esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### SISO and MIMO  
According to the aforementioned findings, transmit diversity improved performance, and receive diversity improved performance compared to an SISO channel without any diversity. Therefore, we implemented transmit and receive diversity at the transmitter and receiver, respectively, to increase the system's performance. Figure 4 displays the effectiveness of the wavelet-based channel diversity strategy for SISO and MIMO in terms of BER. At the same BER, the SNR value was 4 dB at \(10^{-2}\) for the MIMO DWT and 20 dB for the SISO DWT. This performance gain difference was 16 dB because the antenna diversity with a two-element ULA with half-length spacing was applied at both the transmitter and receiver. The two-element ULA with half-length spacing was only considered because having less complex wavelet transforms leads to more complexity in the model at both the transmitter and receiver sides during the construction and reconstruction of data processing, as mentioned in Section 3.2. The MIMO DWT had an SNR value of 11 dB at \(10^{-3}\), which was much better than that of the SISO DWT. The simulation parameters, for which the QAM modulation scheme was used and the dB8 wavelet family was considered, are given Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Parameters** & **MISO DWT** & **SISO DWT** \\ \hline Modulation Scheme & QAM 16 & QAM 16 \\ DWT family & dB8 & dB8 \\ Type of channel & AWGN & AWGN \\ Number of antennas & 2 \(\times\) 1 & 1 \(\times\) 1 \\ SNR range & [0:20] & [0:20] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters for SISO and MISO.  
Figure 4: MIMO diversity with DWT-OFDM.
## 4 FWA Design Considerations, Challenges, and Solutions
### Beamforming Approach
#### 4.2.2 Hybrid Approach  
There are a number of researches that have shown that massive MIMO system performance improves in accordance with the number of deployed antennas at the Tx and Rx. Moreover, a large antenna system can be effectively used in a small area by exploiting the associated mm-wave communication system's small wavelength for performance improvement. This scheme can offer an adequate amount of array gain for compensating wireless communication systems for severe signal attenuation, which is a result of penetration loss, path loss, atmospheric absorption, and rain effect. Meanwhile, in a typical MU-MIMO system, BF is normally achieved in the digital domain. In this approach, a dedicated RF chain is desirable for each deployed antenna element. However, there have been many concerns regarding cost, complexity, calibration, and power constraints that are prohibitively high and may make having a dedicated RF chain for each antenna unrealistic. This is even more impractical in the mm-wave MIMO schemes with envisaged massive antennas. Therefore, the employment of typical DBF schemes for massive antenna arrays may be impractical. In practice, to address the drawback, a hybrid scheme in which the analog and digital processes are combined has been presented as an attractive option for massive MIMO deployment [95; 98; 101; 314; 315; 316; 317; 318; 319].  
In a hybrid BF (HBF), the architecture can be efficiently achieved using a two-level/stage BF scheme. As illustrated in Figure 18, in this scheme, the strategic nexus of a low-dimensional DBF and a high-dimensional RF analog BF (ABF) is employed. The first-level ABF signal processing can be accomplished through a low-cost analog Phase Shift Network to reduce the number of RF chains. As there is no need for an RF chain in the processing, the signal dimension can be reduced significantly. Afterward, the postprocessed signals are sent to a typical second-level DBF with considerably reduced dimension. Consequently, a well-designed HBF scheme is not only capable of duly supporting mm-wave MIMO systems but can also offer comparable performance to DBF at reduced complexity and power consumption [264; 314; 315; 318; 320].  
Furthermore, a hybrid approach is capable of meeting deployment scan range requirements for both suburban and dense urban as aforementioned. Consequently, the hybrid approach seems to be more promising. However, with different technological advances, it is believed that an all-digital solution will equally be more attractive. In addition, DBF is expected to be economical regarding cost and power than the RF BF [264; 307]. A survey on HBF techniques in 5G is given in [314]. Furthermore, further reading on large-scale antenna schemes with HBF for mm-wave 5G is available in [315].
