esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### Software Characteristics
#### 4.4.3 Simulation Tools  
All levels of abstraction, from the physical layer to protocols and routing, should be associated with a full CR simulation program. We have not found a method in the literature that embodies all these characteristics. The only way to do this, to the best of our knowledge, is to combine open source resources, such as radio simulators (e.g., GNU Radio [72] and CogWave [73]) and general network simulators (e.g., Omnet++ [74] and ns-3 [75]).  
GNU Radio is a free and open source software designed to use its graphical user interface flow graphs to simulate radio transmissions and signal processing (GNU Radio Companion). It could be compared to LabView [76] and Simulink [77]. Standard flow graph blocks encompass waveform generators, modulators, instrumentation sinks, math operators, filters, and Fourier analysis. It also facilitates the development of new blocks using the programming language C++, as well as the design of the flow graph using Python. In addition, GNU Radio can be connected to SDR hardware, thus allowing simulations from the testbed to be used.  
Another open-source program suggested for designing CR waveforms is CogWave. It involves many modulation systems, including multichannel DAA-OFDM, Fathers, and others from GNU Radio (e.g., OFDM, BPSK, and QPSK). During run-time, CogWave is able to reconfigure the modulation scheme and can communicate with SDR hardware, as well as GNU Radio, to provide real-time transmission between USRP devices.  
Omnet++ and INET [78] have been widely used in the literature to simulate CR networks for general network simulators [79; 80; 81]. Omnet++ offers a C++ component-based architecture where modules and components can be assembled using a graphical user interface or a high-level network description language, similar to flow graphs in GNU Radio (NED). Its modular architecture therefore eases the reusability of the built models. Furthermore, INET offers protocols, templates, routing, and mobility simulation as an open-source model library for Omnet++.  
Another well-established tool is the ns-3. It is an open discrete-event simulation environment for network research that provides C++ libraries of models for wired protocols, IP and non-IP, wireless, dynamic routing protocols, and so forth. For instance, the ns-3 has been applied in the simulation of CR networks regarding spectrum handover [82], data collection [83], and channel sharing [84].  
It is necessary to note that many programming languages can be used to build a custom simulator, as they provide access to several network and scientific libraries (e.g., Java, C++, Python, and Julia). However, most simulation scenarios encountered in the literature are complex enough to require thousands of lines of code, turning this option impractical for most of the problems.
# Calculation of 1/f Fluctuation from Sound Signal and Comfort Evaluation
## 2 Calculation Method for Fluctuation Coefficient of the Sound Signal  
### Fluctuation Coefficient of the Whole Sound Signal  
We analyzed the fluctuation included in a sound signal using a.wav file, which is the time history of the sound signal. Figure 1a shows the sample of the sound signal.wav file. The spectrum exhibited the discrete Fourier transform, which is defined as follows:  
\[F(k\Delta f)=\frac{N}{T_{0}}\sum_{n=0}^{N-1}x(n\Delta t)e^{-j\frac{2\pi}{N}kn} \tag{1}\]  
\[f_{s}=\frac{1}{\Delta t}\]  
\[k=0,\pm 1,\pm 2,\cdots\pm\frac{N}{2}\]  
where \(T_{0}\) is the music running time [s], \(\Delta t\) is the sampling time [s], \(N\) is the sampling number of wav data, \(x\) is the sampled music data, \(\Delta f\) is the frequency resolution, and \(F\) is the spectral intensity for each frequency range. In this experiment, for all cases, the sampling frequency \(f_{s}\) of the music was 44.1 kHz.  
The least squares method was used to calculate the slope of the spectrum. The relationship between frequency \(f\) and spectrum \(F\) is given as follows:  
\[F=af+b \tag{2}\]  
Figure 1: The calculation result of the fluctuation coefficient of the whole sound signal. (**a**) Time history of the sound signal. (**b**) Spectrum of the whole sound signal (\(\lambda_{m}\): \(-0.873\)).  
