study due to its merits compared to other approaches. The PS-PWM technique is more effective and superior in controlling the MMC, that is, the power distribution over the entire sub-modules can be provided, and voltage balance at the sub-module capacitors can be achieved. Figure 11 shows the process of the PS-PWM technique. Each sub-module has an independent carrier signal in which the reference signal is distributed to all the series sub-modules in each leg. The goal of the modulation is to produce a PWM signal for switching the IGBT gate on each sub-module. The number of the carrier signal applied depends on the level of the MMC, and in this case, it is N\(-\)1, where N is the level of the MMC. The phase shift (\(\phi\)) between the carrier signals can be obtained through \(\phi\) = 360\({}^{\circ}\)/(N \(-\) 1) [31]. The frequency and amplitude of all carrier signals should be equal. As the SM capacitor voltage balancing controller is applied to each SM, the PS-PWM with individual capacitor voltage control technique [28] is considered.
# Research on Material Viscoelasticity and Its Influence on Indentation Rolling Resistance
## 2 Related Studies  
[PERSON] and [PERSON] (2008) assessed indentation rolling resistance using the finite element analysis method [10]. In addition, these scholars conducted experimental research to explore the indentation rolling resistance between the steel core conveyor belt and the roller using a testing device obtained from the University of New Zealand in Australia [11]. In 2009, in a previous study, these researchers reported that different fabric layers can affect the outcome of indentation rolling resistance [12]. In addition, [PERSON] and [PERSON] et al. have widely investigated indentation rolling resistance and reported insightful results. Their research mainly comprises leveraging the viscoelastic characteristics of conveyor belts and energy consumption method to derive the calculation formula for indentation rolling resistance and designing a testing device for evaluating indentation rolling resistance [13; 14; 15; 16; 17]. [PERSON] and [PERSON] et al. (2011) used the finite element method to assess the stress distribution in the contact area as a moving load rolled over a rubber cover layer [18]. [PERSON] and [PERSON] et al. addressed the shortcomings of the method for the calculation of indentation rolling resistance in the LDIN22123 standard [19]. [PERSON] and [PERSON] et al. [20] analyzed the errors in the results obtained from the calculation of indentation rolling resistance using different viscoelastic testing methods. [PERSON] and [PERSON] (2014) from the China University of Mining and Technology delved into extensive simulation analyses of indentation rolling resistance. They established the relationship between stress and strain in the contact area using Fourier series and derived a two-dimensional boundary element discrete equation. Moreover, they proposed an iterative algorithm for solving the boundary element equation [21].  
In summary, extensive research has been conducted on indentation rolling resistance in the past. Several studies have conducted theoretical analyses on indentation rolling resistance and in-depth investigations into viscoelastic properties. These studies provide a valuable reference for further research endeavors. However, there is a gap in the literature regarding the accurate characterization of models of viscoelastic materials. This paper seeks to address this gap by conducting research on the relaxation model of viscoelastic materials based on existing theoretical frameworks. The goal is to apply the characterization model parameters of the materials to calculate the indentation rolling resistance of belt conveyors.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Protocol Operation-Based Routing  
As discussed earlier, protocol operation-based routing can be divided into two distinct categories: swarm intelligence routing and multi-path routing. Network management for UAV-based WSNs is gradually becoming more difficult because this technology is becoming more widespread. The network size, rapidly changing topology, and network complexity inspired algorithms based on swarm intelligence. This kind of algorithm depends on the interaction of a multitude of simultaneously interacting agents. Multi-path routing involves multiple alternative paths in a UAV-based network that can maximize the benefit in terms of fault tolerance, bandwidth, and security.
# A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study
## 5 State-of-the-Art Machine Learning Models  
In this section, we briefly present the state-of-the-art machine learning models which have been established in the literature to address prediction benchmarks. These models will be utilized to base models in order to explore and highlight the performance of the proposed model.  
More specifically, the models are: Decision Tree Regressor (DTR) [23], Gaussian Processes (GP) [24], \(k\)-Nearest Neighbor Regression (\(k\)NN) [25], Least Absolute Shrinkage and Selection Operator (LASSO) [26], Linear Regression (LR) [27], and Support Vector Regression (SVR) [28], which are described below:  
* DTR is a decision tree dedicated for regression problems, which constructs a model tree based on splitting criteria. More analytically, this algorithm develops a tree with decision nodes and leaf nodes, in which the leaves predict the output continuous value utilizing the linear regression algorithm.
