esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 3 Optimization of Design Parameters for an LSTM-Based Predictive Maintenance Model
### Feature Selection for HI
#### 3.2.2 Choosing the Fittest Feature Vector  
After filtering significant features by using correlation analysis, we begin considering all possible combinations of remaining features as follows. First, we define feature vectors containing one or more features, which are candidates for HIs. Then, we train a benchmark LSTM model by using the features included in each feature vector as an input to calculate the accuracy of trained LSTM, while setting hyperparameters to specific values. We compare the resulting accuracy values obtained by using feature vectors, and the feature vector with the largest accuracy is selected as the HI. This procedure is described in Figure 5. In this example, Features 2 and 7 are selected as the fittest feature vector. In general, screening is not suitable for feature selection, due to its exhaustive nature. If there exist \(m\) features, computational complexity to select the fittest set of features is \(O(2^{m})\), which is exponentially proportional to the number of features. However, we carry out correlation analysis to remove surplus features and restrict the maximum length of feature vector to \(n\) to avoid the excessive search for feature space. As a result, we can devise a small number of features \(m_{small}\) and visit only \(\sum_{i=1}^{n}\ m_{i}C_{i}\) feature vectors. This procedure is valid for datasets containing a small number of features or many related features.
# Design of a Flat-Panel Metasurface Reflectarray C-Band Antenna
## 4 Conclusions  
This paper presented a detailed design process for a flat-panel metasurface reflectarray and a design example operating in the C-band for satellite applications. A detailed automated design procedure based on the phase compensation and the proposed design method was demonstrated. The designed metasurface reflectarray was composed of a 16 \(\times\) 16 dual-ring unit-cell array. The designed reflectarray structure was simulated using the FE-BI domain decomposition method to save computation resources such as time and memory. The measured antenna gain and radiation patterns were in good agreement with the proposed design method.  
The proposed automated design method is scalable to any other frequency band. This work could pave a new way for fast design and prototyping of a metasurface reflectarray that has a broad application spectrum. For instance, artificial satellites or mobile communication areas could be good applications for the proposed reflectarray antenna.  
Conceptualization, S.K. and K.Y.P.; methodology, G.K.; validation, C.-M.L.; writing--original draft preparation, G.K.; writing--review and editing, M.H. and H.J.; supervision, S.K.; project administration, K.Y.P.; funding acquisition, C.-M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Agency for Defense Development, Republic of Korea (grant number: UD210003 ED).  
Not applicable.  
All data have been included in the paper.  
The authors declare no conflict of interest.
# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 3 Ray Tracing  
### Algorithm Principles  
Ray Tracing (RT) is a method of simulating high-frequency electromagnetic waves in the research environment into light waves. Combined with the consistent diffraction theory, it is widely used to study the propagation of high-frequency electromagnetic waves [32; 33; 34; 35]. On the premise that the signal frequency belongs to the high frequency band, the signal can be regarded as a ray propagating along a straight line in space and interface. The pseudolite signal adopts the L frequency band, which belongs to the ultra-high frequency band, so it conforms to the theoretical premise of ray tracing.  
Specifically, the pseudolite is abstracted into a point source that emits electromagnetic rays in all directions, and electromagnetic parameters are calculated for each propagating ray by electromagnetic calculation methods, as shown in Figure 4. After the ray reaches the receiving end R, the detailed information such as the arrival amplitude, propagation delay relative to the first path, arrival phase, and arrival angle of each ray is obtained, then the vectors of all arriving rays are superimposed to obtain the simulation data of indoor pseudolite signals at the receiving point.  
Ray tracing is a deterministic calculation method using [PERSON]'s equations, all electromagnetic rays are traced back to the emission source according to the imaging theory, and the computational efficiency of the model is highly dependent on the complexity of the environment. The direct transmission of rays is also called LOS. When the rays are reflected on the surface of the object, the direction of the generated signal rays is determined by  
Figure 4: Mirror ray propagation process.  
the reflection and refraction characteristics of the surface material [36], and the energy is calculated by the Fresnel equation. Similarly, when rays penetrate an object (transmission) or diffract and scatter at the edges of the object, the Fresnel equations are combined with imaging theory to determine the energy and direction of the resulting rays.