Slope \(a\) and intercept \(b\) are respectively shown as:  
\[a=\frac{\sum_{i=n_{1}}^{n_{2}}\left\{\left(f_{i}-\overline{f}\right)\left(F_{i}- \overline{F}\right)\right\}}{\sum_{i=n_{1}}^{n_{2}}\left(f_{i}-\overline{f} \right)^{2}} \tag{3}\]  
\[b=\overline{F}-a\overline{f} \tag{4}\]  
where \(f_{i}\) is the discretized frequency, and \(F_{i}\) is the spectrum at \(f_{i}\). In addition, \(n_{1}\) and \(n_{2}\) are integers to satisfy \(f_{i1}=0.05\) Hz and \(f_{i2}=0.5\) Hz, and \(\overline{f}\) and \(\overline{F}\) are the averages of frequencies and spectra in the sections. In this study, we defined the fluctuation coefficient of the whole sound signal \(\lambda_{m}\), which was calculated using the above method.  
Figure 1b shows a result of the spectral slope obtained by the above equations. The fluctuation coefficient of the whole sound signal \(\lambda_{m}\) was \(-0.873\).
## 3 Mathematical Modeling Formulation
### Modeling of PV and Wind Turbine Power  
The generation of renewable energy from sources like solar photovoltaic systems and wind turbines is greatly influenced by the availability of key energy sources such as sunlight and wind. The electricity generated by solar panels is directly linked to the amount of sunlight that reaches the Earth's surface, a factor that can be influenced by variables like geographical location, weather patterns, and the duration of daylight. Consequently, the efficiency of solar arrays is affected by factors such as cell temperature and the intensity of solar radiation at peak power generation. Equations (16) and (17) [26] can be employed to compute these parameters accurately.  
\[P_{PV}(t)=\left[P_{(PV,STC)}\cdot\frac{G_{T}(t)}{1000}\cdot\left(1-\gamma(T_{j} -25)\right)\right]\cdot N_{PVs}\cdot N_{PVp} \tag{16}\]  
\[T_{j}=T_{\text{amp}}+\frac{G_{T}}{G_{T,STC}}\cdot(NOCT-20) \tag{17}\]  
Equations (16) and (17) define various parameters within the realm of photovoltaic systems. Specifically, the symbols represent the following attributes: \(P_{PV}\) denotes the output power of the photovoltaic system at maximum power point, and \(P_{PV,STC}\) denotes the rated power of the photovoltaic system; \(G_{T}\) represents the radiation level under standard conditions; \(\gamma\) signifies the temperature coefficient of power at the maximum power point; \(T_{j}\) stands for the temperature of solar cells; \(N_{PVs}\), \(N_{PVp}\) signify the number of modules arranged in series and parallel, respectively; and finally, \(NOCT\) denotes the nominal operating temperature of the cell. Furthermore, the power output from wind turbines \(P_{wt}\) is intricately linked to the velocity of the wind, a factor subject to fluctuations across different timeframes, ranging from instantaneous changes to hourly, daily, and seasonal variations. Hence, Equation (18) is utilized to model the actual power generation of a wind turbine [26].  
\[P_{wt}(\
u)=\begin{cases}0&\text{if }\
u<V_{ci}\\ P_{R}(A+B\
u+C\
u^{2})&\text{if }V_{ci}<\
u<V_{r}\\ P_{R}&\text{if }V_{r}<\
u<V_{co}\\ 0&\text{if }V_{co}<\
u\end{cases} \tag{18}\]In Equation (18), \(P_{wt}\), \(V_{ci}\), \(V_{co}\), and \(V_{r}\) represent the wind turbine output power, cut-in, cut-out, and rated wind speeds, respectively. The variable \(A\) denotes the rated power of the wind turbine, while \(B\) and \(C\) symbolize the coefficients linked with the turbine.
