esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Different-Order Multiple Crosstalk Artifacts and Illumination Analysis
Compared to Figure 5a, the illumination of the third layer is significantly increased. Figure 5c shows the illuminance of the primary wavefield and the surface-related multiplied wavefield. The illumination of the shallow layer is significantly increased compared to Figure 5a. Figure 5d illustrates the illumination intensity of the primary wavefield, surface-related multiple wavefields, and internal multiple wavefields (full-wavefield data). The full-wavefield data exhibit superior illumination performance compared to the primary. Full-wavefield migration performs forward propagation of multiples from virtual sources in the migration process, providing additional illumination for the imaging process. Figure 4: The images generated by primaries and different orders of multiples: (**a**) \(U_{B}^{i}D_{F}^{0^{\prime}}\) full-wavefield data imaging result; (**b**) \(U_{B}^{0}D_{F}^{0^{\prime}}\) primary data imaging result; (**c**) \(U_{B}^{1}D_{F}^{0^{\prime}}\) first-order surface-related multiple crosstalk; (**d**) \(U_{B}^{1}D_{F}^{0^{\prime}}\) first-order internal multiples crosstalk; (**e**) \(U_{B}^{1}D_{F}^{1^{\prime}}\) first-order surface-related multiple correct imaging; (**f**) \(U_{B}^{2}D_{F}^{2^{\prime}}\) second-order surface-related multiple correct imaging.
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 4 Simulation Results and Analysis
### Controller Design
#### 4.1.5 The Repetitive Gain \(k_{r}\)  
According to Equation (26), the range of \(k_{r}\) can be further expressed as:  
\[0<k_{r}<\frac{2 min\{cos[\theta_{s}\left(e^{i\omega}\right)+\theta_{p}\left(e^{i \omega}\right)+m\omega]\}}{max[N_{s}\left(e^{i\omega}\right)N_{p}\left(e^{i \omega}\right)]} \tag{34}\]  
where \(0\leq\omega\leq\pi\).  
According to Figure 13, the compensated system phase angle gradually changed from \(0.45^{\circ}\)-\(9.38^{\circ}\) in the frequency range less than 20 kHz, so \(min\{cos[\theta_{s}\left(e^{i\omega}\right)+\theta_{p}\left(e^{i\omega}\right) +m\omega]\}=0.987\). It is easy to know that the maximum gain of \(S\left(z\right)\) is 0 dB, so the amplitude of the completely compensated system is only determined by the gain of \(P_{0}\left(z\right)\). Thus, according to the magnitude plot of Figure 13, the system gain is changed from \(-19.6\) dB (\(0.1047\)) to \(-21.5\) dB (\(0.0841\)) until 20 kHz. Then, \(max[N_{s}\left(e^{i\omega}\right)N_{p}\left(e^{i\omega}\right)]=0.1047\) can be obtained. Therefore, \(0<k_{r}<18.85\).  
However, it is worth noting that the larger the \(k_{r}\) is, the smaller the system steady-state error is, and the faster the error convergence speed becomes, but in order to maintain a sufficient stability margin, the value of \(k_{r}\) should not be too large. Therefore, the specific value of \(k_{r}\) needs further consideration.  
According to Equation (21), define \(H\left(z\right)=Q\left(z\right)[1-k_{r}z^{m}S\left(z\right)P_{0}\left(z\right)]\), which means that the trajectory of \(H\left(z\right)\) should always be inside the unity circle. Figure 14 shows the Nyquist plot of \(H\left(z\right)\) under different values of \(k_{r}\). The plotted curves are all kept well within the unit circle. However, a smaller magnitude of \(H\left(z\right)\) means faster error convergence, a larger stability margin, and better steady-state harmonic compensation performance. Considering the steady-state error, stability margin, and model uncertainties of the system, the \(k_{r}\) is selected as nine as a compromise. Figure 15 shows the open-loop magnitude plot of the PMQR-type RC, and is compared with the generally-used RC, for which \(k_{r}\) is always set as one. Obviously, the proposed controller possesses superior gains to the generally-used RC. Furthermore, Figure 16 displays the open-loop gain diagrams of the plant \(P_{0}\) and the plant compensated respectively by the proportional controller and the proposed controller, which means that it is with the help of proportional controllers that repetitive controllers can achieve higher gains at fundamental and multiple frequencies together with superior steady-state response.  