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 3 Materials and Methods  
Existing localization methods, which were introduced in Section 1, typically use a database that stores the characteristics of the target site, such as radio maps, and use a lazy-learning method that determines the location by comparing the database with the signal values scanned in real time. To use all the data given in real time, the localization result of each sensor should be completed before the localization result of the fastest sensor is estimated. However, the localization results of each sensor may exceed this time, as shown in Figure 1. In other words, because all the received data cannot be used, there may be a decrease in accuracy. The method proposed in this study uses an eager learning technique that determines the location only by inference of the input value. RHLS is a method that can reduce the positioning computation time caused by large-scale learning data and can solve the accuracy degradation caused by mismatching the positioning cycles of multiple sensors.
## 3 Results
### Preprocessing
#### 3.1.3 Analysis of Variance  
The location and season variables are categorical variables. First, the measurement location variables were created using the one-hot encoding method (a method for expressing categorical variables as 0 and 1) to generate in-pig barn (In), out-of-pig barn (Out), and boundary (Boundary) variables. The season variables were divided into four seasons: spring  
Figure 4: Correlation plot; (**left**) correlation bar plot of explanatory variables about response variables; (**right**) heatmap of data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **Correlation** & **VIF** & **Variables** & **Correlation** & **VIF** \\ \hline Ammonia & 0.50 & 2.47 & Butricy acid & 0.35 & 20.40 \\ Hydorgen sulfide & 0.27 & 1.42 & Iso-valeric acid & 0.28 & 34.93 \\ Methyl mercaptan & 0.39 & 1.53 & Valeric acid & 0.24 & 26.34 \\ Dimethyl sulfide & 0.02 & 1.22 & Phenol & 0.29 & 6.52 \\ Dimethyl disulfide & 0.11 & 1.48 & p-Cresol & 0.42 & 4.86 \\ Acetic acid & 0.50 & 8.82 & Indole & 0.09 & 1.07 \\ Propionic acid & 0.46 & 58.70 & Skatole & 0.07 & 1.10 \\ Iso-butyric acid & 0.58 & 6.35 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation analysis and variance inflation factor (VIF) results. (March-May), summer (June-August), fall (September-November), and winter (December-January), and then one-hot encoding was applied, as shown in Figure 5. However, in the case of winter, no corresponding data were present; therefore, it were removed. An ANOVA was performed to determine whether the average amount of occurrence differed for each categorical variable generated in this way, and whether the generated categorical variable was meaningful as a variable. As shown in Table 6, the subscripts (A, B, and C) above the numbers grouped the mean of each category in the case of variables with significant differences in mean. For example, in the case of hydrogen sulfide, it can be said that there is no difference between the categories In and Out because the averages of the two categories are in the same group. On the other hand, since the Boundary is a different group from In and Out, the difference can be said to be significant. As shown in Table 6, the location variables showed no difference in the average amount of occurrence per location for the four variables (DMS, DMDS, ID, and SK). For the variables excluding these four variables, a difference was observed in the average amount of occurrence per location (\(p\)-value < 0.05). Regarding the average amount of occurrence by location, In was the highest for complex odor, NH\({}_{3}\), ACA, PPA, BTA, IVA, VLA, and p-C, and was found to decrease as the distance increased. H\({}_{2}\)S and IBA were similar for In and Out, but decreased for Boundaries. Finally, MM and Ph were high for In, Out, and Boundary and appeared at a similar level.
###### Abstract  
The network slicing of physical infrastructure is required for fifth-generation mobile networks to make significant changes in how service providers deliver and defend services in the face of evolving end-user performance requirements. To perform this, a fast and secure slicing technique is employed for node allocation and connection establishment, which necessitates the usage of a large number of domain applications across the network. PROMETHEE-II and SLE algorithms were used in this study's approach to network design for node allocation and link construction, respectively. The PROMETHEE-II approach takes into account a variety of node characteristics while constructing a node importance rank array (NIRA), including the node capacity, bandwidth of neighboring connections, degree of the node, and proximity centrality among others. The SLE method is proposed to record all possible link configurations for the network slice request (NSR) nodes to guarantee that the shortest path array (SPA) of the NSR has a high acceptance rate. Performance metrics such as the service revenue and acceptance ratio were considered to evaluate the effectiveness of the suggested approach. The effectiveness of network slicing has been further examined under different infrastructure models to determine whether a small-world network structure is beneficial to 5G network. For each scenario, simulations were carried out and the results were compared to previously published findings from other sources.  