## 3 Results and Discussion
### Aerosols, Clouds, and Solar Fluxes during the Evolution of DAEC
For example, due to their high albedo, clouds increase the upward SW flux and thus the SW absorption by ozone and water vapor, or absorbing aerosols (e.g., dust or biomass burning) above them [76]. The SW absorption is also increased within the cloud layers, because they absorb more near-infrared (NIR) solar radiation than water vapor does [77]. On the contrary, clouds can reduce the SW atmospheric absorption by shielding the water vapor and consequently the solar absorption below them. The first two mechanisms dominate the third one in high clouds; in middle clouds, the three mechanisms nearly cancel out; while the third mechanism predominates in low clouds [75]. On the first day of the DAEC (16 June 2016, Figure 3 ii-a), there is a clear contrast between the western and eastern MB, where aerosols and clouds practically do not exist (Figures 3 and S8). The largest SW atmospheric absorption (400-450 W/m\({}^{2}\)) is observed over northeast Algeria, Tunisia, and Tunisian coasts, where DOD is also maximum (Figure 2). Secondarily high absorption is observed over an area along a north-easterly oriented axis, extending from the boundaries between Morocco and Algeria up to the northernmost part of the western Mediterranean Sea, where high-level clouds exist (Figure S8). The patterns of atmospheric solar absorption and their explanation are similar for the next two days of the DAEC, namely on 17 and 18 June 2016. As it concerns the patterns of absorbed solar radiation at the MB's surface (right column of Figure 3), during the whole DAEC, they have many similarities with the patterns of absorbed radiation at the TOA, and contrasts with the patterns of absorbed radiation in the atmosphere, showing that surface absorption accounts for the largest part of the overall planetary (TOA) SW absorption over the study region. A strong solar absorption, with values from 400 up to 1000 W/m\({}^{2}\), occurs over the largest part of the MB during the three days of the DAEC. The largest values (reddish colors) appear over areas with no or few clouds and aerosol loads, e.g., the eastern Mediterranean Sea, whereas the smallest surface solar absorption (down to 150-200 W/m\({}^{2}\), bluish colors) is observed where clouds or aerosols exist and drastically reduce, through scattering and absorption, the solar radiation reaching the surface [5; 75].
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 4 Experimental Results and Analyses
### Data Pre-Processing
Therefore, the sound signal was generated into a time-frequency map by continuous wavelet transform. As shown in Figure 4, the operational condition of a typical pipe is selected for time-frequency map analysis. Figure 3: Time domain and frequency domain diagrams of pipe conditions (normally, single blockage, multiple blockages): (a) time domain; (b) frequency domain. As shown in Figure 4a, lighter color indicates higher energy at the corresponding site. According to the time-frequency map of the normal pipeline. The energy concentrations are present only at the pipeline head and tail ends, while other locations show no energy concentration. For single blockages and lateral connections, as can be seen from Figure 4b,c, the energies have a good time-frequency concentration at the locations of lateral connections and blockages in the pipe, which conform to the acoustic theory. The energies gather at the blockage site while diverging slightly at the lateral connection site. In the case of multiple blockages, as can be seen from Figure 4d, with the increase in blockages, gradual attenuation was observed in the energies between various blockages and lateral connections. Moreover, the frequency bands, where the energies in different operating conditions appear, remain relatively stable. However, energy overlapping occurs when there are two blockages and a lateral connection in the pipeline. Although the energy spectrogram can locate the positions of the pipe head and tail ends, blockages, and lateral connections accurately, it is unable to determine the pipe operating condition properly. Hence, further identification and research of the pipe condition were necessary. Since the sound waves of different frequencies were reflected in varying degrees at different rates, the intensities of signals and their sensitivities to the pipe condition vary in different frequency ranges. The characteristic components irrelevant to the pipeline blockage will interfere with the classification, thereby resulting in lowered classification accuracy.
# Experimental Study on Dynamic Effects of a Long-span Railway Continuous Beam Bridge
## 5 Analysis of Impact Factors
### Comparison with the Bridge Design Code in China  
According to the fundamental code for design on railway bridge and culvert in China [23], the design impact factor of the continuous beam bridge can be calculated by the Equation (4):  
\[\text{IM}\ =\ (1-h)\bigg{(}\frac{24}{30+L}\bigg{)} \tag{4}\]  
where \(h\) is the filling thickness, \(L\) is the bridge span. The IM obtained by the above formula is 0.087. The comparison between the maximum IM from experimental data and the IM from bridge code is shown in Table 1. It can be seen that the IMs obtained from the experimental data are greater than that obtained from the specification, which shows the bridge code underestimates the dynamic effect of the train. The reason may be that bridge specifications only consider the influence of bridge span on the impact factor, and in fact, the impact factor is also affected by factors such as the train speed and the track condition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**IM from Experimental Data**} & \multirow{2}{*}{**IM from Bridge Code**} \\ \cline{2-3}  & **Deflection IM** & **Strain IM** & \\ \hline Section A-A & 0.110 & — & 0.087 \\ Section B-B & — & 0.17 & 0.087 \\ Section C-C & 0.070 & 0.19 & 0.087 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the IM from experimental data and the IM from bridge code.  