Figure 13: Frequency characteristic of the system in different lead-links \(z^{m}\).
## 1 Introduction  
Oceans have abundant resources and energy. Research and exploration activities for marine development, maintaining limitless competition for national interests, and efficient development are currently being conducted globally. Therefore, capital and equipment to develop oceans have been heavily invested. Internationally, unmanned surface vehicles (USVs) have continuously been utilized in the exploration of marine data and marine resources, as well as for military purposes, replacing human resources. However, most of the advances of independent systems have been in scientific technology and commercial use, with limitations, such as acquiring broadband data in a wide range and having to undergo diverse missions at sea. Basically, formation/group driving algorithms are largely classified into behavior-based [1, 2], virtual structure [3], and leader-follower [4] methods.  
Recently, there have been many studies using virtual structure and leader-following in the operation of swarm unmanned surface vehicles. Leader-following is a method that utilizes the geometric relationship in which when a leader moving object moves along a path, the following moving objects move while maintaining a constant relative distance and direction angle with the leading moving object. In [PERSON] et al., the dependency of the leading moving object (leader) is improved. A formation control algorithm was proposed [5]. While numerous studies have focused on swarm control using drones in the air, there has been a lack of research on swarm control in marine environments. Unlike aerial swarm control, the high fluid density and constantly changing conditions of the sea pose significant challenges, making robot systems highly susceptible to disturbances. This ultimately impacts the ability to sustain swarm formations and successfully accomplish missions. Consequently, controlling multiple moving objects while preserving formation integrity at sea using existing aerial drone swarm control technology proves challenging [6, 7]. To address this challenge, we employed a long short-term memory (LSTM) model trained on time-series marine environmental data to forecast maritime disturbances. Subsequently, the model was integrated into a mission-oriented position control algorithm for each USV within the swarm, resulting in the study of a marine unmanned floating vessel swarm control system suitable for diverse marine environments. To overcome these limitations, this study was conducted on a system for intensive mission performance in a wide area through swarm control of multiple small USVs.
## 2 The Working Principle and Parameters of SMWIR-IFTS  
The SMWIR-IFTS is a temporally-spatially combined modulated IFTS. Its internal core structure is shown in Figure 1a, and its external 3D structure is shown in Figure 1b.  
The working principle of the SMWIR-IFTS is as follows: The radiation emitted by the target gas passes through the scanning reflector and the front imaging system before entering the interference core module. This module consists of a multi-stage micro-mirror, a plane mirror, a beam-splitter, and a compensating plate. The incident light is split into transmitted and reflected beams by the beam-splitter, and the multi-stage micro-mirror and plane mirror modulate the optical path difference (OPD) of the two beams. After passing through the reflector, the rear imaging system, and the optical filter, the light reaches the detector's focal plane, where an interference image is formed. After the scanning mirror completes one full scan cycle, the accumulation of different OPDs for the target gas is achieved, resulting in an interferogram data cube containing both two-dimensional spatial information and one-dimensional interference information of the target gas.  
The parameters of SMWIR-IFTS are shown in Table 1.
## 2 Duqu
### Follow-Up Activities  
Multiple security vendors pursued technical analysis of Duqu after our initial work. The most detailed results are from Symantec [11] and Kaspersky [12]. Their discoveries and conclusions are in line with our results and observations and deepen the knowledge about the Duqu threat. Similarities to Stuxnet are common features of these analyses.  
Multiple other Duqu infections were identified around the world, about 20 in total, most of them located in Europe and the Middle East. We had no access to new samples, and thus this information is based on the publicly available reports of the anti-virus industry. Based on these reports, we can say that Duqu and Stuxnet are like malware Lego-kits. Both of them are based on small components assembled together, they exist in several different versions with slight modifications, and they are created to perform their activity in a fast and efficient way. They are designed to avoid identification using individual modifications and very careful error processing. This type of thinking about the threat was reinforced by Kaspersky in their report [12], where they reveal details about the discovery of previously unknown pieces of malware components related to both Duqu and Stuxnet.  
