esa-sceva/satcom-chunk-collection huggingface dataset:
# A CNN-MPSK Demodulation Architecture with Ultra-Light Weight and Low-Complexity for Communications
## 3 The Proposed CNN-MPSK Architecture
### The Accuracy and Loss Curves  
The proposed MPSK modulation assumes a sine wave of one period to represent a symbol. As an example, in BPSK modulation, a sine wave of one cycle represents a 0 or a 1. Our proposed modulation can also be applied for communication systems operated at at MHz or GHz. In order to facilitate comparison, we set the carrier frequency to be 300 KHz and sampling frequency is 6 MHz for BPSK, 4 PSK, 8 PSK and 16 PSK demodulations. Thus, the input to the CNN-MPSK network consists of 20 bits. The training process are similar for these four demodulations. We demonstrate the process by using BPSK as an example. The CNN-BPSK network only requires \(n/2+5=15\) parameters in total. We generate 1 million experimental data at random, half of which is used for training and half for validating. After training the CNN-BPSK network for 15 epochs, we obtain the accuracy and loss curves versus the number of epochs, as shown in Figure 6. The epoch number is on the \(x\)-axis, while accuracy and loss are on the \(y\)-axis.  
The accuracy in Figure 6 takes a value very near 0.95 and the loss is close to 0.15. The training and validating accuracies improve as we train, while the losses decrease. In particular, the four curves change rapidly in the first two epochs, and the top and bottom parts tend to be 0.95 and 0.15, respectively. After the four epoch, the two accuracy curves almost overlap as well as the two loss curves. The training accuracy is stable, leading to a 94.4% accuracy.
## 4 Challenges and Future Research Areas  
### Airframe Shadowing and UAV Machinery  
Airframe shadowing and the ambient conditions of UAVs themselves are still unexplored and very intriguing research fields. As explained in Section 2, airframe shadowing can severely compromise the LOS link, inducing total connection and synchronization loss. In particular, for A2A links in which the elevation angles between the UAVs are in the order of 0-3\({}^{\circ}\), different maneuvers of the wings and tail (e.g., U-turns or large pitch-and-roll turns) can block the LOS path. For example, signal-shadowing events up to 12.5 dB were observed in 45\({}^{\circ}\) rolls, in which the wings blocked the direct component. Diffraction and shadowing events were also found in a wavy UAV movement, which resulted in additional fades up to 2-3 dB. It was also shown that the antenna directivity may also be affected, for example, in yaw turns, in which substantial signal variations up to 17 dB were observed. These were associated with changes in the UAV-antenna-radiation pattern. Therefore, additional tests are required with small and large UAVs in order to measure and model possible airframe-shadowing phenomena related to the kinetic patterns of UAVs. Furthermore, it is worth investigating the ambient conditions, which include vibrations from the UAV motor, air friction, turbulence, and sudden air gusts during flight. These specific phenomena may cause limited shadowing events and partial link losses (e.g., due to antenna misalignment), or a total link loss if the LOS path is blocked between the terminals' antennas. Apart from the terminals' speeds, Doppler effects are also induced by the reflections from the rotor blades, which are associated with the number of rotor-blade evolutions. These effects may increase the Doppler spread further and cause synchronization problems. In this context, additional studies and measurements are necessary not only for UAVs with rotor-propulsion systems (with single or multi-rotor engines), but also for large jet-propulsion vehicles. The latter may generate significant vibrations and jitter that can appear as additional peaks in the Doppler spectrum, thus increasing the spread. Doppler spread can originate carrier-frequency offset and inter-carrier interference, particularly if orthogonal frequency division multiplexing (OFDM) schemes are adopted. Frequency synchronization can be exploited to combat these Doppler events and specific channel-access algorithms (e.g., multi-carrier code-division multiple access) can be employed to sufficiently alleviate Doppler spread. Nevertheless, further assessments are required through the performance of studies and measurements investigating these effects, which are introduced either by the UAV itself (as described above) or through different speed and motion trajectories. Of course, different and higher frequencies have to be
