esa-sceva/satcom-chunk-collection huggingface dataset:
# Sparse Plane Wave Approximation of Acoustic Modes to Address Basis Mismatch
## 1 Introduction
While locating sources in a highly reverberant environment, the unknown room reflection energy ratio has been estimated. The parametric sparse Bayesian learning (SBL) has also been used to simultaneously estimate the unknown monopole positions and path loss exponent [23]. The pre-discretized grids near the sources were first selected, then a combination of the two-dimensional Newton optimization and a feedback mechanism was used to approximate the actual positions for beamforming [24]. In this study, PW discretization involves adaptively optimizing the propagating directions, including inclinations and azimuths, which have rarely been focused upon in previously published literature. This study proposes a combined two-stage method to address the problem of basis mismatch in the PW approximation of acoustic modes. First, we use \(\ell_{1}\)-norm relaxation to solve the coarse PWAs, select dominant PW directions, and prune the other directions. Then, we use the extracted dominant directions to construct a low-dimensional parameterized dictionary that considers the PW directions as unknown parameters instead of densely discretizing the direction space to construct a high-dimensional known dictionary. We solve the PWAs and optimize the PW directions by applying SBL. This is facilitated by the variational Bayesian expectation and maximization (VBEM) technique [22; 25; 26], where the two-dimensional Newton optimization is derived to adaptively modify the inclinations and azimuths. A similar method has not been used in the field of sound reconstruction thus far. Numerical simulations show that the proposed method can reduce the basis mismatch in the PW approximation and improve the approximation accuracy compared with conventional methods, where the computational cost is slightly increased. Additional benefits of the proposed method include a high reconstruction accuracy at extrapolated field points outside the local measurement region and robustness under low signal-to-noise ratios (SNRs). The paper is organized as follows. The problem of the PW approximation of acoustic modes is stated in Section 2, and the proposed two-stage method is described in Section 3. The simulation results are presented to validate the effectiveness of the proposed method in Section 4. A discussion is presented in Section 5, and conclusions are summarized in Section 6.
## 3 Johnson-Cook Model  
The Johnson-Cook model is able to accurately analyze and predict stress-strain behavior for ductile materials, and its applicability as well as accuracy has been studied thoroughly in the literature for steel or aluminum alloys under combined conditions of large deformation, high strain rate as well as elevated temperatures focusing on metal forming or impact performance [16; 17; 18; 19]. The JC stress model is expressed in Equation (1).  
\[\sigma=(A+B\epsilon^{n})\Big{(}1+C\ln\hat{\epsilon}^{*}\Big{)}(1-T^{*m}) \tag{1}\]  
in which \(\sigma\) represents von Mises or equivalent stress, \(A\) is the yield stress of tested material under reference conditions (strain rate and temperature), \(B\) is the strain hardening constant, \(n\) is the strain hardening coefficient, \(C\) is a coefficient of strain rate resulting in post-yield strengthening of the material, \(\hat{\epsilon}^{*}\) is the dimensionless strain rate, \(T^{*}\) homologous  
Figure 6: Engineering stress–strain (**a**) and true stress–strain (**b**) curves for uniaxial tensile tests under different strain rates.  
Figure 7: Engineering stress–strain (**a**) and true stress strain (**b**) curves for uniaxial tests with different notch sizes.  
temperature, and \(m\) is a thermal softening coefficient. It can be observed that the JC model can be separated into three factors based on the static stress condition, influence of strain rate and, finally, influence of temperature when looking at the three parentheses from left to right. In Equation (1), the \(\dot{\tilde{\epsilon}}^{*}\) parameter as well as \(T^{*}\) can be defined in Equations (2) and (3).  
\[\dot{\epsilon}^{*}=\frac{\dot{\epsilon}_{p}}{\dot{\epsilon}_{0}} \tag{2}\]  
\[T^{*}=\frac{T-T_{ref}}{T_{m}-T_{ref}} \tag{3}\]  
where \(\dot{\epsilon}_{p}\) is the accumulated plastic strain and \(\dot{\epsilon}_{0}\) is the reference strain rate which, in this work, was taken as 0.001 s\({}^{-1}\). \(T_{m}\) is defined as the melting temperature of the material and \(T_{ref}\) is the reference temperature. For the scope of this research, performance of the high-strength medium carbon steel material was investigated under quasi-static and medium dynamic strain rates as well as varying stress concentration conditions, but the temperature factor was not considered since the primary failure factor of tensioned members is usually due to corrosion, as mentioned in previous sections.
