esa-sceva/satcom-chunk-collection huggingface dataset:
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 1 Introduction  
Spatial audio aims to replicate a complete acoustic environment, or to synthesize realistic new ones. Sound recording and sound reproduction are two important aspects in spatial audio, where not only the audio content but also the spatial properties of the sound source/acoustic environment are preserved and reproduced to create an immersive experience.  
Binaural recording and rendering refer specifically to recording and reproducing sounds in two ears [1, 2]. It is designed to resemble the human two-ear auditory system and normally works with headphones [3, 4] or a few loudspeakers [5, 6], i.e., the stereo speakers. However, it is a complicated process, as a range of localization cues, including the static individual cues captured by the individualized HRTF (head-related transfer function), dynamic cues, due to the motion of the listener, and environmental scattering cues, should be produced in an accurate and effective way for creating realistic perception of the sound in 3D space [7, 8]. In gaming and personal entertainment, binaural rendering has been widely applied in Augmented Reality (AR)/Virtual Reality (VR) products like the Oculus Rift [9] and Playstation VR [10].  
Soundfield recording and reproduction adopts physically-based models to analyze and synthesize acoustic wave fields [11, 12, 13, 14]. It is normally designed to work with many microphones and a multi-channel speaker setup. With conventional stereo techniques, creating an illusion of location for a sound source is limited to the space between the left and right speakers. However, with more channels included and advanced signal processing techniques adopted, current soundfield reproduction systems can create a 3D (full-sphere) sound field within an extended region of space. Dolby Atmos (Hollywood, CA, USA) [15] and Auro 3D (Mol, Belgium) [16] are two well-known examples in this area, mainly used in commercial cinema and home theater applications.  
This article gives an overview of various recording and reproduction techniques in spatial audio. Two fundamentally different types of rendering techniques are covered, i.e., binaural recording/rendering and soundfield recording/reproduction. We review both early and recent methods in terms of apparatus design and signal processing for spatial audio. We conclude with a discussion of the current state of the field and open problems.
# A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
## 1 Introduction
However, due to the mismatch between the resolution of well curve data and seismic data, as well as an insufficient ability to integrate and learn global information [25], learning seismic data solely by relying on the network structure of UNet is not conducive to the identification of sand bodies of different thickness. Therefore, a structural improvement in the UNet network is carried out to improve its accuracy and resolution by adding the Cycle network. In order to process and interpret complex seismic data and improve the learning efficiency of the machine, this study uses wavelet analysis to capture multi-scale data features of seismic data. Previously, the method based on wavelet transform has been widely used in seismic time-frequency analysis [26; 27]. Compared with the traditional time-frequency decomposition method for processing seismic data, wavelet transform overcomes the fixed spectral resolution of Fourier Transform (STFT) for time-frequency analysis [28] and has a rather high spectral resolution. Current intelligent seismic inversion usually uses various simple networks to directly train various seismic data sets, and the characteristics of frequent frequency changes and an insufficient resolution of seismic data are ignored [14; 15; 16; 17; 18; 19; 20; 21]. Compared with the current intelligent seismic inversion methods, this study uses wavelet analysis to capture data characteristics, realize the separation of seismic data of different frequencies, and learn them separately to improve learning efficiency and accuracy. At the same time, the UNet network is taken as the main structure, and its structure is designed and improved according to the particularity of seismic data, so as to improve its learning ability and resolution. In this paper, a new and reliable prediction model for seismic inversion is established, namely a multi-scale super-asymmetric single-attribute Cycle-JNet network simulation model. This article is structured as follows. Section 2 introduces wavelet transform and the construction of the Cycle-JNet network. Section 3 explains the data used and the exploratory analysis of the data. In Section 4, the structure and prediction accuracy of the model are evaluated.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 3 Development of the MINLP Model Formulation for Truss Superstructures  
Truss synthesis requires the generation of an MINLP truss superstructure, which includes all possible topology/structure alternatives to compete for a feasible and optimal solution. The truss superstructure consists of the provided set of nodes and their interconnections, which represent the elements of the truss, i.e., bars. Special logical relations between the elements have been defined in order to provide the kinematical stability of the structure and prevent the undesirable overlapping of elements involved in different topology/structure alternatives. In general, all nodes and elements are alternative (optional), i.e., they can be selected or rejected from the structure. Some nodes, however, e.g., supported nodes and those where nodal loads are applied, are fixed and included in all alternative truss designs. The fixed nodes together with some fixed elements reduce the combinatorial expense of the optimization problem. Besides topological alternatives, the discrete/standard dimension alternatives are also defined in the superstructure. Although a high number of discrete/standard alternatives, in general, leads to better results, it also leads to extensive models and very expensive and difficult-to-solve problems.
