esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification
## 2 Enf Extraction and Database Formation Based on Location Specific Recordings
### Formation of Enf Database  
After estimating the ENF signals, a database containing the extracted values is developed following a sequential process:  
**Step-1:** After extracting the ENF sequences from the power and audio recordings, 50 Hz and 60 Hz ENF components are separated by measuring the mean values. Thus, for each power and audio recording 50 Hz and 60 Hz ENF sequences are recognized separately.  
Figure 4: Sample ENF sequences extracted from audio recordings from different 50 Hz grid locations.  
Figure 3: Sample ENF sequences extracted from power recordings from different 50 Hz grid locations.  
Figure 2: Sample ENF sequences extracted from audio recordings from different 60 Hz grid locations.  
**Step-2:** Each ENF pattern can be segmented into two sequences--low outliers frequency (LOF) and high outliers frequency (HOF), where LOF sequences show less fluctuations in frequency than HOF sequences. For each case, LOF sequence is determined by passing the original ENF signal through a smoothening filter and HOF sequence is determined by subtracting the LOF sequence from the ENF signal [5]. HOF sequence can be determined as:  
\[f_{hp}(n)=f(n)-\underbrace{\sum_{m=-\frac{M-1}{2}}^{\frac{M-1}{2}}w(m)f(n-m)}_{ f_{lp(n)}} \tag{1}\]  
Here \(f(n)\) is the ENF value at sample \(n\), \(f_{hp}(n)\) is the HOF value, \(f_{lp}(n)\) is the LOF value, \(w(\cdot)\) and \(M\) are the smoothening filter coefficient and order respectively. Thus, for 50 Hz and 60 Hz power and audio recordings separate databases containing LOF and HOF ENF sequences are developed.  
**3. Features Extraction, Classification Model and Performance Analysis**  
This section describes the extracted feature vectors, developed multi-class classification model and its performance analysis for the proposed ENF based location forensic application.
# Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy
## 4 Experimental Results and Discussion
### Embedded Capacity Analysis
Figure 5: Influence of parameters on embedding capacity: (**a**) the effect of the block segmentation method and different thresholds on capacity and (**b**) the effect of the sub-block segmentation method on capacity. Table 3 shows the experimental results of the proposed scheme with different \(T\). The table describes the pure embedding rate, decryption quality, and the recovered image quality with different \(T\). It can be found that the embedding rate increases first and then decreases with \(T\) from 4 to 64. The first three images achieve the maximum embedding rate when \(T\) = 16. However, the last image reaches the maximum at \(T\) = 32. From the average point of view, \(T\) = 16 is optimal. Note that in the experimental results of the Baboon image, two lines are blank. This is because the algorithm requires embedding a large amount of auxiliary data to achieve separable reception and lossless recovery, and Baboon images cannot provide such a large space under these two parameters. At the same time, the decryption quality decreases with the increase in \(T\), but the decryption quality is also acceptable at the maximum embedding rate. As for quality of the recovered image, the sign \"+\(\infty\)\" means that it can be entirely consistent with the original cover image. Figure 6: Schematic diagram of the proportion of different types of image blocks: (**a**) Lena, (**b**) Airplane, (**c**) Peppers, and (**d**) Baboon. Figure 7: Results of the proposed scheme. (**a**–**e**) describe the process for Airplane; (**f**–**j**) describe the process for Peppers. To verify the performance, the maximum embedding capacity is compared with the various algorithms in Table 4. In these algorithms, the maximum embedding rate that can be achieved in works [14; 15; 18; 20; 22; 23; 24] does not exceed 1 bpp, but it does in works [19; 25; 34], and the proposed scheme is between 1-2 bpp. [PERSON] et al. [19] used block encryption and achieved stable capacity by lossless compression in blocks of 2\(\times\)2 size. [PERSON] et al. [25] used three rounds of block segmentation to obtain scalable sub-blocks according to the average value of the blocks to provide embedding space. [PERSON] et al. [34] embedded data into the multiple MSBs of the embeddable pixels according to the strong correlation. After two-round segmentation, the proposed scheme combines two strategies to obtain the best embedding rate. The maximum embedding rates of the experimental images are 1.83 bpp, 2.22 bpp, 1.79 bpp, and 0.54 bpp. However, the embedding rate of complex image Baboon is low due to two factors. One is that the number of AHBs in the image with a complex texture is small. The second is that the amount of auxiliary data is relatively large. Overall, combining the FLME strategy and the AMLE strategy is more helpful in improving capacity, especially for the image with a smooth texture. Compared with the above schemes, the proposed algorithm has a larger capacity than previous algorithms.