5G network; virtual network embedding; resource allocation; heuristic fuzzy; shortest path
## 2 Reactive Power Supply  
This paper aims to achieve an optimized voltage profile of the grid, reducing power system loss and minimizing reactive compensation costs by optimizing the reactive power supply. Due to the inherent relationship between reactive power and the voltage profile, optimizing the reactive power flow and reactive power supply improves the voltage profile. Thus, the reactive power supply should be determined using the best method. Maximizing the output reserve reactive power of the generators, maximizing the line charge and minimizing costs are the three main goals of reactive power compensation. Three different methods have been presented in which the costs corresponding to the best operation mode of the network are determined. The costs include the total cost of supplying reactive power by various resources, including generators, the line charging capacitance, installed capacitors and reactive power compensators, and the costs of active power loss. The three methods of the reactive power supply include:1. Maximizing the output reserve reactive power of the generators: increasing the reactive reserve power or reducing the reactive output power that increases the dynamic voltage stability. 2. Maximizing the line charge: increasing the reactive output power of the inherent reactive power generation resources or the lines' capacitance. 3. Minimizing the costs: the total cost of reactive power supply might be minimized by observing the system constraints. Reducing voltage deviation, increasing voltage stability and reducing active power loss are three methods for improving the voltage profile [13].
## 5 Results
### Bit Rate Coverage Results
For example, results show that depending on the subscriber activity, the sum bit rate in the DS direction corresponding to the coverage of 80% varies from \(\pm 9\%\) with respect to the bit rate obtained for \(p=50\%\) which is about 1.45 Gbit/s for \(d_{OE}=200\) m. Similar considerations apply to the case of US direction. Variability of fronthauling bit rate due to changing FEXT conditions requires the radio node to adapt to the available fronthaul rate as indicated in the previous Section.
## 3 Network Background
### Ultra-Dense 5G/6G Cellular Networks
#### 3.3.4 UND in 6G Cellular Networks  
The 6G ultra-dense cellular networks leverage large-scale MIMO antennas and mmWave communication technologies to enable gigabit-level wireless traffic. Massive MIMO uses hundreds of antennas per BS, enhancing the network capacity while requiring a reduction in the transmit power, which in turn reduces the coverage radius of each BS [45]. mmWave technology further enhances the wireless transmission capacity, operating in the range of hundreds of megahertz. However, due to propagation losses and physical obstructions, its effective transmission range is limited to 100-200 m. As a result, many small cells will be deployed to support ultra-dense network architectures, ensuring seamless connectivity [45; 46]. To achieve extensive coverage, the 6G base station density is required to increase significantly, reaching 40-50 BS/km\({}^{2}\), making 6G cellular networks ultra-high-density systems. This densification introduces architectural challenges, necessitating the exploration of distributed network designs incorporating single- and multiple-gateway models to optimize performance and scalability [5].
## 3 Analysis of Results and Discussion
### Analysis of the Effect of Air Baffles on Explosion Shock Wave Propagation  
According to the simulation results in Section 3.1, combined with the code requirements and engineering practice, the baffle height of 0.5 m and the baffle spacing of 50 m suggested by [PERSON] et al. are appropriate. The addition of gas baffles is an obstacle in the gas cabin, so the effect of the addition of gas baffles on the propagation of the blast wave needs to be studied. To address this issue, this section sets up baffles at 30 m, 40 m, 50 m, and 70 m intervals in turn, as well as without baffles for comparison. The height of the baffle is set at 500 mm, the width at 1800 mm, and the thickness at 250 mm.
## 4 Results  
### Dataset  
In general, the cross-sensor analysis can be viewed as two separate cases: (i) all sensors in the evaluation utilize the same sensing technology, and (ii) the sensing mechanisms differ. We explored both cases and, in this respect, we utilized datasets from LivDet 2013, 2019, and 2021 competitions [18] in our experimental analysis. They consist of live and spoof fingerprint images from five different devices, which differ in scan area and sensing technology (Table 2). The spoof images were collected using cooperative and non-cooperative methods based on the LivDet edition.  