## 2 Flat Panel Antenna Design
### Final Design
#### 2.2.1 Radiating Disc  
Linear periodic metallic patterns in a circular manner are printed on a disc to form a top layer, as depicted in Figure 4 (M\({}_{4}\)). The disc is separated from the other structure to  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(\mathbf{L_{1}}\) & \(\mathbf{W_{1}}\) & \(\mathbf{R_{1}}\) & \(\mathbf{R_{2}}\) & \(\mathbf{R_{3}}\) & \(\mathbf{R_{4}}\) & \(\mathbf{L_{h}}\) & \(\mathbf{W_{h}}\) & \(\mathbf{S_{v}}\) & \(\mathbf{d_{v}}\) & \(\mathbf{S_{w}}\) & \(\mathbf{W_{st}}\) & \(\mathbf{S_{st}}\) \\ \hline
340 & 115 & 111.4 & 157.2 & 155 & 150 & 16 & 12 & 1.5 & 1 & 5 & 4 & 4 \\ \hline \end{tabular}
\end{table}
Table 1: Dimensions of the proposed antenna; all values in mm.  
Figure 4: The layers’ details of the proposed design, M and D, refer to the metal and dielectric layer, respectively. There are three dielectric layers and four metallic layers.  
produce beam steering by its rotation (D\({}_{3}\) with M\({}_{4}\)). The configuration of metallic patterns can be chosen based on the required beam direction; it works as an antenna array. Thus, the critical factors are the number of strips, distance between them, and width of each strip. The space between the elements is chosen to avoid any grating lobes and achieve high gain. By changing the shape and size of the metallic patterns, the beam can be varied. To demonstrate this idea, metallic patterns are printed on the top side of the disc. After careful optimization of the patterns, a period of 8 mm and a total length of 15 \(\lambda\) along the \(x\)-direction at 20 GHz are chosen. A width of 4 mm and a spacing of 4 mm, which provide beam pointing at the \(phi=0^{\circ}\) and \(theta=8^{\circ}\) direction, are considered.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 2 Methodology
### Hardware Device
#### 2.1.2 Accelerometer Sensor  
The system uses a low-cost low-power 3-axis accelerometer (ADXL362) which has a wide range of applications due to its affordability, compact size and versatility [27; 28]. One of the main applications of accelerometers is to obtain dynamic information, which requires high-frequency data sampling. In addition to obtaining dynamic changes or vibration information, the triaxial accelerometer can also determine the tilt or inclination of a system by using the gravity vector and its projection on the axes of the accelerometer. The accelerometer's reference frame is defined as the X axis pointing vertically up, the Z axis pointing towards the solar panel and the Y axis pointing perpendicular to the X-Z plane, as shown in Figure 3a. The tilt \(\phi\) is the tilt angle in degrees between the vertical gravity vector and the X axis illustrated in Figure 3b, and it can be estimated through the three components of acceleration measurement \(A_{X,out}\), \(A_{Y,out}\), and \(A_{Z,out}\)[29; 30]:  
\[\phi=180-\arctan 2\left(\frac{\sqrt{A_{Y,out}^{2}+A_{Z,out}^{2}}}{A_{X,out}} \right)\times 180/\pi \tag{1}\]  
Figure 4 shows a 14-day example of tilting angle variations for the device mounted on a pontoon in the reservoir. A periodical signal is observed, fluctuating within a 4\({}^{\circ}\) range.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test ID** & \begin{tabular}{c} **BDG to Timber** \\ **Board (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Ultrasonic Sensor** \\ **Reading (m)** \\ \end{tabular} &
\begin{tabular}{c} **Difference (m)** \\ \end{tabular} \\ \hline
1 & 1.005 & 1.017 & 0.012 \\
2 & 0.793 & 0.804 & 0.011 \\
3 & 0.459 & 0.469 & 0.010 \\
4 & 1.068 & 1.077 & 0.009 \\ Average & & & 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ultrasonic sensor calibration testing values.  