## 5 Conclusions  
In contrast to previous studies [8], we did not observe a significant reduction in RVEDVi (161 mL/m\({}^{2}\) vs. 122 mL/m\({}^{2}\), \(p=0.3465\)) after Cone repair. Interestingly, 8 out of 18 patients exhibited higher postoperative RVEDVi. This could be attributed to the fact that, in [PERSON] patients with a severely displaced tricuspid valve, postoperative RVEDVi might be increased due to the incorporation of the atrialized RV to the functional RV.  
We observed a postoperative deterioration of RV GLS (\(-15.01\%\) vs. \(-14.53\%\), \(p\) = 0.0155), similar to conventional CMR parameters. Pulmonary artery flows remained unchanged, indirectly suggesting maintained RV systolic function. This suggests that GLS may also depend on the altered postoperative afterload conditions caused by the restored valve function. Preoperative GLS would therefore be a similarly confounded parameter of myocardial function, making it unreliable for assessing perioperative changes in RV function in [PERSON]'s anomaly. In contrast, GRS and GCS did not deteriorate--in fact, they even improved (15.00% vs. 17.83%, \(p\) = 0.0202 and \(-8.82\%\) vs. \(-13.02\%\), \(p\) = 0.0026)--suggesting that they are less affected by postoperative hemodynamic alterations and therefore might have potential for the pre- and postoperative assessment of myocardial function in [PERSON]'s anomaly.  
Conceptualization, C.M.; methodology, C.M.; software, C.F.; validation, C.F., P.E., N.N., J.C. and H.S.; formal analysis, C.F. and F.B.; investigation, C.F. and F.B.; data curation, C.F., F.B., I.F., N.S., S.M. and B.R.; writing--original draft preparation, C.F.; writing--review and editing, F.B., I.F., N.S., P.E., N.N., J.C., H.S. and C.M.; visualization, C.F. and F.B.; supervision, C.M.; project administration, C.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Technical University of Munich (Approval Number: 213/21 S-KK/8 April 2021).  
General informed consent was obtained from all subjects to use these data in a retrospective designed study.  
The authors will share the data on reasonable request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CMR & Cardiovascular magnetic resonance \\ EA & Ebstein's anomaly \\ EDVi & Indexed end-diastolic volume \\ EGC & Electrocardiography \\ ESVi & Indexed end-systolic volume \\ FAC & Fractional area change \\ FT & Feature tracking \\ GCS & Global circumferential strain \\ GLS & Global longitudinal strain \\ GRS & Global radial strain \\ LV & Left ventricle \\ PA & Pulmonary artery \\ RV & Right ventricle \\ SD & Standard deviation \\ SSFP & Steady-state-free precession \\ SVi & Indexed stroke volume \\ TR & Tricuspid regurgitation \\ TV & Tricuspid valve \\ \end{tabular}
## 3 Research Status of Numerical Simulation
### Reaction Kinetics
modified the kinetic parameters of the overall reaction kinetic model of isopropanol combustion by comparing the axial temperature curves obtained from previous experiments of isopropanol hydrothermal combustion, and thus well reproduced experimental results [34]. Based on methanol DCKM of gas-phase combustion [90], [PERSON] et al. optimized nine pressure-dependent reactions and four sensitive reactions, and proposed a DCKM for methanol SCHC by synthesizing the literature data and calculating the high-pressure limits [40]. This model improved the prediction accuracy of CO and CO2 concentration distribution and deepened the understanding of the ignition mechanism, ignition temperature and extinction temperature. In essence, both SCWO and SCHC have higher reaction pressure and lower reaction temperature than gas-phase combustion, and water as the reaction environment and reactant have a profound influence on the reaction path. Therefore, for the modification of gas phase elementary reaction kinetics, it is often necessary to add some intermediate products and the reaction paths of these products. In addition to the reaction kinetic parameters and reaction paths of DCKM, the thermodynamic parameters such as specific heat capacity and enthalpy also