## 3 System Model and Performance Analysis
### Throughput of PU  
The probability that a subchannel is selected by at least one STA can be given as follows:  
\[p_{tr}=1-\bigg{(}1-\frac{\theta}{r}\bigg{)}^{n}. \tag{20}\]  
The probability that the data of the STA are transmitted successfully can be given as follows:  
\[p_{s}=\frac{n\frac{\theta}{r}\Big{(}1-\frac{\theta}{r}\Big{)}^{n-1}}{p_{tr}}. \tag{21}\]  
Thus, the throughput of the random competition phase for high- and low-priority patients is expressed as follows:  
\[T_{P1}=\frac{\min(r,n)K_{f}p_{tr}p_{s}}{t_{succ}}. \tag{22}\]  
The backoff delay (\(E[t_{bo}]\)) can be defined by the following relation:  
\[E[t_{bo}] =DIFS+\sum\limits_{i=0}^{m}\sum\limits_{k=0}^{d_{i}-1}(k+1)\pi(i, w_{i-1}+k)slot \tag{23}\] \[=DIFS+\sum\limits_{i=0}^{m}\frac{-d_{i}^{2}+3d_{i}+1}{3}\bigg{(} \frac{p_{1}p_{2}}{1-p_{1}p_{2}}\bigg{)}^{i}\pi(0,\ 0)slot\] \[=DIFS+\frac{\frac{p_{1}p_{2}+p_{1}p_{2}\sigma_{2}{}^{m}-1}{12p_{1 }p_{2}-4}+\frac{\sigma_{1}}{6p_{1}p_{2}-2}+\frac{w_{0}{}^{2}(p_{1}p_{2}-1) \big{(}2^{2m+2}\sigma_{2}{}^{m+1}-1\big{)}}{60p_{1}p_{2}-12}}slot,\]  
where \(\sigma_{1}=w_{0}\big{(}p_{1}p_{2}+2^{m+1}p_{1}p_{2}\sigma_{2}{}^{m}-1\big{)}\) and \(\sigma_{2}=(-p_{1}p_{2})/\big{(}p_{1}p_{2}-1\big{)}\).  
The average time to successfully transfer data (\(t_{succ}\)) can be defined by the following relation:  
\[t_{succ}=E[t_{bo}]+t_{BSR}+t_{TF}+t_{f1}+t_{MBA}+4t_{f2}+4 SIFS, \tag{24}\]  
where \(t_{BSR}\) is the time to transfer the BSR frame, \(t_{TF}\) is the time for the TF frame transmission, and \(t_{MBA}\) is the transmission time for the MBA frame, and both are fixed values.  
Substituting Equations (20), (21), and (24) into (22), the following expression is obtained:  
\[T_{1}=\frac{\min(r,n)K_{f}n\frac{\theta}{r}\Big{(}1-\frac{\theta}{r}\Big{)}^{ n-1}}{t_{succ}}. \tag{25}\]The probability that at least one STA selects a remaining subchannel to transmit is as follows:  
\[p_{tr}^{*}=1-\left(1-\frac{1}{r-n\theta(1-p_{1})}\right)^{n_{1}\theta p_{1}}. \tag{26}\]  
The probability of successful transmission on the remaining subchannel for the high-priority STA collision occurred is expressed as follows:  
\[p_{s}^{*}=\frac{n_{1}\theta p_{1}\frac{1}{r-n\theta(1-p_{1})}\left(1-\frac{1}{ r-n\theta(1-p_{1})}\right)^{n_{1}\theta p_{1}-1}}{p_{tr}^{*}}. \tag{27}\]  
Then, the expression for the throughput of high-priority patients transmitted on the remaining subchannels is derived.  