Figure 3: Definition of axis and tilt angles in device. (**a**) Accelerometer body frame (**b**) Illustration of the tilt angle \(\phi\).  
Figure 5 shows the impact of pontoon tilting (\(\phi\)) on water height estimation. The error due to the tilting angle is estimated using Equation (2).  
\[err_{h}=h_{pole}-h_{pole}\times\cos(\phi) \tag{2}\]  
With a tilting angle \(<4^{\circ}\) and a pole length \(<1.6\) m, omitting tilt correction introduces minimal error (\(<\)5 mm) in the height estimation based on Equation (2). Furthermore, as the default accelerometer sensor is configured according to a 15 min sampling interval, its frequency is too low to effectively correct the 15 s interval in GNSS positioning data. Consequently, accelerometer readings are not incorporated into the final water height solution. However, these readings remain valuable for indicating the pontoon dynamics and severe weather conditions.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 3 Overview of IEEE 802.15.7
### IEEE 802.15.7 Physical Layer
The first source is Johnson noise \(\sigma_{th}^{2}\) generated by the components of Trans-impedance amplifier electrical noise. It is also known as thermal noise. Then, there is the shot noise induced by the signal and ambient light. It needs to include the dark current component \(i_{d}\) of the photodetector. In Equation (4), \(R_{\lambda}\) is the responsivity of the receiver, \(\sigma_{th}^{2}\) is Johnson noise, \(q\) is the electron charge, \(i_{d}\) is the receiver's dark current, \(i_{b}\) is the background-induced current, and \(B\) is the noise bandwidth of the receiver. In this noise model, the transmitted signal decreases its initial power, \(P_{tx}\), where the decrement ratio can be calculated by the DC channel gain \(H(0)\), that is the sum of all LOS and NLOS contributions of the transmitted signal in the scenario. Each one of the typical values can be seen in Table 1. However, it is important to clarify that Johnson noise value depends on Equation (5). Where \(\eta\) is the Fixed Capacitance, \(G\) is the open-loop voltage gain, \(\Gamma\) is the FET transistor noise factor, \(T_{k}\) is the environment temperature, \(k\) Boltzmann's Constant and \(I_{2}\) and \(I_{3}\) are Noise Bandwidth Factor. The parameters and noise model were based from [49] that they validated experimentally. \[\sigma_{th}^{2}=\frac{8\pi kT_{k}}{G}\eta AI_{2}B^{2}+\frac{16\pi^{2}kT_{K} \Gamma}{g_{m}}\eta^{2}A^{2}I_{3}B^{3} \tag{5}\] \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(R_{\lambda}\) Responsivity & 0.2 A/W & \(q\) electron charge & 1.60217e\({}^{-19}\) C \\ \hline \(i_{d}\) receiver dark current & 0.562 & \(I_{B}\) background current & 1.13e\({}^{-6}\) A \\ \hline \(B\) Noise Bandwidth & 300,000 b/s & \(i_{d}\) dark current & 10 nA \\ \hline G Open Loop Voltage Gain & 10 & \(\eta\) Fixed capacitance & 112 pF/cm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for the SNR calculation.
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 2 System Model  
### Analog Beamforming Model at the MS  
Consider a MS equipped with a single RF chain that is connected to a uniform linear array (ULA) radiating a primary beam \(b_{i}^{\text{MS}}\) at \(\Theta_{i}^{\text{MS}}\) direction, where \(i\) is the beam index, i.e., \(i=1,\,2,\,\ldots,\,I\), see Figure 1. The ULA is composed of \(N_{\text{MS}}\) antennas that are equispaced at \(d_{ant}=\lambda/2\), where \(\lambda\) is the wavelength, \(\lambda=\varsigma/f\), where \(\varsigma\) is the speed of light and \(f\) is the carrier frequency. The antennas are fed in parallel by analog phase shifters to provide continuous beam scanning. The MS combining vector, \(v_{\text{MS}}\), with \(\Theta_{i}^{\text{MS}}\) direction in the combining matrix, \(V_{\text{MS}}\), at the analog stage (i.e., \(v_{\text{MS}}=v_{an}\), where \(v_{an}\) is the analog precoder) is determined by the array response vector for the ULA, \(A_{\text{MS}}\), computed as [21],  
