esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 2 TDS-OFDM System Model
The received signal frame \(\left\{r_{i,k}\right\}_{k=0}^{N+M-1}\) can be expressed as:  
\[r_{i,k}=u_{i,k}+n_{i,k} \tag{6}\]  
Figure 1: TDS-OFDM, ZP-OFDM and CP-OFDM signal structure comparison in the time domain. where,  
\[u_{ijk}= \begin{cases}d_{i-1,k+N}+x_{i,k}\ 0\leq k<L_{ch}-1\\ x_{i,k}\ L_{ch}-1\leq k<N+L_{ch}-1\\ d_{ijk}\ N\leq k<N+M-1\end{cases}  \tag{7}\]  
and \(n_{ijk}\) is the channel additive white Gaussian noise (AWGN). Figure 2 shows the corrupted received sequence; in the received TDS-OFDM frame, the guard interval would affect the OFDM data part, and the tail of OFDM data spreads into the guard interval of the next OFDM data block. The IBI between the OFDM data blocks and its PN sequence contains different features, and, as the OFDM data blocks are unknown, it is hard to obtain perfect estimation over the UWA channel. Completely removing the occurring IBI is a hard job, where OFDM data blocks with large frame sizes are used in the UWA channel to improve spectral efficiency, but it increases the computation complexity of removing the IBIs that occurred in the PN training sequence. Thanks to compressive sensing theory and the sparsity nature of the UWA channel, the authors in [7] used a small IBI-free region for semi-perfect compressive sensing-based channel estimation. With semi-perfect channel estimation and known PN training sequence at the receiver side, the mutual interference of the OFDM data block can be removed, and the BER of the acoustic-based TDS-OFDM can be significantly improved.
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
###### Abstract  
Using the plane alignment design of the Wuhan-Maqin highway as the background of this study, based on the Fluent-Euler two-fluid model, the response law of the wind-sand flow to the embankment under different angles, different curve radii, and different surface windward factors were studied. Additionally, the accuracy of the numerical simulation was verified by the distribution of sand accumulation on the embankment site. The results show that when the wind-sand flow passes through the embankment, and when the angle between the wind direction and the straight embankment gradually decreases from 90\({}^{\circ}\) (vertical) to 0\({}^{\circ}\) (parallel), the speed-division range is correspondingly shortened. Under the influence of plane alignment, the diversion effect leads to a certain difference in the flow-field structure at different positions of the line. The convex windward embankment has the effect of dredging the wind-sand flow outward. The concave windward embankment has the effect of gathering the wind-sand flow inward. The dredging and gathering effects of the flat curve on the wind-sand flow decreases with the increase in the radius. In the plane linear design, line direction should be parallel to the dominant wind direction as much as possible. If a flat curve needs to be set, the convex windward curve should be given priority, and a large radius curve should be selected as much as possible.  
wind-sand flow; numerical simulation; highway embankment; straight line; curve Article
## 3 Attack Surface Description
### Radio Access Control
The study [26] demonstrates successful downgrades without the need for special software and with a low inherent difficulty. In particular, Apple iOS and macOS stations automatically connect to a WPA2-only SSID, even if the initial connection was established using simultaneous Authentication of Equals (SAE) at a WPA3-TM-enabled access point, potentially exposing information needed to crack the pre-shared key. However, Windows and Network Manager, which rely on stored Authentication and Key Management (AKM) for connection establishment, remain unaffected if the previous connection was made using WPA3 [27; 28]. Enterprise Wi-Fi authentication operates in a more robust and secure manner compared to simpler authentication modes. In an enterprise environment, organizations implement WPA/WPA2-Enterprise or WPA3-Enterprise authentication protocols to enhance the security of their Wi-Fi networks. This method goes beyond the use of pre-shared keys and employs a more sophisticated approach by integrating the 802.1X authentication standard. In WPA/WPA2-Enterprise or WPA3-Enterprise, each user or device that connects to the Wi-Fi network is assigned unique credentials, such as a username and password or digital certificates. The authentication process is centralized and managedby a RADIUS server, which validates the credentials provided by users or devices. This personalized authentication improves security by ensuring that each connection is uniquely identified and authorized. This mode is particularly advantageous in business settings where multiple users with distinct access levels require secure and controlled access to the Wi-Fi network. Additionally, the 802.1X standard facilitates the dynamic assignment of encryption keys for each session, further strengthening the overall security posture of the enterprise Wi-Fi network.