* GP constitutes a collection of random variables depending on time or space, in which every collection of those variables has a multivariate normal distribution. The prediction value of this machine learning algorithm is a one-dimensional Gaussian distribution, and it is calculated by the similarity between the training instances and the unseen instances.
* \(k\)NN is a popular machine learning algorithm, which utilizes various distance mathematic formulas to compute feature similarity between each new instance and a predefined number \(k\) of instances in the training data. For regression problems, the output value is defined by the average value of its \(k\) nearest neighbors.
* LASSO is a linear model trained with \(L_{1}\) prior as a regularizer. This algorithm performs both regularization and variable selection in order to enhance the prediction accuracy. Due to its simplicity and efficiency, it has been successfully extended and applied to a wide variety of statistical models.
* LR probably constitutes the most commonly used algorithm for developing an efficient regression model. This prediction algorithm aims to determine the relationship between one or more explanatory (independent) variables and the dependent variable based on the linear mathematical model.  
Figure 2: Architecture of the proposed MNN\({}_{1}\), MNN\({}_{2}\), and MNN\({}_{3}\) models.  
* SVR is a classical machine learning algorithm which is utilized for predicting continuous values. Its main objective is to fit the error within a specified threshold, in contrast to traditional regression algorithms like LR, which focuses on minimizing the error.  
Additionally, the performance of the proposed MNN model was also compared against that of three widely utilized neural network-based models i.e., a fully connected Feed-Forward Neural Network (FFNN), a GRU-based network (GRU), and a Long Short-Term Memory (LSTM) network. Notice in our numerical experiments the hyper-parameters of all regression models were optimized under exhaustive experimentation and are briefly presented in Table 4.
## 4 Datasets
### KTH Human Action Dataset  
The KTH dataset [35] was created by the Royal Institute of Technology, Sweden in 2004. This dataset consists of six types of human actions (walking, jogging, running, boxing, hand clapping and hand waving) performed by 25 actors with 4 different scenarios. Thus, it contains 25 \(\times\) 6 \(\times\) 4 = 600 video sequences. These videos were recorded with static camera and background; therefore, this dataset is also considered relatively simple for evaluation of human activity recognition algorithms. The method proposed in [36] achieved 98.2% accuracy on this dataset, which is the highest accuracy reported so far. The one frame example of each action from four different scenarios is shown in Figure 12.
## 2 Materials and Methods  
### ASE&C Technology  
A new innovative technology, termed Adiabatic Sonic Evaporation and Crystallization (ASE&C), was designed and patented (World International Patent Organization: EP3135635) for contaminated fluid purification using a physical approach, distillation for the separation of liquids and dissolved solids by promoting adiabatic changes and acceleration (Figure 2) [16]. In this case, it completely separated a seawater sample into freshwater and crystallized salts with a cost of energy between 0 and 20 kWh/m\({}^{3}\).  
The specific energy consumption calculated by the ASE&C system is the total amount of power required for internal pumping and the heat exchanger. The required energy quantity for the ASE&C system depends on the complementary energy sources available.  
The system consumes around 19.2 kWh/m\({}^{3}\) without any complementary energy source. If a residual heat source is available, the energy required to boost the water in the circuit is between 3-4 kWh/m\({}^{3}\). However, the energy consumption reaches 0 kWh/m\({}^{3}\) when the ASE&C technology is coupled to a hybrid solar thermal-photovoltaic system (for 24-h operation).  
The ASE&C must be designed strategically to optimize the performance of the entire (zero-liquid discharge) ZLD system while depurating any contaminated fluid [17; 18; 19; 20]. An improper design can reduce the overall efficiency or increase the operating costs.
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 5 Implementation
### Mobile Network Testbed  
To evaluate the framework, we developed a testbed composed of example applications (IoT and video streaming) and software implementations of mobile network's core functions, as well as  
Figure 8: A trust model example.  
emulated User Equipment (UE) and radio access network functions. The testbed produces realistic traffic patterns that were then analysed using machine learning algorithms (See Section 6).  