\[A_{\text{MS}}=\frac{1}{N_{\text{MS}}}\sum_{n_{\text{MS}}=1}^{N_{\text{MS}}}a_{ n}\exp(j(n_{\text{MS}}-1)(kd_{ant}\cos\Theta_{i}^{\text{MS}}+\delta_{\text{MS}}), \tag{1}\]  
where \(a_{n}\), \(k\), and \(\delta_{\text{MS}}\) denote the amplitude of the \(n\)-th antenna at the MS, wavenumber, i.e., \(k=2\pi/\lambda\), and the progressive phase shift between the elements at the MS, respectively. Furthermore, \(\Theta_{i}^{\text{MS}}\) is specified by  
\[\Theta_{i}^{\text{MS}}=cos^{-1}\bigg{[}\frac{-\delta_{\text{MS}}\lambda}{2\pi d _{ant}}\bigg{]} \tag{2}\]Next, the half-power beamwidth (HPBW) for each beam is given by [21],  
\[\phi_{i}^{\text{MS}}=\left\{\begin{array}{c}cos^{-1}(\lambda/2\pi d_{ant}(kd_{ ant}cos\Theta_{i}^{\text{MS}}\pm 2.782/n_{\text{MS}})),\,for\,0\textless\Theta_{i}^{ \text{MS}}\textless\pi,\\ 2 cos^{-1}(1-1.391\lambda/n_{\text{MS}}d_{ant}),for\,\Theta_{i}^{\text{MS}}=0, \pi.\end{array}\right. \tag{3}\]
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Effect of LoRaWAN Mode in Sensor Node Lifetime  
Table 8 shows the effect of different LoRaWAN modes on the sensor node autonomy. As mentioned in Table 5, Mode 3 is the higher data rate with lower range which have the minimum battery impact (the sensor node autonomy can reach 4 years, 6 months and 12 days). However, Mode 1 gives the shortest battery life because of the high value of \(SF\) (the sensor node consumes a lot in this case).
# Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis
## 4 Results and Discussion
### Comparative Analysis
#### 4.5.1 Analysis with Cluster Size = 5  
The analysis of methods, considering cluster size = 5, using Cleveland, Hungarian, and Switzerland databases are specified below:  
Analysis Considering Cleveland Database  
Table 2 elaborates the analysis of methods using the Cleveland database, considering training data with accuracy, sensitivity, and specificity parameters. The maximum accuracy, sensitivity, and specificity is considered as the best performance. Here, the proposed system offers better performances than the existing methods, such as SVM, NB, and DBN, respectively.  
Analysis Considering Hungarian Database  
Table 3 elaborates the analysis of methods using the Hungarian database, considering training data with accuracy, sensitivity, and specificity parameters. The proposed system offers the best performance when considering 90% of training data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**NB**} & \multirow{2}{*}{**DBN**} & \multirow{2}{*}{**Proposed Taylor-BSA–DBN**} \\ \cline{1-1}
**Training Percentage** & & & & \\ \hline \hline \multicolumn{5}{c}{Accuracy} \\
50 & 0.7590 & 0.7603 & 0.7874 & 0.8625 \\
60 & 0.7143 & 0.7682 & 0.7851 & 0.8632 \\
70 & 0.7460 & 0.7627 & 0.8122 & 0.8531 \\
80 & 0.7236 & 0.7619 & 0.7869 & 0.8644 \\
90 & 0.7538 & 0.7647 & 0.7742 & 0.8710 \\ \hline \multicolumn{5}{c}{Sensitivity} \\
50 & 0.7535 & 0.7613 & 0.7908 & 0.8693 \\
60 & 0.7120 & 0.7611 & 0.7886 & 0.8699 \\
70 & 0.7473 & 0.7558 & 0.8172 & 0.8602 \\
80 & 0.7167 & 0.7656 & 0.7903 & 0.8710 \\
90 & 0.7576 & 0.7667 & 0.7714 & 0.8788 \\ \hline \multicolumn{5}{c}{Specificity} \\
50 & 0.7566 & 0.7667 & 0.7838 & 0.8551 \\
60 & 0.7165 & 0.7750 & 0.7815 & 0.8559 \\
70 & 0.7447 & 0.7692 & 0.8068 & 0.8452 \\
80 & 0.7302 & 0.7581 & 0.7833 & 0.8571 \\
90 & 0.7500 & 0.7576 & 0.7813 & 0.8621 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of methods with cluster size = 5 using the Cleveland database. Abbreviations: SVM, Support Vector Machine; NB, Naive Bayes; DBN, deep belief network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**NB**} & \multirow{2}{*}{**DBN**} & \multirow{2}{*}{**Proposed Taylor-BSA–DBN**} \\ \cline{1-1}
**Training Percentage** & & & & \\ \hline \hline \multicolumn{5}{c}{Accuracy} \\
50 & 0.7810 & 0.8043 & 0.8428 & 0.9200 \\
60 & 0.7906 & 0.7976 & 0.8595 & 0.8907 \\
70 & 0.7674 & 0.8143 & 0.8182 & 0.8551 \\
80 & 0.7576 & 0.8  
Table 4 elaborates the analysis of methods using the Switzerland database considering training data with accuracy, sensitivity, and specificity parameters. The better performances of the proposed system, with values, are 0.8462, 0.8571, and 0.8333 for performance metrics, such as accuracy, sensitivity, and specificity.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 2 Related Work