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 3 Modelling Aggregated Traffic Break-Down
### Objective 2: State Modelling and Profiling
#### 3.2.1 Model Selection
The data sequences identified for the two states show high similarities, while those belonging to different states resemble dissimilarities. The Pearson correlation of the two states' data on average for different work-cycles is 0.58. Considering the value range of 0 to 1, with the highest positive correlation at 1 and the highest negative correlation at 0, the acquired correlation value indicates a very small correlated behavior of the states. The distribution of the data points also supports dissimilarities of the identified states, Figure 3(c).
## 4 Empirical Quasi-Static Model of Single cable
### Empirical Relationship between \(b_{\pi}\) and \([\mathbf{B}^{d}\ \rho]\)  
#### 4.2.1 Relationship between \(b_{\pi}\) and D  
In order to comfortably obtain the complete relationship between \(b_{\pi}\) and \(\left[\begin{array}{cc}D&\beta&\rho\end{array}\right]\), the relationship between \(b_{\pi}\) and one of the mentioned parameters are considered first and that results in new parameters. These parameters now just depend on the rest and so we recursively repeat the procedure until the last parameter. In our case, the order of execution will be \(D\), \(\rho\) and \(\beta\). By variating \(D\), fixing \(\beta\) and \(\rho\), and using linear regression to establish (21), the relationship between \(b_{\pi}\) and \(D\) can be guessed by forming the scatter plot in Figure 6.  
According to the results illustrated in Figure 6, the relationship between \(b_{\pi}\) and \(D\) can be expressed as in Equation (27). The parameter of this equation changes if \(\rho\) changes.  
\[b_{\pi}=h_{b}(\rho)\cdot D \tag{27}\]  
Figure 6: Relationship between \(b_{\pi}\) and \(D\) with different \(\rho\).
## 5 Simulation Results  
This section compares the two methods used to prevent the quality of service to users from deteriorating in the event of network congestion. In the first method, traffic is routed through different shortest paths in case of congestion. Secondly, the SDN-NFV based dynamic network scaling method proposed in this study was applied. Both methods were implemented in a simulation environment and their performance was compared. The average data rate, one of the QoS parameters, was considered as a performance indicator.  
In the simulation environment, flows were started simultaneously according to the traffic characteristics in Table 5. After a while, it was observed that the data transfer rates decreased and network congestion occurred. Initially, in the event of network congestion, routing was performed through different shortest paths using the Dijkstra algorithm through the SDN controller without scaling. Using this method, the average data rates at the receiver were measured to be 46.9 Mbps for Flow 1, 9.95 Mbps for Flow 2 and 43.9 Mbps for Flow 3 per stream. When this method is evaluated in terms of performance rate, it can be seen that the expected throughput values at the receiving end are achieved by 93.8% for Flow 1, 99.5% for Flow 2 and 87.8% for Flow 3.  
When the SDN-NFV based dynamic network scaling method proposed in this study is applied, the average data rates at the receiver are measured to be 49.8 Mbps for Flow 1, 9.96 Mbps for Flow 2, and 49.8 Mbps for Flow 3 per stream, respectively. When evaluated as a performance rate, it was found that the expected data rates for all flows were achieved at a rate of 99.6%. Table 6 shows the average throughputs achieved per stream for both methods.  
In Figure 11, traffic routing over different shortest paths and simulation results of the proposed SDN-NFV-based dynamic network scaling method are shown graphically.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Flow No**} & **Traffic Routing Using Different** & **SDN–NFV-Based Dynamic** \\  & **Shortest Paths** & **Network Scaling Method** \\ \hline Flow 1 & 46.9 Mbps & 49.8 Mbps \\ Flow 2 & 9.95 Mbps & 9.96 Mbps \\ Flow 3 & 43.9 Mbps & 49.8 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 6: Traffic Routing over Different Shortest Paths and SDN–NFV-Based Dynamic Network Scaling Simulation Results.  
Figure 11: Comparison of system performance based on average data rate.