###### Abstract  
In this study, a series of Li\({}_{2}\)O-Al\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\) glasses doped with various concentrations of Dy\({}_{2}\)O\({}_{3}\) (where x = 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0 mol\(\%\)) were prepared by using a conventional melt-quenching technique. The structural, physical and optical properties of the glasses were examined by utilising a variety of techniques instance, X-ray diffraction (XRD), UV-Vis-NIR spectrometer, Fourier transform infrared (FTIR) and photoluminescence (PL). The XRD spectra demonstrate the amorphous phase of all glasses. Furthermore, the UV-vis-NIR spectrometers have registered optical absorption spectra a numbers of peaks which exist at 1703, 1271, 1095, 902, 841, 802, 669, 458, 393 and 352 nm congruous to the transitions from the ground of state (\({}^{6}\)H\({}_{152}\)) to different excited states, \({}^{6}\)H\({}_{11/2}\), \({}^{6}\)F\({}_{11/2}\) + \({}^{6}\)H\({}_{9/2}\), \({}^{6}\)F\({}_{9/2}\) + \({}^{6}\)H\({}_{7/2}\), \({}^{6}\)F\({}_{7/2}\), \({}^{6}\)F\({}_{5/2}\), \({}^{6}\)F\({}_{3/2}\), \({}^{6}\)F\({}_{9/2}\), \({}^{4}\)F\({}_{9/2}\) and \({}^{6}\)P\({}_{7/2}\), respectively. The spectra of emission exhibit two strong emanation bands at 481 nm and 575 nm in the visible region, which correspond to the transitions \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{6}\)H\({}_{15/2}\) and \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{6}\)H\({}_{13/2}\). All prepared glass samples doped with Dy\({}_{2}\)O\({}_{3}\) show an increase in the emission intensity with an increase in the concentration of Dy\({}^{3+}\). Based on the obtained results, the aforementioned glass samples may have possible applications, such as optical sensor and laser applications.  
borate glass; Dy\({}_{2}\)O\({}_{3}\); UV-VIS-NIR; photoluminescence
# Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network
## 4 Simulation and Analysis
With the increase in \(N_{s}\), the directional angle of the CM beamforming constantly changed from 0 to \(\pi\). When \(N_{s}=16\), the SR was the highest when the CM beamforming was transferred to the direct channel from \(\mathbf{h}_{ab}\). When \(N_{s}=256\), the CM beamforming was directed to the middle of the \(\mathbf{H}_{ai}\) channel and the \(\mathbf{h}_{ab}\) channel. When \(N_{s}=1024\), the SR was the highest when the CM beamforming was aimed at the Alice-to-IRS channel. These results were mainly due to the fact that the Alice-to-Bob direct channel dominated in the case of the small-scale IRS, whereas the cascaded channel via Alice, the IRS, and Bob dominated for the large-scale scenario. Based on the inspiration of Figures 5-7, we propose three different MRT methods for designing CM beamforming vectors. In Figure 8, the SR versus \(N_{s}\) is plotted for the various MRT methods. The figure shows that the three methods had different advantages under different numbers of IRS elements. When \(N_{s}\) ranged from 8 to 64, \(\mathbf{v}_{CM}\) was aligned with the \(\mathbf{h}_{ab}\) channel to achieve the best SR performance. When \(N_{s}\) varied from 64 to 512, \(\mathbf{v}_{CM}\) was aligned with the channel between \(\mathbf{h}_{ab}\) and \(\mathbf{H}_{ai}\) to achieve the best SR performance. When \(N_{s}\) changed from 512 to 1024 (i.e., under hyperscale), \(\mathbf{v}_{CM}\) was aligned with the \(\mathbf{H}_{ai}\) channel to achieve the best SR performance. Figure 4: Secrecy rate versus the distance between [PERSON] and [PERSON]. Figure 5: Secrecy rate versus \(\theta_{CM}\). Figure 9 plots the computational complexity versus \(N_{\text{s}}\). Figure 9 shows that there was an increasing order in complexity (FLOPs): the existing method in [36] (\(\mathcal{O}(N^{2})\) FLOPs),  
Figure 8: Secrecy rate versus \(N_{\text{s}}\). Figure 6: Secrecy rate versus \(\theta_{CM}\). Figure 7: Secrecy rate versus \(\theta_{CM}\). the MRT-NSP-PA (\(\mathcal{O}(N^{2})\) FLOPs), the Max-SR-SLNR (\(\mathcal{O}(N^{3})\) FLOPs), and the SDR in [37] (\(\mathcal{O}(N^{4.5})\) FLOPs). The MRT-NSP-PA was at least one and two orders of magnitude lower than the Max-SR-SLNR for the small-scale and large-scale IRS, respectively. Moreover, as \(N_{s}\) increased, the complexity gradually and linearly rose. From Figures 2 and 9, it can be seen that the two methods proposed in this paper had higher SRs than the method in [36]. In addition, the gap in the SR performance between the Max-SR-SLNR method and the SDR method in [37] was small, and the computational complexity of the Max-SR-SLNR method was much lower than the SDR method in [37]. Thus, the two methods proposed in this paper have their own advantages in terms of the SR performance and computational complexity.
# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 2 Related Work  
### Name Resolution System  
In this paper, we focus on the NRS of the ICN architectures which use identifier-locator separation mode, where name resolution and routing are decoupled into two steps. The user first sends a name resolution request to an NRS to find a locator mapped by the identifier and then uses this locator for routing and forwarding.  
The NRS is the core infrastructure in ICN, and its structure is crucial and directly affects the capabilities and efficiency of name resolution in ICN. Distributed hash table (DHT) is a widely applied, distributed storage algorithm due to its logarithmic scalability, robustness, and resilience. In studies [20; 21], the authors applied DHT to their structures design of NRS and the selection of locations for name mapping records storing, based on chord [22] and content-addressable network [23], respectively. In MobilityFirst [16], the authors designed a global NRS with a one-hop DHT scheme to achieve the dynamic binding of identifiers and locators. An inherent problem of DHT is the non-location-aware characteristic. The path of name resolution queries is relatively long and the upper bound of the resolution latency is difficult to guarantee [24]. As improvements, some researchers have proposed hierarchical NRS architectures to overcome this deficiency. NetInf proposesa multi-level DHT (MDHT) [25], which constructs a hierarchical DHT based on different name resolution domains, using a traditional DHT within a domain and a hierarchical structure to aggregate individual DHTs among domains. Name mappings are published to the local resolution domain and the corresponding upper-level domain, and the query is performed in the direction from low to high. The hierarchical NRS architecture is also used in PSIRP, DHT-NRS [26], and scalable multi-level virtual distributed hash table (SMVDHT) [27]. In addition, some researchers use a tree as the structure of NRS. [PERSON] et al. proposed a flat name-based ICN NRS called Griffin [28], where the name resolution is mapped to a tree-based T-space derived from the network topology, providing efficient name resolution with a more explicit query path compared with DHT schemes. [PERSON] et al. proposed Ftree [29], which tries to avoid accessing the root node and only stores name records in its leaf nodes to achieve fast and proximal name resolution. The tree structure considers proximity among name resolution nodes and reduces query path length. The tree-based NRS architecture is also used in data-oriented networking architecture (DONA) [15], B-NRS [30] in ID-based networking architecture [31], and the dynamic name resolution mechanism (DNRM), proposed by [PERSON] et al. in [32].  
The above-mentioned architectures are primarily concerned with scalability issues. There is little discussion on how to use an NRS to provide name resolution service within a deterministic transmission latency. However, this problem is a key requirement and is a challenge for ICN to effectively support new scenarios of 5G/B5G, such as industrial control and IoT.
## 2 Review of Existing Mode-Pairing Criteria  
In this section, some of the most used criteria for mode pairing, which can be applied for systems with complex modes, are briefly presented. In addition to their mathematical formulation, the advantages and disadvantages of using each of these criteria are also presented.
## 3 Air Data System Description and Functions
### Air Data Computer Description  
The ADC encompasses two solid-state pressure transducers (absolute and differential ones), all necessary hardware components for power management and distribution to the probes, avionic bus interface capabilities with the A/C flight control system (FCS), and calculation functionalities. For evaluation of the ADC's mean time between failures (MTBF), the ADC is split into (1) two redundant (dual) main boards (FPGA, resistors, capacitors, etc.) (2) one absolute pressure transducer, and (3) one differential pressure transducer.  
The choice of a redundant board guarantees limited capabilities to identify failures at the ADS level. In fact, the ADC can perform a Built-in-Test (BIT) in order to evaluate its operative status. Therefore, the ADS can operate in _normal mode_ or _emergency mode_ according to the results of the internal BIT. In _normal mode_, the ADC provides the flight control computer (FCC) with a complete message containing its own status validity, whereas in _emergency mode_, the ADC declares itself in failure with predefined output messages. The ADC has the following characteristics:  
* The HW is compliant to DO-254 level B certification.
* The chosen FPGA in the HW is single event upset (SEU) immune and is used to configure I/Os and other HW critical aspects.
* The ADC contains two redundant boards. In case of a serious failure that could involve permanent damage to the nominal board, the redundant one switches off all the nominal internal supplies, reporting the failure to the avionic system.
