esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 RPM Path Planning Method Based on Probabilistic Roadmap and Heuristic Rule
### PH-RPM Design  
In the PH-RPM proposed in this paper, the optimal control model for PMCU pushing path planning is first discretely solved using the RPM method to transform it into a finite-dimensional NLP problem. The PRM algorithm pre-plans the passable space and solves the effective PMCU pushing suboptimal path by combining the path pruning heuristic method, which can effectively improve the quality of the initial value guess while ensuring the speed of the solution. The initial solution is used as the initial value guess of the NLP problem. In order to solve the NLP problem, SQP is adopted in this paper. Compared with other methods, the SQP has good convergence, high computational efficiency, and strong boundary search capability and has been widely valued and applied by scholars once it was proposed [52; 53; 54]. The basic principle is that at the current iteration point \(x^{k}\), taking the [PERSON] second-order expansion of the Lagrange function \(L(x,\lambda)\) of the NLP model at \(\left(x^{k},\lambda^{k}\right)\) for the variable \(x\) as the objective function. Using the [PERSON] first-order expansion with the constraint at \(x^{k}\) as the constraint, a quadratic programming subproblem is constructed to obtain the search direction \(d^{k}\), and the process is repeated iteratively to find the optimal solution for the problem.  
Overall, the summary composition of the proposed optimal control-based path planning algorithm, which we called the PH-RPM, is shown in Figure 6:  
Figure 5: Path pruning heuristic method flow.
# Faster MDNet for Visual Object Tracking
## 4 Experiments
### Results on OTB100
#### 4.4.2 Comparisons with MDNet-Based Trackers  
We also compared our tracker with MDNet [20] and two methods based on MDNet, VITAL [21], and RT-MDNet [22]. As shown in Table 2, our tracker achieves the best results in terms of both precision and AUC (Area-Under-Curve) score. In addition, our tracker achieves a tracking speed of 36 FPS, which is much faster than other methods.
# Principles of Different X-ray Phase-Contrast Imaging: A Review
## 2 Methods
### X-ray Holography
The results revealed a structure where the magnetic droplet existed, and the size and spatial position of the droplet was consistent with expectations. A minimum resolution of 32 nm was achieved [40]. Stable peomagnetic information in meteoritic metal is carried by the \"cloudy zone\". In 2020, [PERSON] et al. imaged the structure of the cloudy zone of the Tazewell IIICD meteorite using X-ray holography. A spatial resolution of 40 nm was achieved [41]. In the same year, [PERSON] et al. demonstrated that a resolution smaller than the source size of the reference beam could be achieved up to the diffraction limit of the hologram with their methods based on numerical postprocessing of the reconstruction [42]. Also in 2020, [PERSON] et al. imaged topological magnetic states in the lamellae of a centrosymmetric alloy (Mn\({}_{1-\mathrm{X}}\) Ni\({}_{\mathrm{X}}\))\({}_{0.65}\) Ga\({}_{0.35}\) (x = 0.5) using X-ray holography. Their results showed the presence of magnetic stripes evolving into single-core magnetic bubbles. A resolution of approximately 20 nm was achieved [43]. In 1996, [PERSON] and [PERSON] proposed the X-ray fluorescence holography (XFH) method. The experiment was performed with strontium titanate (SrTiO\({}_{3}\)) crystal platelets 30 mm in diameter and 0.5 mm thick [44]. There are two types of this method: \"normal holography\" or \"inverse holography\". In the normal mode, an atom in the sample absorbs the incident X-ray and emits a characteristic X-ray. The characteristic X-ray expands as a spherical  
Figure 4: Sketch of the prism interferometer. wave and is scattered by the surrounding atoms. Here, the scattered wave is an object wave, and the unscattered wave is the reference wave. In inverse mode, part of the incident X-ray beam is scattered by the atoms in the sample. The scattered and unscattered waves interfere with each other at the nanoscale. Thereafter, atoms absorb X-rays and emit characteristic X-rays. The intensity of the characteristic X-ray is proportional to the interference field. It operates like a time-inversed normal mode. The XFH method obtains atomic resolution with coherent high-power X-ray sources and has been widely used to determine the distribution, valence states, lattice distortion, and other atomic structures of the atoms in samples [45; 46; 47; 48]. The principles and applications of XFH have been thoroughly discussed by [PERSON] and [PERSON] [49]. This method requires a coherent and high-brightness source that constrains the application on sources other than synchrotron sources or X-ray free-electron lasers. While this method is applicable for material science usage, the high dosage limits its application to living biological samples.