# A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks
## 6 Case Study  
### Case Network  
We provide a SDN-MN case based on the MobileFlow architecture [10] and [PERSON]'s solution [30] as an example of a security assessment. Based on [10], we construct the whole network layers and some main network components special to SDN-MN. Based on [30], we detail the components of the MobileFlow controller. As shown in Figure 8, the network is divided into three layers. The application layer includes various applications utilizing the SDN-MN controller to manipulate the network infrastructure. The control layer includes the SDN-MN controller components, such as application interface, service compiler, network compiler, _etc._ The infrastructure layer includes the radio interface, deep packet inspection, MobileFlow forwarding engine, user equipment, _etc._  
We suppose some of the function modules in the network are vulnerable. The vulnerable modules include the \"Administrator Workstation\" module, \"Northbound Interface\" module, \"Network Configuration\" module, and \"Radio Interface\" module. The vulnerabilities and their details are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Node Name** & **CVE\#** & **Detail** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{“Administrator Workstation” module} & & \multicolumn{2}{c}{Vulnerability that allows remote users to execute arbitrary code in some Serv-U versions.} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Vulnerability that allows} \\  & & & \multicolumn{2}{c}{VVE-2004-1992} & remote attackers to execute DoS in some Serv-U versions. \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{“Northbound Interface” module} & & \multicolumn{2}{c}{VVE-2003-0533} & Stack buffer overflow in Active Directory service. \\ \cline{3-4}  & & & \multicolumn{2}{c}{VVE-2004-0417} & Integer overflow in some CVS versions. \\ \cline{3-4}  & & & \multicolumn{2}{c}{VVE-2004-0415} & Vulnerability that allows local users to access portions of kernel memory. \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{“Network Configuration” module} & & \multicolumn{2}{c}{Vulnerability that allows remote attackers to execute DoS and execute arbitrary codes.} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Vulnerability that allows} \\ \cline{3-4}  & & & \multicolumn{2}{c}{VVE-2002-0392} & Vulnerability that allows remote attackers to execute DoS and execute arbitrary codes. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of vulnerabilities in case network.  
Figure 8: Case of Software-Defined mobile network.  
To explain the influence toward the security assessment results of dynamic factors, we define two schemes. The two schemes differ in the decision matrix. This means certain actions affect the security differently in different environments.
## 2 Applications
### Others  
With the advancements in UWSNs, increasing number of IoUT applications are becoming attractive, such as sports [25], navigation [26], and localization [27] applications. Note that the localization applications are particularly challenging yet very valuable. Specifically, in the real world, identifying the location underwater is challenging because Global Positioning System (GPS) cannot work in underwater environments. Imagine, for example, underwater sensors can be used as location reference points, and thus can provide swimmers, divers, ships, and underwater vehicles with valuable location information.  
* The authors in [25] provide a systematic review, focusing on the biomechanical analysis of swimming by using the inertial sensors underwater. As mentioned in this paper, in recent years, the swimming athletes, coaches, and sport scientists have been working harder and harder to achieve a \"fraction of a second\" improvement. The use of sensors plays an important role in supporting performance enhancement from a biomechanical point of view. This paper introduces numerous existing methods that use wearable inertial sensors (including accelerometers, gyroscopes, and magnetometers) to assess the biomechanics of swimming performance. The results indicate that the underwater inertial sensors are suitable and reliable tools for swimming biomechanical analyses.
* As highlighted in [26], for a variety of applications, the sensed data can only be interpreted meaningfully when referenced to the location of the sensors; for example, navigation is such an application. The authors propose a multi-stage AUV-aided localization scheme for UWSNs. They conducted simulations to evaluate the performance of the proposed scheme in terms of the localization coverage, accuracy, and communication costs. The results indicate that good performance can be achieved by properly choosing the communication range.
* In [27], the authors propose an Anchor-Free Localization Algorithm, called AFLA, for underwater environments. Most of the traditional underwater localization algorithms assume that special underwater nodes (with special equipment) or AUVs can be used as \"anchor nodes\" to help identify the location underwater. The proposed AFLA does not need information from the special anchor nodes. Instead, AFLA uses the adjacent relationship of normal sensor nodes to identify the location underwater. The simulation results indicate that AFLA can be used in both the static and dynamic UWSNs.
## 5 Results and Discussion  
### The Validation Case  
The core of the computational model was developed previously for E-gasifier [4]. As indicated in Table 6, the comparison has been made with the NETL's modeling results [38]. The difference for H\({}_{2}\)O is 5.85 percentage points, while the differences of other species are less than one percentage point. Syngas composition consists mainly of CO and H\({}_{2}\), for which the comparison in Table 6 confirms that the preliminary CFD model is appropriate for predicting gasification behavior.  