Reference [8] mentions different machine learning-based solutions for connecting IoT devices with cellular network. However, resource constraints on our device prevent them from adopting these solutions. Techniques attaining _Green Internet of Things_ are discussed in References [23,24]. The work in Reference [24] proposes a technique for green Industrial-Tactile IoT that employs deterministic traffic flows in layer-2 and -3 with switches and routers creating an intrusion detection system. The strategy in Reference [23] achieves a green environment using fog computing in a cloud network. It also introduces a security _wall_ between the cloud network and the Internet to protect the privacy of data. Additional papers in the area security and _real-time communications_ in practical scenarios are References [9,25]. Reference [25] describes a lightweight key policy attribute-based encryption scheme (H-KP-ABE) for packet exchanges in an IoT health care system. Reference [9] deals with real-time communications using IoT network to monitor and establish a landslide warning system. The authors in Reference [28] provide a classification algorithm to detect forged nodes that are compromised and in turn perform machine-to-machine DoS attacks. Reference [29] discusses the prevention and detection of ARP spoofing and poisoning of IP addresses. _Ethical hacking_ on cyber-physical systems and LTE networks is described in References [30,31].
## 3 Results and Discussion
### Inhibitory Effect of His-Glu on Calcium Ionophore-Mediated degranulation of RBL-2H3 Cells  
Since His-Glu did not significantly suppress the elevation in cytosolic calcium ion concentration in RBL-2H3 cells, we investigated whether acidic amino acid residue-containing dipeptides inhibit degranulation when calcium ions are artificially transported into the cell. A23187 is a divalent cation ionophore and induces mast cell degranulation [20]. We confirmed that A23187-induced degranulation was significantly inhibited when RBL-2H3 cells were treated with His-Glu (Figure 2A). We also confirmed that His-Glu tended to downregulate the elevation in cytosolic calcium ion concentration in RBL-2H3 cells (Figure 2B), but not to a statistically significant degree. This fact suggests that L-glutamic acid-containing dipeptides possess a weak chelating effect and inhibit degranulation mainly by affecting intracellular processes after the increase in the cytosolic calcium ion concentration, such as the formation of the SNARE complex. There is a calcium ion-independent pathway inducing mast cell degranulation, which mainly consists of Fyn, Gab2, phosphoinositide 3-kinase (PI3K), and Akt [21; 22]. The pathway is activated upon antigen binding to IgE and does not require an increase in calcium ion concentration in the cytosol. Acidic amino acid residue-containing dipeptides might affect this pathway to inhibit the degranulation of RBL-2H3 despite rising the calcium ion concentration in the cytosol.  
Figure 2: Effect of an acidic amino acid residue-containing dipeptide on A23187-induced degranulation. RBL-2H3 cells were treated with His-Glu and subsequently stimulated with A23187 to induce degranulation. (**A**) Relative degranulation was calculated based on the enzymatic activity of \(\beta\)-hexosaminidase used as a marker of degranulation. (**B**) The elevation in cytosolic calcium ion concentration in RBL-2H3 cells was monitored using Fluo-3. The relative fluorescence intensity of Fluo-3 reflects the cytosolic calcium ion concentration. Closed circle, RBL-2H3 cells untreated with His-Glu and stimulated with A23187 (control); gray circle, the cells treated with 2.0 mM His-Glu and stimulated with A23187; open circle, the cells not treated with His-Glu and not stimulated with A23187. Data are shown as mean \(\pm\) SEM (\(n=3\)). **** \(p<0.0001\) against the control by [PERSON]’s test.