## 3 Results and Discussion
### Determination of Total Phenol, Flavonoid, Hydroxycinamic Acid and Flavonoid Content
(2018) also reported TPC in two-component herbal teas. They studied the TPC of honeycomb (_Cyclopia species_), rooibos (_Aspalathus linearis_ (Burm.f.) [PERSON]) and special tea (_Monsonia burkeana_ L.) in combination with bush tea (_Athrixia phylicoides_ [PERSON]). The results showed that the special tea contained the highest TPC of all one-component herbal teas (1.10 mg GAE g\({}^{-1}\) of the dry sample). Still, this content was higher in the mixture with bush tea, where the ratio of the herbals was combined as 75% of special tea and 25% of bush tea (1.44 mg GAE g\({}^{-1}\) of the dry sample). The current study results showed that three-component mixtures contained lower TPC than two-component mixtures. The highest TPC was determined in the _WTSL221_ mixture (1.45 g L\({}^{-1}\)), which was expected considering that this mixture had a higher amount of \(S\) and _WT_. However, this value was almost twice lower than the value of the _WT_ extract. A similar effect of a combination of more than two herbal extracts was obtained by [PERSON] et al. (2018). They reported TPC in mulberry (_Morus alba_ L.) leaves and mixtures of mulberry leaves with other herbs, made as two-, four-, five-, and six-component herbal tea blends, where the two-component herbal mixture composed of mulberry leaves (70%) combined with cinnamon (_Cinnamomum verum_ J.Presl) bark (30%) had the highest content of polyphenols (16.06 mg GAE g\({}^{-1}\) of the herbal blend). On the contrary, [PERSON] et al. (2018) reported on two-, three-, four- and five-component decoctions containing yerba mate (_Ilex paraguariensis_ A.St.-Hi.l.) of different commercial brands combined with peppermint (_Mentha x piperta_ L.), pennyroyal (_Mentha pulgeium_ [PERSON]), peperina (_Minthostachys verticillata_ (Griseb.) [PERSON]), boldo (_Peumus bolhus_ [PERSON]), melissa (_Melissa officinalis_ [PERSON]) and lemon verbena (_Aloysia citrodora_ Palau). Their study showed that the five-component mixture had the highest TPC (45 mg GAE g\({}^{-1}\) of sypba mate), but it was also the only mixture containing melissa, contributing to higher TPC. There was no statistically significant difference between TFC in WT and S extract and their two-component mixtures in the current study. The lowest TFC was determined in the \(L\) extract and its two- and three-component mixtures. Our findings were not supported by [PERSON] et al. (2018), whose results of TPC in herbal tea blends correlated with a high content of flavonoids in the mixture of mulberry leaves and cinnamon bark (70:30) (2.23 mg QE g\({}^{-1}\) herbal blend). The results were also different in research by [PERSON] et al. (2018), where bush tea alone contained the lowest flavonoid content, but the flavonoid content increased in two-component mixtures.
## 2 Modeling and Controller Design
### Battery Side Converter Control  
The operation of the battery side converter, as shown in Figure 1, is the same as the PV side converter presented in Section 2.2 except for the reference current generation. The reference current is produced from the constant power reference. The current dynamics are governed by Equations (1) and (2) through the converter dq voltage, which further generates the modulating signal (Ma,b,c) for the VSC-B. The reactive current is set to zero for transferring only real power to the PCC. Figure 2b presents the battery side converter control. The reactive current reference for PV and battery side converter depicted in Figure 2 is zero, whereas real current references differ from each other.  