There is no data available in Ref. [38] for direct comparison in terms of outlet syngas temperature; nevertheless, the predicted exit temperature seems, from empirical judgment, somewhat higher than practical value in entrained-bed gasifier. Further examining the system process in the literature reveals that there is a cooling design by partially recycled clean syngas downstream from the gasifier exit, which moderates the raw syngas temperature to a value, complied with the operating specification of syngas cooler. Such kind of cooling design is not implemented in the present CFD model, due to the fact that iterative scheme and more CPU time would be needed; moreover, the more complicated  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Flow rate (tons/day)** & \\ - **Coal (as received):** & **2545** \\ - **Slurry water:** & **831** \\ - **Coal slurry:** & **3376** \\ \hline First Stage Flow rate (tons/day) & \\ - Oxidant: & 1947 \\ \hline Second Stage Flow rate (tons/day) & \\ - Oxidant: & 3376 \\ \hline Operating Condition: & \\ - & Pressure (atm): & 28/70 \\ - & Inlet Oxidant Temp. (K): & 411 \\ - & Inlet Feedstock Temp. (K): & 450 \\ \hline Operating Parameters: & \\ - & O\({}_{2}\)/Coal (DAF): & 0.92 \\ - & Coal (MF)/Coal Slurry: & 0.67 \\ - & O\({}_{2}\)/C: & 1.14 \\ - & H\({}_{2}\)O/C: & 0.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feedstock and operating conditions of the base case.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Particle diameter (\(\mu\)m)** & **Minimum** & **10** \\
**Maximum** & **50** \\
**Mean** & **30** \\ \hline Particle distribution &
\begin{tabular}{c} \(\mathrm{Rosin}\)–\(\mathrm{Rammler}\) \\ \(\mathrm{Density}\) (kg/m\({}^{3}\)) \\ \(\mathrm{Specific}\) heat (J/kg-K) \\ Thermal Conductivity \\ (W/m-K) \\ Emissivity \\ Scattering Factor \\ \hline \hline \end{tabular}
\end{table}
Table 5: Properties of coal particles.  
computational model might cause additional artificial error. It is estimated that the exit temperature in the NETL's report is around 1600 K.
## 2 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound** & **LRI** & **ID \#** & **Aroma** & **Area** & **REF** \\ (_E,Z_)-3.5-Octaiden-2-one & 1180 & A, B, D &
\begin{tabular}{c} Fruity, fatty, \\ mushroom \\ \end{tabular} & \(11.2\pm 0.2\) & [24,29,31] \\ \hline _Acids_ & & & & \\ \hline Acetic acid & 591 & A, B & Sour, pungent & \(326\pm 2.4\) & [22,29,31,33] \\ \hline Propanoic acid & 713 & A, B & Pungent, acidic & \(22.3\pm 0.5\) & - \\ \hline
2-methyl-Propanoic acid & 793 & A, B & Acidic, sour & \(1.69\)\({}^{\text{a}}\) & - \\ \hline
3-methyl-Butanoic acid & 896 & A, B & Sour, cheesy & \(4.8\pm 0.1\) & - \\ \hline Hexanoic acid & 1041 & A, B &
\begin{tabular}{c} Sour, fatty, \\ sweet, cheesy \\ \end{tabular} & \(12.3\pm 0.3\) & - \\ \hline
2-methyl-Propanoic acid & 793 & A, B & Acidic, sour & \(1.69\)\({}^{\text{a}}\) & - \\ \hline
3-methyl-Butanoic acid & 896 & A, B & Sour, cheesy & \(4.8\pm 0.1\) & - \\ \hline Hexanoic acid & 1041 & A, B &
\begin{tabular}{c} Sour, fatty, \\ sweet, cheesy \\ \end{tabular} & \(12.3\pm 0.3\) & - \\ \hline _Alkanes_ & & & & \\ \hline Tridecane & 1269 & A, B & - & \(3.7\pm 0.1\) & [19] \\ \hline
3-methyl-Dodecane & 1355 & A, B & - & \(0.73\)\({}^{\text{a}}\) & - \\ \hline Tetradecane & 1438 & A, B, C & Waxy & \(1.96\)\({}^{\text{a}}\) & [24,28] \\ \hline _Others_ & & & & \\ \hline Pyrrole & 812 & A, B & Sweet, nutty & \(3.6\pm 0.1\) & - \\ \hline Methoxy-phenyl-oxime & 951 & A, B & - & \(4.6\pm 0.2\) & - \\ \hline
2(5H)-Furanone & 1001 & A, B & Buttery & \(3.0\pm 0.1\) & [31] \\ \hline
2-pentyl-Furan & 1081 & A, B & Fruity, green, earthy & \(3.1\pm 0.1\) & [20,24,26,29,30] \\ \hline
1-Tridecene & 1499 & A, B & - & \(1.12\)\({}^{\text{a}}\) & [28] \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound** & **LRI** & **Identification \#** & **Aroma** & **Area** & **REF** \\ \hline \multicolumn{5}{c}{_Aleohols_} \\ \hline