need to be modified. Because of the drastic changes of thermodynamic properties near the critical point, the theoretical calculation is difficult. [PERSON] et al. summed up the thermodynamic models used to calculate the density and heat capacity of SCWO mixtures [36], and found that most of the work available estimated mixture properties by using conventional cubic equations of state (EoS) or averaging tabulated reference properties of the pure components. [PERSON] et al. increased the reaction pressure in the calculation, so that the density calculated by the ideal gas EoS could be consistent with that of supercritical water under the actual pressure [86]. The Chemkin-real gas software package used in the research of [PERSON] et al. [85] and [PERSON] et al. [48] could replace the ideal gas EoS with the real gas equation of state. [PERSON] et al. modified the thermodynamic data of ethanol and isopropanol in the process of exploring the co-oxidation mechanisms of sludge and alcohol in supercritical water, so that the calculated results were closer to the experimental values [78].
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
## 1 Introduction
Among the various methods that have been proposed to improve the pattern-dependent performance of SOAs used for direct signal amplification (see [15] and the relevant references therein), optical filtering has attracted intense research interest [20; 21; 22; 23; 24; 25]. This passive method has been conceived of based on the observation that pulse amplification in an SOA is accompanied by spectral broadening to longer wavelengths (red shift) due to the manifestation of self-phase modulation (SPM) [26]. The SPM-induced spectral shift to longer wavelengths is higher than for the lower amplitude amplified pulses. This means that placing an optical filter after the SOA to pass a larger portion of the broadened spectrum for the less intense amplified pulses while blocking it for the more intense amplified pulses can compensate for the uneven red shift, which is converted then by the filter's slope [27] into more equalized pulse peak amplitudes (Figure 3). During last few years, we have applied the optical filtering technique for SOA pattern effect suppression by means of a platform comprised of different notch filters [15; 28; 29; 30; 31; 32; 33]. Among these filters, we particularly distinguish the optical delay interferometer (ODI) [30; 31; 32] and the microring resonator (MRR) [33], which share several common features, such as an all-optical and passive nature, simple structure, compatibility both with fiber medium and microelectronic fabrication processes, compact size, periodic and tunable transfer function and potential for integration and co-packaging with the SOA in a single module. These filters have conventionally been destined to serve more classical filter-oriented applications. However, in this paper, we do not treat them individually, but in conjunction with the SOA and the serious PE problem of the latter, which they intend to solve. Since the MRR has recently received intense interest and is being widely exploited in numerous and diverse light wave applications, but the ODI has been employed more for assisting the SOA operation, it would be useful to investigate and assess whether this trend of shifting to the MRR technology should be extended to the mitigation of the SOA PE. To this aim, we conduct a quantitative and qualitative comparison of the ODI and MRR capability to confront the SOA pattern effect, thereby completing our work on the specific research topic. The comparison reveals that although the ODI suitability for the intended goal has been well tested and confirmed, both theoretically and experimentally, the MRR can realize better improvements in a set of key performance criteria and features several better operating characteristics than the ODI. This indicates that the MRR also has the technological potential to contribute in equal terms with its ODI counterpart for resolving the complications provoked by the SOA pattern effect.