\[T_{2} =\frac{\min\left(r_{1},n_{1}^{*}\right)K_{f}p_{tr}^{*}p_{s}^{*}}{t_ {succ}} \tag{28}\] \[=\frac{\min(r-n\theta(1-p_{1}),n_{1}\theta p_{1})K_{f}n_{1}\theta p _{1}\frac{1}{r-n\theta(1-p_{1})}\left(1-\frac{1}{r-n\theta(1-p_{1})}\right)^{ n_{1}\theta p_{1}-1}}{t_{succ}}.\]  
Thus, the throughput of PU is \(T_{P}=T_{1}+T_{2}\), we have:  
\[T_{P} =\frac{\min(r,n_{1})K_{f}n\theta(1-\theta)^{n-1}}{t_{succ}} \tag{29}\] \[+\frac{\min(r-n\theta(1-p_{1}),n_{1}\theta p_{1})K_{f}n_{1}\theta p _{1}\frac{1}{r-n\theta(1-p_{1})}\left(1-\frac{1}{r-n\theta(1-p_{1})}\right)^{ n_{1}\theta p_{1}-1}}{t_{succ}}.\]
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 2 Background
### Artificial Intelligence Technology in Autonomous Ships  
#### 2.2.1 Situational Recognition and Detection  
According to the International Maritime Organization's 1974 SOLAS (Chapter 5, Rule 19 of the 1999/2000 Amendment), the installation of an AIS is mandatory for most ships, except for passenger ships weighing less than 300 tons. The AIS reports static and dynamic data that are essential for the recognition of maritime traffic. The combination of AIS data with other data requires a high capability of recognition and situational judgment, and methods capable of such include neural networks, Bayesian networks, and Gaussian processes [9]. Additionally, deep-learning-based techniques that detect anomalies in maritime regions using numerous data obtained from AIS are being studied, such as variational recurrent neural networks and artificial neural networks (ANNs) [10; 11]. The disadvantage of using convolutional neural networks (CNNs) to detect objects at sea is that it requires extensive computational work; as this is not always possible in a maritime environment where the size of the object in the image varies with distance, the region proposal network (RPN) is proposed as one of the ways to solve this issue. RPNs are useful when the area of an object in pixels is very small (e.g., small boats) [12].  
The main challenges in recognizing a ship's overall situation are safety and the detection of anomalies. As shown in Figure 2, an AIS, a global navigation satellite system GNSS, camera images, audio signals, and sensor data can be combined to recognize the circumstances and detect anomalies. For example, if the detected and classified results match the meta data of the AIS message, it can be considered that the situational recognition is normal. This process serves as the basis for the next step, which is to recognize the identified surrounding objects and calculate the collision probabilities using the planned path.  
Figure 2: Overall view of an AI-driven maritime situational awareness system.
## 8 JADEDO Box-Plot Analysis  
The box-plot analysis of the JADEDO optimizer across different functions provides insights into the distribution and variability of the best fitness scores achieved during optimization, as seen in Figure 7. JADEDO exhibits consistent stability across CEC 2022 benchmark functions (F1-F12), as indicated by its tightly packed boxplots and minimal variance. This suggests it is a reliable optimizer with low fluctuation in results. However, its best scores are generally lower than those of other optimizers, implying limited exploration capabilities. FLO and CPO, in contrast, often achieve significantly higher best scores but with much wider interquartile ranges and greater outliers, indicating inconsistent performance. Comparisons with FVIM, STOA, and SOA show that JADEDO remains among the lower-performing optimizers, particularly in complex multimodal functions such as F10, F11, and F12. While JADEDO excels in stability, it struggles in highly complex landscapes where broader exploration is necessary. Therefore, it is suitable for scenarios requiring precision and consistency but may not be the best choice for highly rugged optimization problems, where more exploratory optimizers like FLO, CPO, and ROA demonstrate stronger performance.  
Figure 6: Average fitness for CEC2022 (F1–F12).