Figure 12: Strain time histories of point P1 with one train running on the bridge.
# Incorporating Clinical Imaging into the Delivery of Microbeam Radiation Therapy
## 2 Materials and Methods
### Image Guided MRT Workflow  
Given the tools available to a user at IMBL, the following workflow is proposed:  
Expanding in greater detail on Figure 3, the workflow is as follows.  
Figure 3: A brief overview of the entire treatment workflow, following patients from CT imaging through to treatment.  
Figure 2: Planar X-ray image of the rat phantom on the DynMRT Stage superimposed with the manually identified skeletal landmarks from the X-ray image (tri-point blue), registered landmarks from the composite CT reconstruction (tri-point green) and inferred location of the centre of the ball bearing (red cross).  
1. Patients are imaged at the adjoining Monash Biomedical Imaging facility on a Siemens SOMATOM go.Up clinical CT scanner, mounted horizontally in a stereotactic frame. Intravenous iodine contrast is injected to provide tumour contrast.
2. The CT dataset is then digitally orientated to reflect the desired beam's eye view of the synchrotron beam for treatment; the orientated CT dataset is then exported.
3. The exported CT dataset is then imported into SyncMRT and summed down the beam's eye view, producing a DRR for comparison to the later acquired planar radiograph.
4. The tumour volume is then visually identified in the CT dataset and selected by the user.
5. The patient is then mounted vertically on the DynMRT stage in a stereotactic frame and a 2D planar image is acquired with the off-axis imaging system. The location of six prominent skeletal landmarks are then selected in both the DRR and newly acquired planar radiograph.
6. The tumour location with respect to the identified skeletal landmarks was identified in the DRR. Given that the skeletal landmarks are readily visible in the planar radiograph, assuming a rigid body transformation between the DRR and planar radiograph, the location of the tumour in the planar radiograph can be calculated.
7. SyncMRT is used to calculate and apply the necessary transformation to the treatment stage, aligning the centre of the tumour volume with the centre of the treatment field.
8. A second radiograph is taken for alignment verification immediately before treatment. SyncMRT overlays the centre and extremities of the treatment field over the radiograph. If any issues in the patients' position are observed then steps 7 and 8 are repeated until the alignment is satisfactory. Once the alignment is satisfactory, treatment may commence.  
With stage calibration, CT scans reconstructed and loaded into SyncMRT in advance, steps 4 through 8 may be completed in as little as three minutes per patient, minimising the time the patient is sedated and enabling fast patient turnover. This efficient process also serves to reduce the potential pain and distress an animal may experience during treatment, consistent with the refinement step of the three Rs principle in animal experimentation. The DynMRT treatment stage is the only component used that would limit the use of this protocol to image and treat larger animals.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
## 3 Secure D2D Communication
### Details of Communication Mechanism
Therefore, even if the attacker taps the data transmitted in the middle of the key exchange, the secret key cannot be derived. Secure data transmission, the last step of the proposed D2D communication, performs data encryption communication. At this time, encryption uses a lightweight AEAD cipher. The lightweight AEAD cipher is a cryptographic algorithm that provides not only confidentiality but also integrity and authenticity. It encrypts the data to be transmitted and creates a MAC for authenticating data integrity. Moreover, in the encryption process, the AEAD cipher uses additional information about a communication session and the other party, called the associated data (AD), thereby the AEAD cipher provides authenticity, which means the message is transmitted from the right party at the right time. In the proposed D2D communication system, the AD consists of the D2D token and context sequence information and manages the sequence for each transmission. Upon receiving the cipher text using the AD configured as described above, the UE may check whether the other UE performing D2D communication has received data corresponding to the current situation along with authentication. The data format used in secure data transmission is shown in Figure 4. In this step, any lightweight AEAD cipher can be applied according to available resources. Table 2 shows available lightweight  
Figure 3: The whole process of secure D2D communication. AEAD ciphers which are candidate cipher from NIST lightweight cryptography standardization (Round 2) [10].