Figure 2: (**a**) PV side converter control and (**b**) Battery side converter control.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 4 Experiments  
In this section, we detailed the significant experimentation parameters and the two demonstrated scenarios. The primary tool employed for simulating the experiments is OMNeT++. Specifically, our study utilizes the Open Source Satellite Simulator (OS3) framework, a popular and mature tracking library implemented in OMNeT++ and INET to simulate and visually represent various forms of satellite-based communications by importing authentic satellite trajectories and even meteorological data. The original version of OS3 [49] encountered challenges regarding the outdated documentation and compatibility with up-to-date versions of OMNeT++ and INET. To address these issues, our experiment was constructed based on an updated version of the OS3 framework. For a comprehensive view of the framework please refer to repositories [6; 50] and paper [51] in which authors provide a detailed description of the design and implementation, while also describing simulation scenarios using LEO satellite constellations.  
In this section, we describe some significant parameters for the experiments as well as the discussed experimentation scenarios. The simulations were executed on a laptop equipped with an Intel Core i7 CPU, running Ubuntu 16.04 LTS. The software stack included OMNeT++ v4.5.2, INET v4.3, and the updated OS3 framework [6; 50; 51].
## 2 Materials and Methods
### IBMP Analysis  
The extraction method of IBMP in berry was as in [PERSON] et al. [24]. The frozen whole berries (15 g) were weighed and deseeded, ground into a fine powder in liquid nitrogen and placed in a 50 mL plastic centrifuge tube. An aliquot of 2 mM NaF solution (5.0 mL) was added to the powder in the tube. The solution was homogenized with a vortex mixer until smooth. After homogenization, the temperature of the solution was maintained below 2 \({}^{\circ}\)C. The suspension was centrifuged at 8000 rpm at 4 \({}^{\circ}\)C for 10 min. An aliquot (5.0 mL) of the supernatant was transferred to a 20 mL screwcap headspace vial containing 2.0 g of NaCl. The extraction method of IBMP from the grape stem, pulp, skin and seed is similar to that of whole berries, except that each extraction used only 10 g of powder from different parts and added 10 mL of 2 mM NaF solution.  
HS-SPME conditions were the same as in Section 2.4. GC-MS analysis for IBMP used the method of [PERSON] et al. [25] with slight modifications. GC-MS analysis was performed on an Agilent 7890B GC in tandem with an Agilent 7000D MS. The separation was performed on a DB-WAX capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent, J & W Scientific, Folsom, CA, USA). The GC temperature program was as follows: an initial temperature of 40 \({}^{\circ}\)C was maintained for 10 min, and increased by 10 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C/min, followed to 140 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, and finally increased by 25 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C and held for 3 min. The whole process was carried out by a helium carrier with a flow rate of 1 mL/min. The MSD transfer line heater was set to 250 \({}^{\circ}\)C. The temperatures of the ion source and quadrupole were 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. The mass detector was operated in selected ion monitoring (SIM) mode with EI mode at 70 eV. SIM was used at mass channels of \(m/z\) 94 and 124 for IBMP. Peak areas of the ion \(m/z\) 124 were used for quantification.  
IBMP standard curve establishment: IBMP standard was first diluted to 1 mg/L with ethanol, and then a juice sample (IBMP supernatant extracted from berry) was used to make a second dilution of 1 \(\upmu\)g/L stock solution. IBMP juice sample solutions (0, 2, 5, 10, 15, 30, 50 ng/L), 5 mL of each concentration was added to a 20 mL headspace bottle with 2.0 g NaCl and 10 \(\upmu\)L of internal standard (4-methyl-2-pentanol), and two parallel sets were made for each concentration. The peak area of the IBMP quantitative ion (\(m/z\) 124) was standardized using the internal standard characteristic ion peak area (\(m/z\) 45) and the content was calculated to produce the standard curve. The validation of the method was verified by configuring IBMP juice sample solutions at concentrations of 20 ng/L and 35 ng/L, with two replicates for each concentration. The specific parameters of the standard curves are shown in Table S1.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 2 Related Work