# Implementation of a Cross-Layer Sensing Medium-Access Control Scheme
###### Abstract  
In this paper, compressed sensing (CS) theory is utilized in a medium-access control (MAC) scheme for wireless sensor networks (WSNs). We propose a new, cross-layer compressed sensing medium-access control (CL CS-MAC) scheme, combining the physical layer and data link layer, where the wireless transmission in physical layer is considered as a compress process of requested packets in a data link layer according to compressed sensing (CS) theory. We first introduced using compressive complex requests to identify the exact active sensor nodes, which makes the scheme more efficient. Moreover, because the reconstruction process is executed in a complex field of a physical layer, where no bit and frame synchronizations are needed, the asynchronous and random requests scheme can be implemented without synchronization payload. We set up a testbed based on software-defined radio (SDR) to implement the proposed CL CS-MAC scheme practically and to demonstrate the validation. For large-scale WSNs, the simulation results show that the proposed CL CS-MAC scheme provides higher throughput and robustness than the carrier sense multiple access (CSMA) and compressed sensing medium-access control (CS-MAC) schemes.  
Keywords:cross-layer; compressed sensing; medium-access control; wireless sensor networks +
Footnote †: journal: Sensors
# Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network
## 2 System Description
### Hybrid Energy Storage System  
The structure of the HESS directly impacts on the vehicle's performance. Regardless of the lithium-ion battery and supercapacitor as the HESS energy source, the study in [18] has discovered that the effectiveness of HESS performance mostly depends on the DC/DC converter. Therefore, the effective HESS clearly could not be achieved without neglecting its topology and components order. In general, the HESS topologies can be categorized into three typical types: passive parallel topology, fully active topology, and semi-active topology [19]. The three different topologies have advantages and disadvantages in terms of building cost, performance, and control capability. The passive parallel topology would be low in building cost with the trade of performance and the system control capability due to the absence of a DC/DC converter. Meanwhile, the semi-active topology only utilizes one DC/DC converter to provide tolerable performances [19].  
Here, we consider a fully active parallel HESS topology, as shown in Figure 1, due to its superior control ability and performance [20]. The topology utilizes two DC/DC converters to connect two energy sources where the battery is the main power and the SC is the auxiliary power. The SC is connected through a bidirectional the DC/DC converter to the DC bus, which can be operated in boost or buck mode depending on the control decision. The battery is connected to the DC bus through a boost DC/DC converter to feed EV power demand and maintain the DC bus voltage at 800 volts. The battery and SC could simultaneously supply power and total power required by EV is shared between battery and SC as defined by (1), where \(P_{d}\), \(P_{butt}\), and \(P_{sc}\) denote total power demand, power battery, and power SC, respectively.  
\[P_{d}=P_{butt}+P_{SC} \tag{1}\]  
An issue that arises with the management strategy to control the HESS is mainly regarding to the effectiveness of the system in protecting the battery by using the SC [8]. Most existing strategies focus on generating references current or power ratio between battery and SC. Accordingly, both the DC/DC converters would deliver the power based on the ratio while guaranteeing that each part of the system performs well with high reliability and performance. Thus, a control algorithm for HESS power split is required in this setup. This paper applies a fuzzy logic controller to determine the power proportion  
Figure 1: Hybrid energy storage system model.  