Furthermore, the materials used to fabricate the PAIs vary across the training and the test sets, as reported in Tables 3 and 4. Due to this lack of knowledge of the spoofing nature, we can simulate a true attack scenario, following the typical fingerprint PAD evaluation protocols.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Participant** & **Algorithm Name** & **Type** & **Acronym** \\ \hline \multirow{2}{*}{Dermalog} & LivDet21 ColC2 & Deep-learning & Col \\ \cline{2-4}  & LivDet21 DobC2 & Deep-learning & Dob \\ \hline \multirow{2}{*}{Unesp} & contreras & Hand-crafted & con \\ \hline \multirow{2}{*}{Hangzhou Jinglianwen Tech. Co., Ltd.} & JLWLivDetL & Hybrid & JLW \\ \hline \multirow{2}{*}{MEGVII (BEIJING) Technology Co., Ltd.} & megvii\_single & Deep-learning & m\_s \\ \cline{2-4}  & megvii\_ensemble & Deep-learning & m\_e \\ \hline University of Applied Sciences Darmstadt & PADUnk & Hand-crafted & PAD \\ \hline \multirow{2}{*}{Chosun University} & B\_ld2 & Deep-learning & bld \\ \hline \multirow{2}{*}{Anonymous} & bb8 & Hybrid & bb8 \\ \cline{2-4}  & r2d2 & Hybrid & r2d2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the pre-trained PAD LivDet 2021 competitors used for the analysis.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Scanner** & **Model** & **Resolution [dpi]** & **Image Size [px]** & **Format** & **Type** \\ \hline Biometrika & FX2000 & 569 & 315x372 & PNG & Optical \\ \hline Orcanthus & Certis2 Image & 500 & 300x\(N\) & PNG & Thermal swipe \\ \hline DigitalPersona & U.are.U 5160 & 500 & 252x324 & PNG & Optical \\ \hline GreenBit & DactyScan84C & 500 & 500x500 & BMP & Optical \\ \hline Dermalog & LF10 & 500 & 500x500 & PNG & Optical \\ \hline \hline \end{tabular}
\end{table}
Table 2: Device characteristics for LivDet 2013, 2019, and 2021 datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Training Sets** & **Live** & **Latex** & **RProFast** & **WoodGlue** & **Ecoflex** & **Gelatine** \\ \hline LivDet 2021 GreenBit Training & 1250 & 750 & 750 & - & - & - \\ \hline LivDet 2021 Dermalog Training & 1250 & 750 & 750 & - & - & - \\ \hline LivDet 2019 DigitalPersona Training & 1000 & 250 & - & 250 & 250 & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of samples for each scanner employed in the training phase.
## 3 Linear Extended State Observer (LESO)  
Assume \(\xi_{1}=y\), \(\xi_{2}=y,\ldots,\xi_{\rho}=\xi^{(\rho-1)}\), then,  
\[\left\{\begin{array}{l}\dot{\xi}_{i}=\xi_{i+1}\ i\in\{1,2,\ldots,\rho-1\}\\ \dot{\xi}_{\rho}=f\big{(}\xi_{1},\xi_{2},\ldots,\xi_{\rho},w,t\big{)}+(b(t)-b _{0})u+b_{0}u\end{array}\right. \tag{3}\]  
Consider the additional state,  
\[\xi_{\rho+1}=f+(b(t)-b_{0})u=L \tag{4}\]  
where \(L\) is denoted as the _generalized disturbance_, which comprises all unidentified external disturbances, internal dynamics, and uncertainties. Several methods are used to choose the value of the parameter \(b_{0}\in\mathbb{R}\setminus\{0\}\). In [10], the coefficient \(b_{0}\) is a loose estimation of \(b(t)\) in the range of \(\pm 50\%\), while in [43], the parameter \(b_{0}\) was chosen empirically by the user as a design parameter.  
**Assumption A2.**_There exists an upper bound for the time derivative of the generalized disturbance_ (_i.e._, _at least \(L\in C^{1}\) and \(sup_{t\in[0,\infty)}|L|=M<\infty\) where \(\in\mathbb{R}\)_).  