The zero-day exploit within the Duqu dropper was confirmed by Symantec and Microsoft in the last days of October 2011, and fixed by a Microsoft patch [8] in December 2011. Anti-virus vendors now include detections on parts of the Duqu threat, and even generic detections on the exploit used by our known Duqu dropper, currently identified as CVE-2011-3402.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 4 Quantitative Model  
QIM steganography changes the frequency of occurrence of index values and the correlation of index values. Index distribution characteristics is good for steganalysis [3].
## 3 Results and Discussion
### Effect of Light Intensity on Carotenoids and Astaxanthin Production by H. pluvialis
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Chl a** & **Chl b** & **TChl** & **TC** & **Car/Chl** & **Vol TC** \\  & **mg L\({}^{-1}\)** & **mg L\({}^{-1}\)** & **mg L\({}^{-1}\)** & **mg L\({}^{-1}\)** & / & **mg L\({}^{-1}\)day\({}^{-1}\)** \\ \hline \multicolumn{7}{c}{Exp1: 35 umol m\({}^{-2}\) s\({}^{-1}\)} \\ \hline BWW & 0.05 & 0.11 & 0.16 & 0.59 & 3.69 & 0.10 \\ BWW-BG11 1:1 & 0.08 & 0.11 & 0.19 & 0.76 & 4.00 & 0.13 \\ BWW-BG11 1:3 & 0.12 & 0.21 & 0.33 & 0.57 & 1.73 & 0.09 \\ BG11 & 0.54 & 0.56 & 1.1 & 0.67 & 0.60 & 0.11 \\ \hline \multicolumn{7}{c}{Exp2: 140 umol m\({}^{-2}\) s\({}^{-1}\)} \\ \hline BWW & 0.04 & 0.03 & 0.07 & 0.79 & 11.28 & 0.13 \\ BWW-BG11 1:1 & 0.27 & 0.26 & 0.53 & 0.54 & 1.02 & 0.09 \\ BWW-BG11 1:3 & 0.19 & 0.24 & 0.43 & 0.43 & 1.00 & 0.07 \\ BG11 & 0.01 & 0.01 & 0.02 & 0.49 & 24.50 & 0.04 \\ \hline \multicolumn{7}{c}{Exp3: 35 umol m\({}^{-2}\) s\({}^{-1}\) then 140 umol m\({}^{-2}\) s\({}^{-1}\)} \\ \hline BWW & 0.3 & 0.19 & 0.49 & 0.64 & 1.28 & 0.10 \\ BWW-BG11 1:1 & 1.72 & 1.42 & 3.14 & 0.69 & 0.22 & 0.11 \\ BWW-BG11 1:3 & 1.59 & 1.8 & 3.39 & 0.42 & 0.17 & 0.07 \\ BG11 & 0.7 & 0.66 & 1.36 & 0.73 & 0.53 & 0.12 \\ \hline \end{tabular} Note: TChl = Total Chlorophyll (Chlorophyll a + Chlorophyll b), TC = Total Carotenoids, Car/Chl = ratio between carotenoids and chlorophyll, Vol TC = volumetric productivity of total carotenoids.  
\end{table} 
Table 4: Chlorophyll and carotenoid accumulation under mixotrophy. Figure 4: Micrographs of \(H\). _pluvialis_ in the 4 PBRs systems at day 0 and day 10 with a light intensity of 35 umol m\({}^{-2}\) s\({}^{-1}\) during the first 5 days and 140 umol m\({}^{-2}\) s\({}^{-1}\) during the remaining 5 days. Please note that the scale bar is not reported and is not—homogenous to allow the focus on the colour of cells.
## 3 Analysis of Test Results
### Basic Physical Parameters and Wave Velocity Determination of Coal Stone Samples
#### 3.1.1 Basic Physical Parameters
Among them, the highest slope of the increase or decrease in wave velocity was observed when the temperature was reduced from \(0\,^{\circ}\)C to \(-10\,^{\circ}\)C and then to \(-20\,^{\circ}\)C.  