between the battery and the SC according to power demand, the SOC of the battery, and the SOC of the SC. The analysis of the control algorithm is presented in Section 3.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 3 Problem Formulation and Analysis
### Zone 1  
Thus, the received signal at the D2D end user can be expressed as,  
\[Y_{D}^{1}=\sqrt{P_{D}}h_{D2D}d_{D2D}^{-\alpha/2}x_{1}+\sqrt{P_{C}}h_{C2D}d_{ C2D}^{-\alpha/2}x_{2}+n_{D} \tag{4}\]  
Similarly, the received signal at the cellular user from the BS can be given as follows,  
\[Y_{C}^{1}=\sqrt{P_{C}}h_{C2B}d_{C2B}^{-\alpha/2}x_{2}+\sqrt{P_{D}}h_{D2B}d_{ D2B}^{-\alpha/2}x_{1}+n_{C} \tag{5}\]  
Thus, the SINR at the respective cellular and D2D users can be derived from Equations (4) and (5) as follows,  
\[\gamma_{C}^{1}=\frac{P_{C}|h_{C2B}|^{2}d_{C2B}^{-\alpha}}{P_{D}|h_{D2B}|^{2}d_ {D2B}^{-\alpha}+N_{0}} \tag{6}\]  
\[\gamma_{D}^{1}=\frac{P_{D}|h_{D2D}|^{2}d_{D2D}^{-\alpha}}{P_{C}|h_{C2D}|^{2}d_ {C2D}^{-\alpha}+N_{0}} \tag{7}\]Now, the SINR at the receiver cellular user should be above a certain predefined threshold given by \(\gamma_{th}\), i.e.,  
\[\gamma_{\text{C}}^{1}\geq\gamma_{th} \tag{8}\]  
Furthermore, the maximum tolerable interference that the cellular user can withstand is given by,  
\[P_{max}^{1}=P_{D}|h_{D2B}|^{2}d_{D2B}^{-\alpha} \tag{9}\]  
Thus, by equating Equations (8) and (9), we obtain,  
\[P_{max}^{1}=\frac{P_{\text{C}}|h_{C2B}|^{2}d_{C2B}^{-\alpha}}{\gamma_{th}}-N_{0} \tag{10}\]  
The maximum transmit power, i.e., \(P_{D}=P_{dmax}^{1}\), that can be attained by the D2D users in Zone 1 can be expressed as follows,  
\[P_{dmax}^{1}=\frac{P_{\text{C}}|h_{C2B}|^{2}d_{C2B}^{-\alpha}-N_{0}\gamma_{th}} {\gamma_{th}|h_{D2B}|^{2}d_{D2B}^{-\alpha}} \tag{11}\]  
EE is an important performance metric to evaluate the proposed communication system. EE may be defined as the ratio of the D2D sum rate to the total power consumption [3]. It has become an indispensable part of green communication. This is because, in recent times, low-power equipment is required to enable D2D communication in mmWave networks. To evaluate the total power consumption in Zone 1, we considered the circuit power consumption \((P_{T})\) along with the D2D transmission power \((P_{D})\) and the incoherent diffused scattering power \(P_{S}\) at the receiver node. Thus, the total power consumption in Zone 1 can be expressed as,  
\[P_{Total}=P_{D}+P_{T}+P_{S} \tag{12}\]  
Therefore, the EE of the D2D users in Zone 1 can be expressed as,  
\[EE^{1}=\frac{Log(1+\gamma_{D}^{1})}{P_{Total}} \tag{13}\]  
Equation (13) gives us the expression for the EE of D2D users in Zone 1 considering different power consumption factors.
## 3 Results and Discussion
### Validation of Thermal Plumes
#### 3.1.2 Thermal Measurements  
In a first step, using a Flir E50 Infrared camera and a black cardboard sheet, we obtained a qualitative distribution of the temperature measurements in the thermal plume. Figures 11a and 12a are present thermographic images captured in this way, allowing for qualitative inspection of the air-temperature distributions within the thermal plume. Figures 11b and 12b present their numerical counterparts.  
Their comparison results in similar distributions, showing that the temperature gradient and values have almost the same allure and the same height for both sets of results. The comparison between the experimental and numerical results represents, as previously, a situation with a natural convective thermal plume.  
Figure 10: Velocity magnitude distributions in the sagittal plane. (**a**) Thermal manikin, PIV; (**b**) virtual passenger, CFD.  
Figure 9: Velocity magnitude distribution in the coronal plane of the manikin. (**a**) Thermal manikin-PIV, (**b**) virtual passenger, CFD.  
As we wanted to provide a quantitative comparison as well, we used a network of 24 thermocouples installed in sagittal and coronal plane above the manikin head, as explained previously, within a grid with six rows and four columns, keeping a distance of 40 mm between them (see Figure 13). Each thermocouple had an ID composed of its row number and column number, starting from the bottom-left thermocouple, as displayed in Figure 13.  
Figures 14a and 15a present a comparison of the temperature values obtained from the thermocouples and those obtained from CFD modeling at the same locations above the heads of the manikins. Blue bars represent experimental values, and orange bars represent numerical values. For the most part, the experimental results show slightly higher values than the numerical results (Figures 14a and 15a). Figures 14b and 15b show relative error distribution between numerical and experimental results. As these differences stay within the accuracy range of the measuring technique, we therefore we find that results are satisfactory.  