1-Pentanol & 831 & A, B, C & Pungent & \(34.1\pm 0.4\) & [20,21,24,26,25,26] \\ \hline (_R,R_)-2,3-Butanediol & 848 & A, B, D & \
\begin{tabular}{c} Creamy, fruity, buttery, \\ green, leafy \\ \end{tabular} & \(16.6\pm 0.3\) & [26,27] \\ \hline (_R,S_)-2,3-Butanediol & 859 & A, B, D &
\begin{tabular}{c} Creamy, fruity, buttery, \\ green, leafy \\ \end{tabular} & \(45.8\pm 0.5\) & [26,27] \\ \hline
1-Octen-3-ol & 1065 & A, B &
\begin{tabular}{c} Mushroom, \\ earthy, green \\ \end{tabular} & \(49.5\pm 0.6\) & [10,24,29] \\ \hline
2-ethyl-1-Hexanol & 1112 & A, B, C &
\begin{tabular}{c} Cittrus, fresh, \\ floral, sweet \\ \end{tabular} & \(5.3\pm 0.1\) & [10,20] \\ \hline \end{tabular}
\end{table} 
Table 3: VOCs of the bleached melon pomace after 24 months of storage (M_B_O_t24) identified through HS-SPME-GC-MS analysis, grouped by chemical classes. Data are expressed as mean (\(n=3\)) TIC area \(\times\)\(10^{6}\)\(\pm\) SD\({}_{\text{(3)}}\). Figure 4 shows the HS-SPME-GC-MS chromatogram of a sample of bleached melon pomace fiber, oven dried and stored for 24 months in a closed vessel, in the dark, at room temperature. Table 3 reports its descriptive features.
## 2 Materials and Methods
### Data Analysis  
After data collection, kinematic and force plate data were processed offline using a custom-made program written in MATLAB(tm) (Version 5.3 (R11), The MathWorks Inc., Natick, MA, USA). Data were low-pass filtered using a Butterworth filter with a 15 Hz cutoff frequency for kinematic data [23] and a 10 Hz cutoff frequency for kinetic data [24]. The ML and anteroposterior (AP) coordinates of the centre of pressure (yP and xP, respectively) were computed from force plate data in accordance with the instructions from the manufacturer (AMTI) as follows:  
\[\mathrm{yP} = \frac{\mathrm{Mx}+\mathrm{Fy}\times\mathrm{dz}}{\mathrm{Fz}}, \tag{1}\] \[\mathrm{xP} = \frac{-\mathrm{My}+\mathrm{Fx}\times\mathrm{dz}}{\mathrm{Fz}}, \tag{2}\]  
where Mx and My are the moment around the AP and ML axes, respectively; Py, Fx and Fz are the ML, AP and vertical ground reaction forces, respectively; and \(\mathrm{dz}\) is the distance between the surface of the force plate and its origin.  
Instantaneous acceleration of the centre of mass along the AP, ML and vertical axes was determined from the ground reaction force according to [PERSON]'s second law. The centre of mass velocity and displacement were computed by the successive numerical integration of centre of mass acceleration using integration constants equal to zero, namely, initial velocity and displacement null [3].  
Gait initiation onset (t0) and swing-foot contact were determined from force plate data. Since gait initiation onset did not necessarily occur simultaneously in the ML and AP axes, two t0 times were estimated, one for each axis. T0y and t0x corresponded, respectively, to the instants when the ML and AP centre of pressure trace exceeded 2.5 standard deviations from its baseline value, respectively [24]. The one occurring first was considered t0. Swing-foot contact corresponded to the instant when the ML and the AP centre of pressure trace shifted abruptly towards the swing-leg side and forward, respectively. Swing heel-off and swing toe-off were determined from VICON data. They corresponded to the instants when the swing heel marker's vertical position and the swing toe marker's anterior position exceeded their position in the initial static posture by 3 mm.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 3 Results
### Signal Acquisition Results  
Several algorithms were simulated and validated in terms of detection probability and acquisition results according to the acquisition strategy. The results and analysis are presented below.  