## 4 Results and Discussion
### Possible Example Use-Cases
#### 4.2.2 Simulating System Performance over a UWAC Link  
We begin by generating a sequence of pseudo-random binary numbers (PNs) through the use of a linear-feedback shift register (LFSR). The LFSR generates numbers with a value of either \(\mathbf{e}+1\) or \(-1\), each with an equal probability. The PN sequence is then subjected to 3 GPP Turbo coding [25] using a rate-\(1/2\) Turbo code. Following this, the coded sequence is mapped onto a differential quadrature phase shift keying (DQPSK) constellation. The next steps involve computing the inverse fast Fourier transform (IFFT), appending a cyclic prefix (CP), and modulating the baseband sequence with the real part of a complex exponential carrier signal. The sequence is upsampled and multiplexed using orthogonal frequency  
Figure 8: Transmitted signal waveform and spectrum.  
Figure 9: Received signal waveform.  
division multiplexing (OFDM). The resulting signal is transmitted through our Simulink-based channel model. On the receive side, the passband sequence is demodulated with a complex exponential carrier. The OFDM demodulator removes the CP, computes the fast Fourier transform (FFT), and demaps the received DQPSK sequence. It employs a soft decision-based demodulation approach using approximate log-likelihood ratios (LLRs). Approximate LLRs provide reliable results while avoiding not-a-number (NaN) errors that tend to occur at high signal-to-noise ratios (SNRs) when using exact LLRs. This is because exact LLRs involve computing exponentials of very large or very small numbers using finite-precision arithmetic, which ultimately results in high bit-error rates (BERs) [26] due to numerical instabilities.  
Next, we simulate the BER performance of the above formulated (a) DQPSK-OFDM 1/2-rate Turbo coding for the single-transmit-single-receive system; (b) DQPSK-OFDM 1/2-rate Turbo coding for \(4\times 4\) multiple-transmit-multiple-receive system for four different use-case scenarios (i) SNR = 12 dB, message length = 1024, CP length = 64, roll-off factor of the transmit and receive filters = 0.2, sampling frequency = 300 Hz, upsampling factor = 24, \(\chi=0\), \(K=0\), \(Q=6\), \(L=500\), (ii) SNR = 12 dB, message length = 1024, CP length = 64, roll-off factor of the transmit and receive filters = 0.2, sampling frequency = 300 Hz, upsampling factor = 24, \(\chi=0.05\), \(K=0\), \(Q=6\), \(L=500\), (iii) SNR = 12 dB, message length = 1024, CP length = 64, roll-off factor of the transmit and receive filters = 0.2, sampling frequency = 300 Hz, upsampling factor = 24, \(\chi=0.05\), \(K=3\), \(Q=6\), \(L=500\), and (iv) SNR = 12 dB, message length = 1024, CP length = 64, roll-off factor of the transmit and receive filters = 0.2, sampling frequency = 300 Hz, upsampling factor = 24, \(\chi=1.2\), \(K=5\), \(Q=6\), \(L=500\). A comparative plot for BER curves is presented in Figure 10.
## 7 Experimental Setup  
We have performed a set of simulations using NS-2.35 [28; 29] developed by Monarch research group. The simulations are conducted on a Lenovo G-580 machine with Intel Core-i3 processors of 2.66 GHz and 4-GB SDRAM running in a Ubuntu-16-Intel-64 bit operating system. NS-2 simulator has good support for simulating complete wireless network protocol model from physical and data link layer, Mac layer and routing layer to application layer. The basic parameters of our simulation are defined in Table 2.
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
###### Abstract  
With the density of wireless networks increasing rapidly, one of the major goals in next-generation wireless LANs (Local Area Networks) is to support a very dense network with a large number of closely deployed APs (Access Points) and crowded users. However, the CSMA (Carrier-Sense Multiple Access)-based medium access control of current wireless network systems suffers from significantly degraded performance when the network becomes dense. Recent WLAN (Wireless Local Area Networks) standards include measures for increasing spatial reuse such as BSS (Basic Service Set) coloring, but the schemes based on BSS coloring such as OBSS/PD (Overlapping BSS/Preamble Detection) have limitations in improving spatial reuse. In this paper, we propose a spatial reuse method for uplink which can utilize BSS color and proximity information to improve the efficiency of carrier sensing and thus spatial reuse. Specifically, through the BSS color and the proximity information, a node receiving a preamble can figure out how far the receiver of the ongoing traffic is located. This information is used to determine whether the node should aggressively start transmitting or defer its transmission to protect the ongoing transmission. Simulation results show that the proposed method outperforms existing methods in terms of throughput and fairness.  