Comparing the samples in the dry and saturated states, the wave speeds of the coal and rock samples in the saturated state were all greater than those of the coal and rock samples in the dry state. The degree of rapidity of growth or decrease in the saturated state of the coal and rock was higher than that in the dry state. The wave speed of coal increases more than that of rock from \(20\,^{\circ}\)C to \(0\,^{\circ}\)C and from \(-10\,^{\circ}\)C to \(-20\,^{\circ}\)C. At \(0\,^{\circ}\)C to \(-10\,^{\circ}\)C, rocks decrease more than coal. The increase in wave speed potentially occurred because the contraction of mineral particles or the phase change of water ice changed the longitudinal wave speed of coal and rock samples. As the pore ice filled the voids inside the coal and rock, the medium was solid-liquid-solid before, and it became solid-solid-solid. The wave impedance of ice was closer to that of the solid medium of coal and rock compared to that of water in the crevices; this caused the propagation of ultrasonic waves inside the coal and rock, and the ice in the pores reduced the wave diffraction and reflection between the original coal and original rock interfaces and the surface of the pores, thus causing a great increase in the longitudinal wave speed. The ice in the pore space reduced the diffraction and reflection of longitudinal waves at the interface between the coal or rock and the pore surface, which led to a significant increase in the longitudinal wave speed. This observation is in high agreement with [PERSON]'s findings [38]. Figure 7: Wave velocity profiles of dry and saturated coal and rock samples at different temperatures.
## 1 Introduction  
Multiferroic materials possess spontaneous electric and magnetic order which offers integration possibilities for multiple functionalities in a single material [1; 2]. Particularly, multiferroics open a new venue for controlling the magnetism by applied electrical field through magneto-electric coupling at room temperature, and vice versa [3; 4]. However, the G-type anti-ferromagnetic form and the incommensurate spin cycloid structure makes the electric-magnetic coupling rarely weak in bismuth ferrite [5]. BiMnO\({}_{3}\) shows ferromagnetic order and ferroelectric order below 100 K [6]. Therefore, the mixing compound Bi\({}_{2}\)FeMnO\({}_{6}\) drove much concern in recent years [7; 8; 9]. The reduced Neel temperature, the complicated exchange coupling between Fe and Mn ions and the G-type antiferromagnetic structure, which differs from BiFeO\({}_{3}\) and BiMnO\({}_{3}\), have shown the enormous novel physical properties of this compound [10; 11; 12; 13].  
The electronic properties in condensed matters directly show their physical properties. The interplay among the electron, lattice and spin degrees of freedom in multiferroic and other correlated electron materials can be studied in the time domain by ultrafast spectroscopy [14; 15]. The ultrafast d-d transfer blue shift is found in YMnO\({}_{3}\) which shows the connection between the d-electron charge transfer band gap and the macroscopic magnetism in multiferroics [16]. The low-temperature phase transition of BiFeO\({}_{3}\) is revealed by transient reflectivity, which shows the underlying magnetic-lattice coupling in bismuth ferrite [17]. The role of the double exchange mechanism in their magnetism's origin and the magneto-electric coupling of LuFe\({}_{2}\)O\({}_{4}\) is revealed by transient reflectivity in the vicinity of the Neel temperature [18]. Therefore, the d-d charge transfer rate is quite sensitive to the magnetism, which could be used to study the underlying d-electron structure and magnetism in Bi\({}_{2}\)FeMnO\({}_{6}\). For the single B site multiferroics, the pump-probe technique is widely used; however, the hybrid 3d transition element in B sites have been seldom investigated, which could show spectrum selectivity. In this letter, we report the temperature dependent, ultrafast charge transfer in Bi\({}_{2}\)FeMnO\({}_{6}\)/Si film, a compound of d-electron materials. The 1.55 eV transition is attributed to the Mn\({}^{3+}\) d-d charge transition, and the 3.1 eV transition is attributed to dominate by way of the d-d transition between the antiferromagnetic Fe\({}^{3+}\) ions. The temperature dependent transition probability of d-d charge transfer in Fe\({}^{3+}\) and Mn\({}^{3+}\) could be attributed to the temperature dependent correlations between Mn spins and between Fe spins, respectively. These behaviors indicate the transition probability in Bi\({}_{2}\)FeMnO\({}_{6}\) differs between the two transitions by high-spin and low-spin transitions [19]. The spin-lattice coupling process is also shown to be element dependent, which is related to the Mn-O-Fe and Mn-O-Mn ordering temperatures, respectively.