Figure 11: Temperature distribution in the coronal plane. (**a**) PIV, (**b**) CFD.  
Figure 12: Temperature distribution in the sagittal plane. (**a**) PIV, (**b**) CFD.  
Figure 14: (**a**) Mean temperature measurements achieved with the thermocouples in the coronal plane of the manikin. (**b**) Relative error distribution between numerical and experimental results.  
Figure 13: Thermocouple placement in the thermal plume.
# A Dual-Gas Sensor Using Photoacoustic Spectroscopy Based on a Single Acoustic Resonator
## 2 Materials and Methods
### Selection of Absorption Lines  
Infrared bands including mid-infrared and near-infrared are commonly used for gas sensing. Near-infrared laser sources are more widely used in gas sensing due to their ad  
Figure 3: Frequency response curves of the PA cell with the first longitudinal resonance.  
Figure 2: (**a**) Plot of the relationships of time and wavelength of lasers during one frequency scan. (**b**) First harmonic signal of 100 ppm NH\({}_{3}\) during one frequency scan. (**c**) First harmonic signal of 80 ppm CH\({}_{4}\) during one frequency scan. (**d**) Simultaneous recording of the PAS of 60 ppm CH\({}_{4}\) and 50 ppm NH\({}_{3}\). All measurements were taken at room temperature (around 300 K) and one standard atmosphere (atm).  
vantages of low cost and increased reliability and operation at room temperature compared with mid-infrared laser sources. However, trace gases in the mid-infrared spectrum have stronger absorption than those in the near-infrared band. DFB lasers emitting at 1512.24 and 1653.72 nm were selected in this compact PA instrument for measuring NH\({}_{3}\) and CH\({}_{4}\). For accurate NH\({}_{3}\) and CH\({}_{4}\) detection, potential spectral interference from other abundant atmospheric species (such as H\({}_{2}\)O and CO\({}_{2}\)) was also taken into account. Figure 4a shows the simulation of absorption of 400 ppm CO\({}_{2}\), 20,000 ppm H\({}_{2}\)O, 10 ppm NH\({}_{3}\) and 10 ppm CH\({}_{4}\) around 1512 nm with a temperature of 300 K, air pressure of 1 atm and absorption path length of 100 cm. Figure 4b shows the simulation of absorption of 400 ppm CO\({}_{2}\), 20,000 ppm H\({}_{2}\)O and 1 ppm CH\({}_{4}\) around 1653 nm, where the absorbance of NH\({}_{3}\) is zero. According to the HITRAN database, NH\({}_{3}\) and CH\({}_{4}\) have almost no interference with each other's absorption in the tuning wavelength regions of the DFB lasers [33].
# Improved Bio-Oil Quality from Pyrolysis of Pine Biomass in Presurized Hydrogen
###### Abstract  
The pyrolysis of pine sawdust was carried out in a fixed bed reactor heated from 30 \({}^{\circ}\)C to a maximum of 700 \({}^{\circ}\)C in atmospheric nitrogen and pressurized hydrogen (5 MPa). The yield, elemental composition, thermal stability, and composition of the two pyrolysis bio-oils were analyzed and compared. The result shows that the oxygen content of the bio-oil (17.16%) obtained under the hydrogen atmosphere was lower while the heating value (31.40 MJ/kg) was higher than those of bio-oil produced under nitrogen atmosphere. Compounds with a boiling point of less than 200 \({}^{\circ}\)C account for 63.21% in the bio-oil at pressurized hydrogen atmosphere, with a proportion 14.69% higher than that of bio-oil at nitrogen atmosphere. Furthermore, the hydrogenation promoted the formation of ethyl hexadecanoate (peak area percentage 19.1%) and ethyl octadecanoate (peak area percentage 15.42%) in the bio-oil. Overall, high pressure of hydrogen improved the bio-oil quality derived from the pyrolysis of pine biomass.  
pine sawdust; hydrogen; nitrogen; pyrolysis; bio-oil +
Footnote †: journal: Applied