wireless LAN; spatial reuse; medium access control; BSS color; proximity information Article  
Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity  
11/074. [[https://doi.org/10.3390/app112211074](https://doi.org/10.3390/app112211074)]([https://doi.org/10.3390/app112211074](https://doi.org/10.3390/app112211074))
## 4 Results and Discussion
### Attenuation Behavior of Fiber-Reinforced Materials
The differences are in part due to voids in our samples because of out-of-autoclave curing. It will be useful to measure the attenuation in aerospace-grade T700 CFRC samples to understand the origin of high attenuation. However, the low attenuation in the L direction seems to suggest that our samples are of good quality. The final composite group contains Kevlar epoxy laminates. These plates were made at Avtek, Inc. (Camarillo, CA, USA), using woven fabric. Attenuation value was high, even in the fiber direction, reaching 1406 dB/m at 1 MHz. In the S direction, the cubic frequency spectrum was found, however without the damping term. Because of this cubic dependence, the S direction had lower \(\alpha\) values than the fiber direction in the plane, until almost reaching 2 MHz. This is a peculiar feature of KFRCs. KFRCs have been known as high damping materials since they first appeared in the 1980s. Tests R33 and R34 confirm this quantitatively. This section presented the results of attenuation measurements on continuous fiber-reinforced composites, namely GFRCs, CFRCs, and KFRCs. Because of anisotropic elastic and attenuation behavior, diffraction correction terms were not applied, while the transmission coefficient was included. In the fiber direction, the attenuation behavior was consistent and the levels were relatively low at 70-155 dB/m at 1 MHz. In 60% of the off-fiber tests, the Biwa relation of linear and cubic spectra fitted the attenuation well. This cubic term comes from the scattering of wave incidents normal to parallel fibers. Newer T700 CFRCs gave higher attenuation values in the off-fiber orientation, possibly owing to the matrix properties, as well as voids.
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 3 CNT-Based Materials for X-Ray and Gamma Ray Attenuation
Their observations revealed a positive correlation between MWCNT loading and the effectiveness of gamma radiation shielding in NRL composites. This enhancement in shielding properties suggests that incorporating MWCNTs into NRL significantly improves its suitability for radiation shielding applications. These findings are consistent with previous research demonstrating the effectiveness of carbon-based materials, such as CNTs, in enhancing the radiation shielding capabilities of polymer composites. CNTs often contain a significant weight fraction (up to 20%) of iron catalyst depending on the synthesis method. The iron contributes to the shielding effect of CNT materials. CNT can be used for both EMI and higher energy radiation shielding [53; 54; 55; 56]. [PERSON], [PERSON] et al. [57] conducted X-ray attenuation experiments using various carbon-based nanomaterials, including single-wall carbon nanotubes (SWCNTs), multi-wall carbon nanotubes (MWCNTs), single-wall carbon nanohorns (SWCNHs), fullerenes (C60), graphene oxide (GO), and a fabric-textile coated with MWCNTs. These materials were compared with highly oriented pyrolytic graphite (HOPG). The study found that CNTs and CNT-coated fabrics efficiently absorb X-rays, with the attenuation coefficient of CNTs increasing as the sample thickness decreased. Their experiments produced results suggesting that CNT materials can be used to produce efficient and lightweight X-ray safety equipment and devices. The study also reported unexpectedly high X-ray attenuation in CNTs, with the mass attenuation coefficient of CNTs being 20-50% greater than that of HOPG and fullerenes. This increased attenuation was attributed to the nanoscale morphology of CNTs, which traditional absorption theory does not account for. Additionally, coating a textile fabric with CNTs resulted in enhanced X-ray attenuation of 70% for a textile thickness of 25 mm. The researchers proposed that the high shielding effect of CNTs could be due to their small size and cylindrical morphology. However, this conjecture needs further study to validate. Furthermore, the study considered the catalyst impurities in CNTs, which may have improved the attenuation properties. CNTs synthesized using the floating catalyst method can contain about 20 wt% of Fe catalyst, and intentionally adding impurities could enhance the attenuation properties of CNT-based sheets. This finding highlights the potential for using CNT materials in the fabrication of lightweight and efficient X-ray safety equipment and devices.