Based on Assumptions A1 and A2, a \((\rho+1)^{th}\) order LESO can be designed to estimate both the generalized disturbances and the states of the system given in (2) following the method presented by [44],  
\[\left\{\begin{array}{l}\dot{\xi}_{i}=\dot{\xi}_{i+1}+\beta_{i}\big{(}y-\dot {\xi}_{1}\big{)},\ i\in\{1,2,\ldots,\rho-1\}\\ \dot{\xi}_{\rho}=\dot{\xi}_{\rho+1}+\beta_{\rho}\big{(}y-\dot{\xi}_{1}\big{)} +b_{0}u\\ \dot{\xi}_{\rho+1}=\beta_{\rho+1}\big{(}y-\dot{\xi}_{1}\big{)}\end{array}\right. \tag{5}\]  
where \(\beta_{i}\) is an observer gain coefficient to be adjusted and \(i=1,2,\ldots,\rho+1\), \(\big{(}y-\dot{\xi}_{1}\big{)}=e\) is the estimation error. The bandwidth-based and pole placement methods are the traditional techniques to adjust a linear extended state observer. If the aim is to lower the number of coefficients, the observer gain parameters may be expressed as functions of the bandwidth \(\omega_{0}\) of the LESO. The estimator gains are chosen as  
\[\left(\begin{array}{c}\beta_{1}\\ \beta_{2}\\ \vdots\\ \beta_{\rho+1}\end{array}\right)=\left(\begin{array}{c}a_{1}\omega_{0}\\ a_{2}\omega_{0}^{2}\\ \vdots\\ a_{\rho+1}\omega_{0}^{\rho+1}\end{array}\right)\]  
where \(a_{i},i=1,2,\ldots,\rho+1\) are selected in such a way that the characteristics equation \(s^{\rho+1}+a_{1}s^{\rho}+\ldots+a_{\rho}s+a_{\rho+1}\) is stable Hurwitz. Large values of the LESO parameters will increase the adverse effect of the measurement noise, and thus will decrease the system performance. Moreover, large values of the LESO parameters lead to the \"peaking phenomenon\", which is caused by multiplying the estimation error \(e=(y-\dot{\xi}_{1})\) by large values. Therefore, there is a need to adopt a new class of ESO that relieves the effect of the aforementioned issues. Nonlinear ESO is a new class of ESO that manipulates the estimation error \(e=(y-\dot{\xi}_{1})\) in a nonlinear fashion and can alleviate the effect of both measurement noise and the peaking phenomenon.
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.1 Reference Soil Moisture Data
(v) Most of the ISMN sites in the data set are located on relatively flat (non-mountaneous) surfaces with low-to-moderate vegetation cover (such as croplands, grasslands, savannas) for the sake of limiting the incoherent scattering effects in the analysis. The SCAN sites provide daily mean SM (measured from top 2.5-5 cm of the soil), air temperature, and precipitation measurements for annual periods. The OzNet sites provide 20-min SM (for top 0-5 cm of the soil), soil temperature (measured at top 2.5 cm), and precipitation measurements for annual periods. Therefore, their measurements are preprocessed in order to obtain daily averages in this study. Measurements from years 2017 and 2018 for both SCAN and OzNet networks are used in this study since CYGNSS measurements have been made available starting from mid-March, 2017.
## 5 Performance Test Results and Discussion  
The test campaign results are presented in this section.  
Initially, five thermal-vacuum cycles (T-V cycles) were done in order to check the proper temperature behavior. The last T-V cycle, just before the start of the dynamic tests, is shown in Figure 10. The cooling system took almost two days to reach the needed temperature for the superconductors to be operative (\(<\)90 K). However, this long time only applied for the bottom input SMB, whose thermal path was very large and thin. Most of the prototype mass was well below 60 K in about 12 h.  
The vacuum reached before starting the dynamic test was 3\(\cdot\)10\({}^{-3}\) Pa. Different tests were done in order to characterize the mechanical and thermal performance. Some results are shown below.
## 1 Introduction  
### Background  
Many civil structures in service may deteriorate or degrade due to many factors, such as environmental corrosion, long-term fatigue, strong winds, severe earthquakes, and so forth. The failure of these deficient structures could be catastrophic, not only in terms of loss of life and the economy, but also of subsequent social and psychological impacts [1]. It is therefore imperative to identify the parameters of the structure, detect the damage, and guide the maintenance to ensure structural safety and functionality. Moreover, since the loadings applied to the structures are of importance for structural evaluation and future life prediction, the development of loading identification techniques is also highly required.