## 3 Results
### Validation and Comparison of the PNN Method
The six categorical statistics of these two methods are also presented in Figure 5. As indicated by Figure 5a, b, the FAR of TRMM 2A12 (mean value = 0.33) was higher than that of PNN results (mean value = 0.23), whereas the POD of TRMM 2A12 (mean value = 0.57) was lower than PNN (mean value = 0.68). This indicates that the TRMM 2A12 can obtain higher false alarm and misdetected precipitation rates than PNN. The Kmean method presented a higher FAR and a lower POD compared with PNN, indicating that the results of the Kmean contain more false alarm and misdetected precipitation than PNN. Besides, Figure 5c-f shows that the CSI, ETS, HK and HSS of the TRMM 2A12 and Kmean were all smaller than PNN and even smaller than the SI_threshold. Therefore, the PNN method showed an obvious better performance in the delineation of rain areas than the TRMM 2A12 and Kmean methods. The performances of the four rain area delineation methods were also assessed by calculating the POD, FAR, CSI, ETS, HK and HSS of summarized test datasets (e.g., putting the test datasets of the 16 rain events together). Figure 6 presents the results of the six categorical statistics. It can be seen that the rain area delineated by the PNN method presented the lowest FAR and highest POD, indicating that this method generated the least false alarm and misdetected precipitation. The CSI, ETS, HK and HSS of the PNN were also higher than the other three methods. Figure 6: The false alarm ratio (FAR), probability of detection (POD), equitable threat score (ETS), critical success index (CSI), Hanssen and Kuiper discriminant (HK) and Heidke Skill Score (HSS) of the threshold method of the Scatter Index (SI_threshold), probabilistic neural network (PNN), TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product (TRMM 2A12) and dynamic cluster K-means (Kmean) using the summarized test datasets of the 16 rain events.
# A Distributed Terahertz Metasurface with Cold-Electron Bolometers for Cosmology Missions
## 4 Experimental Check  
As can be seen in Figure 8, the backward wave oscillator (BWO) acts as a tuneable wave source, whose signal is split by the chopper between the pyroelectric detector and the optical window of the He3 sorption cryostat. The pyroelectric is used as a reference detector for simultaneous calibration of power emission from BWO. Half of the BWO power passes through the chopper and enters the optical window of the cryostat. The window contains a combination of high- and low-pass quasioptical filters and a set of cold neutral density filters which act as cold attenuators to reduce the power of blackbody radiation from the room temperature background. After the filters, the signal is collected by a silicon lens mounted in a holder on the cryostat cold plate. The chip with an array of CEBs is placed on the back of the lens and DC-connected to the room temperature electronics via biasing filters containing cold resistors.  
The resulting experimentally measured frequency response [14] normalized to the theoretically expected one is shown in Figure 9. The response decay above 350 GHz is associated with the cut-off frequency of the low-pass quasioptical filter at the cryostat optical window. Experiments with the blackbody radiations source demonstrated the voltage responsivity higher than \(10^{9}\) V/W [14].  
Figure 7: (**a**) NEP components for the 2D periodic array of cold-electron bolometers with 10 \(\times\) 10 unit cells, each containing four bolometers incorporated into a ring. Electron temperature, Te, is shown for both cases referring to the right axis. Parameters: f = 350 GHz, Po = 20 pW lamp = 5 \(\mathrm{fA}/\mathrm{Hz}^{1/2}\), Vamp = 5 \(\mathrm{nV}/\mathrm{Hz}^{1/2}\) (JFET), R = 1 k\(\Omega\), \(\Lambda\) = 0.02 \(\mathrm{\SIUnitSymbolMicro m}^{-3}\), T = 300 mK, (**b**) dependence of the noise component and electron temperature on optical power of absorbed signal.