## 2 Analysis of DC Arc Fault Characteristics of PV System
### The Influence of Working Current Noise of PV System on Fault Detection  
Whether from the perspective of time or frequency domains, the DC arc current in PV systems will show high-frequency strikes, especially within the bandwidth of 100 kHz. However, PV inverters will introduce similar noises to the current due to their PWM operation, the amplitude of which may be the same as or even higher than that of arc signals. As a result, it will be difficult to distinguish the arc fault from the normal operation using the amplitude differences.  
DC arc is an inherently random phenomenon that can be observed even in a short time window of a few milliseconds, while PWM noise is rhythmic due to periodic modulation and system inertia. Therefore, the random behavior of DC arcs can be used for arc fault detection.  
Due to its PWM control, the PV inverter emits high-frequency noise to the current during normal operation. The PV inverter noise may overlap with the arc signal in frequency bandwidth, resulting in nuisance tripping. Therefore, we have to take measures to discriminate DC arcs from the normal operation of a PV system. The PSD can describe the random signals, which defines the strength of the signal's power contents as a function of frequency. Figure 3 shows the waveform and the corresponding PSD of a current time series. The PV inverter noise can be regarded as a stationary random signal within a short time slot (e.g., 10 ms) due to the inertia of the system. However, DC arcs exhibit different randomness characteristics that make them easy to distinguish from interfering signals.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Load** & **Voltage/Current** & **Gap** & **Speed** \\ \hline \multirow{2}{*}{PV Inverter \#A} & 490 V/7 A & 0.8 mm & \\ \cline{2-4}  & 810 V/14 A & 1.1 mm & \\ \hline \multirow{2}{*}{PV Inverter \#B} & 490 V/7 A & 0.8 mm & \\ \cline{2-4}  & 810 V/14 A & 1.1 mm & \\ \hline \multirow{2}{*}{PV Inverter \#C} & 490 V/7 A & 0.8 mm & \\ \cline{2-4}  & 810 V/14 A & 1.1 mm & \\ \hline \end{tabular}
\end{table}
Table 2: Detailed parameters for the test platform.  
Figure 2: The close-up view of the arc generator.
## 2 Materials and Methods
### Terahertz Time-Domain Spectroscopy  
Terahertz time-domain spectroscopy (THz-TDS) is a novel NDT technique [39; 40] known for its strong penetration capability, transparency to various materials, and non-ionizing nature when applied to non-metallic materials. In this study, a reflection-based THz-TDS technique was utilized to examine the thermal barrier coating samples, aiming to obtain THz time-domain data. The propagation of reflected terahertz waves within the thermal barrier coating is shown in Figure 3.  
Figure 1: Particle size of powder: (**a**) 25–55 \(\upmu\)m; (**b**) 45–106 \(\upmu\)m.  
Figure 3: Schematic diagram of terahertz propagation in thermal barrier coatings.  
The fundamental principles of THz-TDS involve observing the propagation of short-pulsed electromagnetic waves within the tested object, as shown in Figure 4. To generate broad-spectrum terahertz pulses, femtosecond lasers and optical pulse sources are commonly used as light sources, employing nonlinear optical mechanisms. These pulses propagate through the sample under the guidance of terahertz optical components, interacting with the object being tested. The test sample exhibited distinct absorption, transmission, and reflection characteristics toward terahertz waves, which were closely linked to their physical, chemical, and structural properties. Consequently, analyzing the interaction between terahertz waves and the test sample allowed for the retrieval of valuable information about the sample. Subsequently, the terahertz pulses passed through the sample and their changes in amplitude and phase were measured using a detector.  
The propagation and interaction of terahertz waves within the TBCs were influenced by factors such as the coating's porosity, distribution of material composition, and microstructure. Hence, THz-TDS offered a highly sensitive approach to detecting the TBCs' porosity and its reflective properties at different frequencies [41]. In this study, THz-TDS was employed to investigate the TBCs' porosity on aero-engine blades, with a focus on obtaining time-domain data. Specifically, the obtained terahertz time-domain data were subjected to fast Fourier transform (FFT) to obtain frequency-domain spectra, phase spectra, and reflectance spectra. Subsequently, data analysis and extraction of characteristic parameters were conducted on these spectra.