We compared the detection probabilities of the various methods mentioned above and the results are shown in Figure 13. At carrier-to-noise ratios below 20 dB-Hz, non-coherent integration and semi-bit coherent integration can barely detect the BDS signal, while conventional differential integration has a 10-20% probability of capturing it. This shows that the differential integration method is more effective in suppressing white noise. When the carrier-to-noise ratio is greater than 30 dB-Hz, the semi-bit coherent integration algorithm shows its superiority, as the noise interference is lower and coherent integration can effectively amplify the signal gain. The semi-bit coherent differential integration method that combines these algorithmic features performs much  
Figure 15 shows the correlation functions of various algorithms on the code-frequency search grid for PRN 20 as an example. Simulations were run with a Doppler frequency \(\hat{f}_{d}\) of \(-857.68\) Hz and a code phase of 506 chips. It can be observed that the non-coherent integration and the semi-bit coherent integration do not acquire the signal, i.e., there is no significant peak, while the peaks appear in the correlation function plots of the CODI and SBCODI methods. Furthermore, although the coherent differential integration algorithm detects the signal, there are still many peaks, while in the SBCODI method, there is only one distinct peak. Thus, the SBCODI can detect the BDS B1I signal with an ultra-low carrier-to-noise ratio.  
Figure 14: Peak metric values and detection status for each satellite searched by NCBI, CODI, SBCOI, and SBCODI methods for \(T_{coh}\) = 200 ms (**left**) and \(T_{coh}\) = 400 ms (**right**). (Not acquired signals (blue) and acquired signals (green)).
## 2 Materials and Methods
All of the RBFDPs were cemented with dual-cured resin cement (Relyx Ultimate, 3M, Milano, Italy) and polymerized for 60 s using led lamp (400-500 nm), but with different procedures. Air abrasion was performed with 50 \(\upmu\)m Al\({}_{2}\)O\({}_{3}\) particles at a 2.5 bar pressure for 15 s at a distance of 10 mm on the wings of RBFDPs in zirconia. After drying, the wings were cleaned with 96% isopropanol for 3 minutes. The bonding (Scotchbond Universal Adhesive, 3M, Milano italy) was applied on the wings for 20 s and polymerized for 60 s. Air abrasion was performed with 110 \(\upmu\)m Al\({}_{2}\)O\({}_{3}\) particles at a 2.5 bar pressure for 15 s, at a distance of 10 mm on the wings of RBFDPs in PMMA-based material with ceramic fillers. After drying, the primer (Visiolink, Bredent, Seden, Germany) was applied on the wings and polymerized for 60 s. Instead, the wings of RBFDPs in lithium disilicate were etching with hydrofluoric acid (Ceramic Etching gel, Ivoclar Vivadent, Bologna Italy) for 60 s, 60 s of rinsing, and 30 s of drying. The bonding (Scotchbond Universal Adhesive, 3M, Milano, Italy) was applied for 20 s and polymerized for 60 s. All procedures were performed according to the manufacturer's instructions. The samples were numbered through the engraving on the resin of a code indicating the material. All samples were mounted on a 30\({}^{\circ}\) angled support of a universal testing machine (Acumen 3, MTS Systems Corporation, Eden Praire, MN, USA) and a static loading at a crosshead speed of 1.5 mm/min, until failure to the incisal edge of the pontic tooth was performed to simulate real situation (Figure 5). The load was transferred through a 6-mm diameter steatite ball in the middle of the incisal edge of the pontic tooth until failure [25]. Four types of failure were recorded: debonding of RBFDPs, fracture of the connector, debonding of the RBFDPs with fracture of the abutment tooth, and fracture of the abutment tooth with the RBFDP still bonded. All of the methods were synthetized in the following flowchart (Figure 6). Figure 4: RBFDPs in lithium disilicate fabricated with wax lost technique. Figure 5: A sample mounted on a 30\({}^{\circ}\) angled support of the universal testing machine. Figure 6: Flowchart of the methods. The Kruskal-Wallis test with a post hoc analysis using [PERSON]'s test was used to compare the three groups. The level of statistical significance was set as \(\alpha\) =0.05 and statistical power of 80%. A statistical software (SPSS v16.0; SPSS Inc., Chicago, IL, USA) was used for the analysis.