* The HW communication link to FCC using CANaerospace Revision is 1.7 in the form of the CAN2.0B (29-bit identifiers) format with 1 Mbit/s bus speed. Nominal and redundant links are implemented in order to prevent a single failure event.
* The HW provides provisional link ARINC-664 (AFDX) for alternative use in CANaerospace with (a) redundancy management, (b) an integrity checker, and (c) deterministic packet delivery.
## 3 Twist Angle Function and Lateral Deflection Function  
In study [42], in which lateral torsional buckling of fork-supported beams was analysed, the function of the twist angle of the section was approximated using power polynomials that described \"the deflection function\" of the hinged beam (Table 1, polynomials \(W_{Pi}\)). In Table 1, formulas for the polynomials of deflection were written in the dimensionless coordinates \(\rho\) = \(x\)/\(L\). To account for the elastic restraint against warping in the beam support section, in study [34], the twist angle function (\(\phi\)was extended. That was done by introducing additional polynomials that describe \"the deflection function\" of the restrained beam (Table 1, polynomials \(W_{Uii}\)). \"Hinged\" polynomials (\(W_{Pi}\)) were coupled with \"restrained\" polynomials (\(W_{Uii}\)) by means of the index of fixity against warping \(\kappa_{\omega}\) (5a) [34] acc. formula:  
\[\phi(x)\ =\ \sum_{i\ =\ 1}^{3}a_{i}((1-\kappa_{\omega})\cdot W_{Pi}+\kappa_{ \omega}\cdot W_{Uii}) \tag{8}\]  
where: \(a_{i}\)--free parameters of the twist angle function, \(W_{Pi}\), \(W_{Uii}\)--polynomials acc. Table 1.  
The polynomials used (Table 1) satisfy the boundary conditions of the twist angle function for the fork support \(W_{Pi}\) (\(\phi=0\), \(\phi^{\prime\prime}=0\) for \(x=0\) and \(x=L\)), and for full restraint \(W_{Uii}\) (\(\phi=0\), \(\phi^{\prime}=0\) for \(x=0\) and \(x=L\)), respectively.  
In this study, to account for the elastic restraint of the beam against lateral rotation at the support nodes, the lateral deflection function (\(u\)) was written in a way analogous to (8). Thus \"hinged\" polynomials (\(W_{Pi}\)) and \"restrained\" polynomials (\(W_{Uii}\)) were \"coupled\" by means of the index of fixity \(\kappa_{u}\) (5b) acc. formula:  
\[u(x)\ =\ \sum_{i\ =\ 1}^{3}b_{i}((1-\kappa_{u})\cdot W_{Pi}+\kappa_{u}\cdot W _{Uii}) \tag{9}\]  
where: \(b_{i}\)--free parameters of the lateral deflection function.  
The polynomials used (Table 1) satisfy the boundary conditions of the lateral deflection function for the fork support \(W_{Pi}\) (\(u=0\), \(u^{\prime\prime}=0\) for \(x=0\) and \(x=L\)) and for full restraint \(W_{Uii}\) (\(u=0\), \(u^{\prime}=0\) for \(x=0\) and \(x=L\)).  
Functions (8) and (9) make it possible to model boundary conditions at the nodes for the elastic restraint against warping and against lateral rotation for arbitrary (i.e., from 0 to 1 interval) values of the indexes of fixity \(\kappa_{\omega}\) acc. (5a) and \(\kappa_{u}\) acc. (5b).
## 2 Basic Principles and Signal Models for Vital Sign Detection  
As illustrated in Figure 1, the millimeter wave radar [16] comprises a radio frequency system, analog clock, and digital circuit. The radio frequency system includes a transmitter and receiver, and the digital circuit comprises an analog-to-digital converter (ADC) and a digital signal processor (DSP).  
The millimeter wave radar transmits linear FMCWs, and the transmit signal model \(S_{T}(t)\) can be expressed as follows:  
\[S_{T}(t)=A_{t}e^{j(2\pi f_{c}t+\pi\frac{B}{T}t^{2}+\varphi_{0})} \tag{1}\]  
where \(A_{t}\) is the amplitude of the linear FM signal, \(f_{c}\) is the linear FM signal carrier, \(B\) is the signal bandwidth, \(T\) is the signal sweep time, and \(\varphi_{0}\) is the random initial phase of the signal.  