# Development and Validation of Universal 3D Blast Fragmentation Model
## 2 Fragmentation Model Fundamentals
### Principles of Rock Fragmentation by Blasting  
Explosive charge detonation generates a pressure wave that propagates in a cylindrical shape around the blast hole. The propagation of a high-energy pressure wave results in a tensile load between rock particles and the formation of tensile fractures. Any existing discontinuities in the rock medium will prevent wave propagation between rock particles since there is no physical connection between them. Therefore, preexisting rock mass joints will limit the length of blast-induced fractures if their infill is weaker than the primary rock medium or if there is no infill material.  
The principle, as mentioned earlier, is essential for rock mass blasting and proper blast design since primary block size and distribution in space will influence blasting results. Preexisting joints separate primary blocks in the rock mass, and to reduce their size by blasting, the explosive charge must pass through a single primary block. Otherwise, oversized fragments will be formed, which is an undesired outcome in every blasting situation, especially in underground environments with constrained space conditions. Besides constrained space, mining methods such as sublevel caring may experience significant performance decline and disruption of the gravity flow.  
Figure 7 illustrates radial tension fracture formation in a monolith rock medium and fractured rock mass. This is one of the main principles used in the 3D fragmentation model presented in this paper.  
Figure 6: Formation of blast-induced fractures perpendicular to the free surface [22].  
Figure 7: Tension crack formation in monolith and jointed rock.
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 2 Preliminaries
### Hamming Correlation  
Assume that \(\mathbb{F}\) is a frequency set and \(\mathbf{S}\) is an FHS set containing \(M\) FHSs with length \(N\) over \(\mathbb{F}\). For any two FHSs \(\mathbf{x}=(x_{0},x_{1},...,x_{N-1})\) and \(\mathbf{y}=(y_{0},y_{1},...,y_{N-1})\) in \(\mathbf{S}\), at the time delay \(\tau\), \(1\leq\tau<N\) if \(\mathbf{x}=\mathbf{y}\) and \(0\leq\tau<N\) if \(\mathbf{x}\
eq\mathbf{y}\), the periodic Hamming correlation \(H(\mathbf{x},\mathbf{y},\tau)\) of \(\mathbf{x}\) and \(\mathbf{y}\) is defined as follows.  
\[H(\mathbf{x},\mathbf{y},\tau)=\sum_{t=0}^{N-1}h[x_{t},y_{t+\tau}\bmod{}_{N}] \tag{1}\]  
where \(h[x_{t},y_{t+\tau}\bmod{}_{N}]=1\) if \(x_{t}=y_{t+\tau}\bmod{}_{N}\) and \(0\) otherwise. If \(\mathbf{x}=\mathbf{y}\), then it is called the periodic Hamming autocorrelation. Otherwise, it is called the periodic Hamming cross-correlation.  
Furthermore, the maximum periodic Hamming autocorrelation (MPHAC) \(H_{a}(\mathbf{S})\), the maximum periodic Hamming cross-correlation (MPHCC) \(H_{c}(\mathbf{S})\) and the MPHC \(H(\mathbf{S})\) are, respectively, defined by  
\[\begin{split}& H_{a}(\mathbf{S})=\max\{H(\mathbf{x},\mathbf{x}, \tau)|1\leq\tau\leq N-1,\forall\mathbf{x}\in\mathbf{S}\},\\ & H_{c}(\mathbf{S})=\max\{H(\mathbf{x},\mathbf{y},\tau)|0\leq \tau\leq N-1,\forall\mathbf{x},\mathbf{y}\in\mathbf{S},\mathbf{x}\
eq\mathbf{y }\},\\ & H(\mathbf{S})=\max\{H_{a}(\mathbf{S}),H_{c}(\mathbf{S})\}.\end{split} \tag{2}\]
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results
### Radiation Performance Analysis
#### 2.2.3 Realized Gain  
It was observed that in every stage of the antenna design the 3D radiation pattern changed depending on the frequency of interest and the results are displayed in Figures 12-15. Peak radiation concentration is shown in red color.  
Moreover, realized gain values changed with the structure modifications and reached their peak values with \(-31\) dB at 402.5 MHz and \(-22\) dB at 2.45 GHz. The antenna showed directional behavior in both frequencies. In the MedRadio band, the radiation pattern was directed to the x \(-\) (-y), as shown in Figure 15a plane, and in the ISM band, it was directed to the z axis. Table 2 shows the results of maximum Realized Antenna Gain extracted from simulations.