###### Abstract
Their simulation showed that with background potential derived from the concentration guiders, even coupled random walkers can generate realistic paths for axons. [PERSON] et al. [11] proposed an ad-hoc growth model built upon the diffusion principle to reproduce the shape of Micro-Tissue Engineered Neural Networks (Micro-TENNs). The proposed model imposed various rules for individual neuronal growth to a 3D diffusion equation. [PERSON] et al. [12] proposed a generative growth model to investigate growth rules for axonal branching patterns in cat area 17. The model achieves better statistical accuracy both locally and globally than the commonly used Galton-Watson model. [PERSON] et al. [13] proposed a closed-loop growth model in which neural activity affects neurite outgrowth in an interactive way. Experiments showed that the model can be potentially used to form large-scale neural networks by self-organization. However, it is very difficult to study how the scattered neurons in different parts of the growth region form the whole network through interactive behaviors with biological methods, because the growth environment of cells is complex, and so it is difficult to precisely control their growth conditions to verify the specific growth mechanism. Therefore, the study of the growth process of a neural network by numerical simulation is very effective. In a simulation environment, researchers can set strict growth conditions and analyze the growth result of the network in a variety of ways and then compare it with a real biological neural network to verify the validity of the designed model [9; 14; 15; 16]. However, the numerical simulation of the growth of a biological neural network has often been unable to simulate the control function of the network, and it can only compare the simulation model with the real biological network in terms of morphology and statistics. At the same time, in the control based on a neural network, it is precisely the complexity of the formation process of the biological neural network and the simplifications and assumptions made for the simulation model that make the network control model unrealistic and not universally applicable [17]. Therefore, it is necessary to combine the growth process with the control function in the control of bionic robots based on the CPG network to allow the two to develop in a coordinated way in the long term. Therefore, we propose some physical characteristics (including axon resistance, capacitance, length and diameter, etc.) that can determine the corresponding parameters of the control model to combine the growth process and the function of the CPG control network. By coordinating the two, we aim to realize the control function of the CPG network under the condition of biological reality as far as possible. In this way, the complexity of the developed control model will be closer to that of a biological neural network, and the control network will have more control functions.
## 1 Introduction
### Related Literature  
The modeling and application of hybrid air-terrestrial networks for access of ground users have drawn much research attention recently. A statistical propagation model has been introduced in [14] to predict the air-to-ground path loss between AP and ground users based on ray tracing simulation. The authors in [15] considered the modeling of small-scale fading for air-to-ground channels, where both line-of-sight (LoS) and non-LoS (NLoS) channel environments were considered. In [4], the authors discussed the basic networking architecture and major design challenges in UAV-aided hybrid air-terrestrial networks. In [16], the authors investigated the 3D beamforming design for a hybrid air-terrestrial network with a large-scale antenna array and analyzed the effect of AoD imperfection on the system performance.  
On the other hand, the WET in terrestrial networks has also been extensively investigated for both indoor and outdoor scenarios [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. In [17; 18], the authors investigated the outage performance of the indoor WET system and showed that increasing the variance of the log-normal channel would degrade the system performance. In outdoor scenarios, to combat the path loss due to the long charging distance, a number of works considered the utilization of multi-antenna technologies in the WET system. In particular, the benefits and challenges of combining WET with multi-antenna technologies were discussed in [19; 20]. The authors in [21] considered the EE optimization in a massive MIMO system with WET and proposed an iterative algorithm to compute the optimal transmit power and duration of the WET phase. The authors in [22] investigated the throughput optimization problem for a WET-enabled massive MIMO system. In [23], an asymptotically optimal downlink power allocation strategy was proposed to maximize the uplink sum rate in a WET-enabled massive MIMO system with linear beamforming. In [24], the 3D massive MIMO was utilized for WET, where the BS applied linear beamforming for energy broadcasting and the users applied power splitting for information detection and energy harvesting. In [25], the energy and information beamforming design for a full-duplex massive MIMO system was investigated. The authors in [26; 27] investigated the combined benefits of WET and cooperative relaying systems with large-scale antenna arrays. Due to practical constraints, [19; 20; 21; 22; 23; 24; 25; 26; 27] have assumed that the energy harvesters (e.g., sensors) are equipped with a single antenna. Recently, several works began to drop this assumption and consider multiple-antenna sensors [28; 29; 30]. It was shown that, by equipping more antennas at the sensor, the energy harvesting and achievable rate performances can be improved through coherent processing at the sensor side. However, the increase of antennas also incurs problems such as high circuit energy consumption and hardware complexity, which may be unaffordable for sensors. Therefore, in this paper, we assume a single-antenna sensor, and the general case with multiple-antenna sensors will be considered as future work.