The reflection from the human thorax is intercepted by the receiver antenna. The received signal \(S_{R}(t)\) can be expressed as  
\[S_{R}(t)=A_{r}e^{j(2\pi f_{c}(t-t_{d})+\pi(\frac{B}{T})(t-t_{d})^{2}+\varphi_{ 0}+\varphi)} \tag{2}\]  
where \(A_{r}\) is the signal's amplitude, \(\varphi\) is the additional phase shift of the reflection, and \(t_{d}\) is the delay in the echo signal. The received signal is combined with the local oscillator (LO) signal in the mixer, and the IF signal is obtained as follows:  
\[S_{IF}(t)=A_{t}A_{r}e^{j(4\pi\frac{B}{T}t+\frac{4\pi}{\lambda}R)}=A_{t}A_{r}e^ {j(f_{b}t+\Phi_{b})} \tag{3}\]  
where \(R\) is the distance between the target and radar, \(f_{b}\) is the IF signal carrier frequency, and \(\Phi_{\rm b}\) is the initial phase.  
The life signal processing flow is shown in Figure 2.  
To measure the subtle displacement of the chest cavity due to breathing and one's heartbeat, obtaining the phase change within the target range bin \(\Delta\phi_{\rm b}\) is necessary, and this is expressed as  
\[\Delta\phi_{\rm b}=\frac{4\pi}{\lambda}\Delta R \tag{4}\]  
The target phase of the signal can be obtained from the signal in the range bin via the FFT. The vibration signal in the range bin at distance m is extracted as follows:  
\[x(m,nT_{s})=\frac{\lambda}{4\pi}\phi_{\rm b}(m,nT_{s}) \tag{5}\]  
Figure 1: Block diagram of millimeter wave radar system.  
where \(m\) is the target distance, \(n\) is the chirp index, and \(T_{s}\) is the chirp periodicity (the continuous monitoring time).
## 2 Simulation of Blast Events
### Characteristics of Blast Load and Pressure Wave Propagation
Shock waves differ from pressure waves by the higher propagation velocity and considerably higher peak overpressure. In addition, the duration of the positive and negative phase is much shorter than that of pressure waves. In this contribution, an explosion blast load with high peak pressure and long duration shall be analyzed. A high peak pressure tends to correspond to a detonation scenario with a shock wave loading. However, the long pressure duration corresponds more to a slow deflagration scenario, which also matches the basic hazard in an ammonia plant. Considering the order of magnitude of pressure and duration (30 kPa and 300 ms proposed in [13; 14]), the source of an explosion is assumed to be from outside the plant boundaries rather than inside of them, i.e., in principle, it can also correspond to a solid explosion followed by a blast wave. Indeed, the analysis of detonation scenarios with shock wave loading has been investigated quite often, as the literature review in Section 2.3 will show. Dealing with a blast wave from slow deflagration scenarios is a more challenging task and has hardly been analyzed so far. Therefore, a slow deflagration scenario is mainly analyzed in this contribution. To provide an understanding of the analysis approach derived in Section 3.5, some basic aspects of blast load, shock and pressure waves are explained first. In general, an explosion event--solid detonation or slow deflagration--is characterized by a rapid release of energy and a propagating incident blast wave \(\mathrm{P_{inc}}\). If the incident wave strikes a target, it rebounds with greater amplitudes as the reflected blast wave \(\mathrm{P_{ref}}\). For detonation events, the pressure time histories of both incident and reflected pressure show an instantaneous pressure increase followed by decay behavior with an exponential rate until they reach the ambient value \(\mathrm{P_{0}}\). Slow deflagration events are characterized by a gradual increase in pressure leading to a peak overpressure (either incident \(\mathrm{P_{inc}}\) or reflected \(\mathrm{P_{ref}}\)), followed by a gradual decrease in pressure and a negative phase. The corresponding period of both pressure time histories for solid detonation and slow deflagration time is known as the positive phase duration \(\mathrm{t_{d}}\). Furthermore, the pressure value drops below the ambient pressure \(\mathrm{P_{0}}\), creating suction, and finally grows once more until it reaches the ambient value after a duration, which is referred to as the negative phase duration. Figure 1 shows a simplified representation of such pressure time histories, also defined as a shock wave for solid detonation (Figure 1a) and pressure wave for slow deflagration (Figure 1b). The incident and correspondingly reflected impulse \(\mathrm{I_{inc}}\) and \(\mathrm{I_{ref}}\) for each of the two phases is defined and calculated as the area between the pressure curve of incident and reflected pressure and the time axis.