The testbed's devices comprised (see Figure 9) one laptop computer connected via WiFi and Ethernet to a server in the local laboratory network named Willab. User Equipment (UE), Evolved Node B (eNB), and all Evolved Packet Core (EPC) components were run on the laptop. For those, we used OpenEPC Rel. 6 [61] that implements the EPC components and emulates eNB and UE functionalities in various virtual machines. In our case, the UE, eNB, and Packet Data Gateway (PGW) each had their own virtual machine. The Serving Gateway (SGW) and EPC-Enabler virtual machines contained more than one component, e.g., the SGW's virtual machine supported the Mobility Management Entity (MME) in addition to the SGW. The EPC-Enablers also contained the Media Delivery Function (MDF) server that was used for streaming video to the UE. The virtual machines were connected to each other through virtual network interfaces (net_a/b/c/d in the figure). In our setup, the UE used an Long Term Evolution (LTE) connection to the eNB and the EPC. The OpenEPC also supports WiFi connections, but we did not utilize them in these tests. Traffic can be generated by activating any application in the UE or on the server. We implemented the libcoap [62] client and server to generate IoT traffic that uses the Constrained Application Protocol (CoAP) [63]. The Session Initiation Protocol (SIP) [64] was needed to initiate and control the video streaming sessions over a Real-time Transport Protocol (RTP) [65]. The traffic data from each interface was captured with Wireshark [66]. The captured packet traces can then be further analysed with security inferencing algorithms from Spark.  
Figure 9: The testbed. UE: User Equipment; SGW: Serving Gateway; MME: Mobility Management Entity; CoAP: Constrained Application Protocol; PGW: Packet Data Gateway; DNS: Domain Name System; HSS: Home Subscriber Server; MDF: Media Delivery Function; LTE: Long Term Evolution; IMS: Internet Protocol Multimedia Subsystem.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 4 The Immersive Video ME App  
The i-EVS framework, developed by Italtel, can provide immersive video services at the network edge [24]. It consists of two main parts:  
* The i-EVS ME App;
* The i-EVS End-User App.  
The ME app can provide real-time or file-based media content sharing capabilities to consumers. Furthermore, it can manage users and groups, by storing and maintaining updated the related information in an appropriated database [24].  
The end-user app runs on end users' smartphones or tablets, where it can be downloaded on demand. It offers the possibility to register to i-EVS, and to create groups of users who can share media contents, off-line or in real-time. The main features of the i-EVS ME app and the end-user app, as well as their interactions, are briefly summarized in the following paragraphs.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 3 Scenarios and Dataset Generation
### Scheduling Policy Impact  
The scheduler in cellular networks is responsible for adapting the MCS to the channel conditions. Furthermore, in Orthogonal Frequency-Division Multiple Access (OFDMA)-based cellular networks the scheduler also assigns physical resources to traffic flows. Therefore, the scheduling policy may have a strong impact on the actual transmission rate and on the RLC buffer variation, which is our congestion indicator. In the following we depict the evolution of the parameters that we monitor, to analyze how they are affected by the scheduling policy. In particular, we have used two different schedulers. The first one is a greedy scheduler that allocates radio resources, according to the RLC buffer status and the corresponding MCS, to maximize the throughput, while the second one is the well-known max-weight algorithm [42].  
Figures 2 and 3 show the instantaneous evolution of the different parameters obtained using both schedulers for the very same wireless channel instance. The results have been obtained during 3 s, in a scenario where the user is 30 m away from the base station, and the traffic rate is set to 50 Mbps. We represent the amount of data sent to the physical channel and the RLC buffers' occupancy in kB, as well as the packet delay and IaT in milliseconds.  
As can be observed in Figure 2, the greedy scheduler sends the traffic to the physical channel in bursts, leveraging those periods with good channel conditions (i.e., line-of-sight). This yields a clear variation of the RLC buffer occupancy, which increases when the transmitted traffic is low, and gets empty periodically, concurring with longer physical transmissions. In turn, the results also evince that the delay observed at the transport layer follows a similar pattern to that of the RLC buffer. Opposed to that, the IaT exhibits a less predictable behavior. The relationship of the IaT with the congestion level will be further analyzed in Section 4.  
Then, Figure 3 shows that the max-weight scheduler yields a different behavior. Again, the traffic is sent in bursts, but with different periodicity and burst duration. As can be seen, it leads to different patterns of the RLC buffer length, as well as of the parameters measured at the transport layer. Nevertheless, in both cases the delay seems to follow the buffer growing trend while the IaT presents a different behavior