## 3 System Model and Problem Formulation
### Scenario Description
More specifically, assuming that there are \(N\) cells under the coverage area of a single satellite, and at the same time slot we can select \(K\) cells to be simultaneously illuminated, so there are \(C_{N}^{K}\) illumination patterns for each time slot. Moreover, each beam can use a continuous  
Figure 1: Schematic diagram of the beam hopping scenario. segment of \(m\) sub-frequency-bands, which means there are \(\frac{m(m+1)}{2}\) types of band allocation schemes for each beam to select. Therefore, consider a time-segment containing \(T\) time slots, the searching space of the entire beam hopping allocation strategy is \(\left(C_{N}^{K}\times\left(\frac{m(m+1)}{2}\right)^{K}\right)^{T}\), which makes it difficult to find the global optimal solution. In order to illustrate the strong coupling relationship between three dimensional resource allocation more intuitively, we give an example as shown in Figure 2. Assuming that we can light three cells at the same time slot and each serving spot beam can use 9 consecutive equal-width sub-bands. Consider two different actual scenarios, in Figure 2a, the distribution of hotspot areas with large traffic demand is relatively scattered, the cells that are lit at the same time are geographically dispersed, and there is almost no co-frequency interference, so each beam can use the full frequency band. While in Figure 2b, hot spots are geographically concentrated and all three adjacent cells have great access requirements. Once they are lit at the same time with a full band, co-channel interference will become very serious and effective communication will not be possible. In this case, three consecutive sub-bands are allocated to adjacent beams, respectively, which can not only meet the needs of simultaneous communication of hot spots, but also avoid mutual interference. Moreover, depending on the selected frequency band, the transmitting power limit of the beam will be different due to the interference avoidance requirements of the GEO ground station.
## 4 Experimental Verification
### Identification of the Axle Weight  
Multiple tests were carried out using different configurations of two-axle and three-axle vehicles at different transverse positions. The detailed parameters of the vehicle are listed in Table 9.  
Based on the optimal combined strain influence line, the axle and gross weight of the different test vehicles were identified, and the results are listed in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Test** \\ **ID** \\ \end{tabular} } & \multirow{2}{*}{**Vehicle**} & \multicolumn{2}{c}{**Transverse Position**} & \multicolumn{2}{c}{**Identification Error (\%)**} \\  & & **(cm)** & & & & \\ \hline \multirow{4}{*}{1} & & & A1 (kg) & A2 (kg) & A3 (kg) & GVW (kg) \\  & Veh1 & \(-12.6\) & \(5.77\) & \(3.41\) & - & \(4.63\) \\  & Veh1 & \(-4.2\) & \(-2.79\) & \(-7.16\) & - & \(-4.90\) \\  & Veh2 & \(5.2\) & \(-5.24\) & \(4.73\) & - & \(-1.06\) \\  & Veh2 & \(-5.2\) & \(-1.12\) & \(-5.35\) & - & \(-2.89\) \\  & Veh3 & \(10.4\) & \(2.79\) & \(1.99\) & \(-3.63\) & \(-1.13\) \\  & Veh3 & \(11.2\) & \(3.67\) & \(-6.17\) & \(3.43\) & \(1.50\) \\  & Veh4 & \(-10.2\) & \(3.16\) & \(-5.10\) & \(-3.44\) & \(-2.12\) \\  & Veh4 & \(3.8\) & \(-6.92\) & \(3.73\) & \(3.51\) & \(0.79\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: The identified results of test vehicles.  
Figure 15: The averaged combined strain influence line.  
As listed in the table, the maximal identified error of the axle weight is about 5.8%, and that of the gross weight is about 4.9%. Due to the influence of the local unevenness of the model and measuring noise, the experimental test's identified error is slightly larger than that of the numerical simulation. However, the current identification accuracy can meet practical engineering applications.