### Learning-Based VANET Routing Research for Packet Forwarding
The Q-learning algorithm is trained with historical traffic flow on road segments in an urban scenario. The greedy algorithm was chosen as the model's packet-forwarding strategy. When a vehicle node carrying a packet passes an RSU, it transfers the packet information to the RSU. Based on this information, the RSU determines the next hop intersection for relay using Q-learning, and the source node then uses a greedy method to transfer the packet to the target intersection. The IV2 XQ approach learns from historical traffic information in VANET environments. It can select another non-congested road segment by monitoring the real-time link status. Experimental results indicate that the proposed approach outperforms QGrid, GPSR, and RAVP since it can forward packets to road segments with the optimal vehicle density. Table 1 presents a comparative overview of learning-based research on VANET routing protocols. For most studies [12; 13; 14], vehicular data were artificially generated. Even for studies that utilize historical data, the real-time addition of new vehicles was not addressed. Studies employing historical data [17; 18] did not address whether the historical data aligns accurately with the actual vehicle travel paths due to GPS localization offset issues. Please note that although TDRL-RP uses the SDN trust-based DRL framework, VRDRT emphasizes traffic predictions and the road vehicle density with DRL, and IV2 XQ considers both historical and real-time vehicular information, these three approaches do not correct historical vehicular trajectory information or integrate DRL and RF for real-time vehicle routing decision-making. However, the proposed IDRF approach applies the SDN architecture to integrate DRL and RF to first correct historical vehicular trajectory information and then execute real-time vehicle routing decision-making to determine the shortest end-to-end delay packet relay path between the source and destination. This is done by considering both historical and real-time vehicular information, the end-to-end delay, the inter-vehicle link stability, the vehicle movement direction, and the newly generated packet forwarding path due to adding new vehicles. Approaches adopted in the proposed IDRF approach are discussed in the next section.
# Federated Learning for Edge Computing: A Survey
## 4 Frameworks
### TensorFlow Federated  
TensorFlow Federated (TFF) is an open-source machine learning framework for computations performed on decentralized data that provides the building blocks for FL based on TensorFlow [91]. This framework was developed for Python 3 for FL by Google. Google's main goal and motivation for creating this framework was the aforementioned mobile keyboard for Android phones, which predicted text for the user. It was the first example of using this framework in the real world. TFF enables researchers and developers to test new algorithms and simulate the built-in FL algorithms on their models and data. TFF can also be used to execute computations such as federated analytics that are not directly linked to model learning and training. The interfaces of TFF are split into two primary layers: Federated Learning API and Federated Core API. The FL API is an interface that makes it easier to carry out operations related to FL, model training, and model evaluation using TensorFlow. The interfaces offered by this layer consist of the following three parts: models, federated computation builders, and datasets. Federated Core (FC) can be understood as a subset of the lower-level interfaces that serve as the basis for the FL layer's implementation. In short, developers can implement its features in the FC. FC represents a development environment that allows compactly expressed program logic that combines TensorFlow code with distributed communication operators. These are the building blocks used in FL.  
It was also possible to find articles and experiments comparing different frameworks for FL. In this case, [92] compares TFF and PySyft, which were used for text recognition. The findings demonstrate that federated text recognition models can match or even outperform central deep learning-based algorithms in accuracy. TFF achieves the best feature accuracy on a distributed dataset with five clients at 49.20%.
## 4 Simulation
### Detection  
The BER performance of the EBPSK system will be diverse with different bit durations and sampling rates. To prove the effectiveness of the proposed method, various simulations were conducted. In Figure 8 we compare the BER performance of the SVM detector with TD for different bit durations N. The SVM-K2N5, SVM-K2N4, SVM-K2N20 and TD-K2N20 BER plots in Figure 8 perform significantly better than TD-K2N5. Compared to SVM-K2N5, the performance of SVM-K2N4 deteriorated greatly, and the former performed slightly  
In the next experiment we face a bandwidth efficient communication model which is proposed via a NBPF at the transmitting end of the system. Though we can achieve a bandwidth efficient transmission and suppress the interference to other channels, the transmitted signals would be distorted and the amplitude of SIF output signal would be not as high as usual. We use the SVM detector to solving such issue and give the BER performance comparison between SVM detector and TD. The bandwidth of the linear phase NBPF is designed to be \(\left[\dfrac{0.98N}{T},\dfrac{1.02N}{T}\right]\).  
PSD of the modulated signals is plotted in Figure 10(a). When this signal is filtered by the NBPF, its corresponding spectrum is illustrated in Figure 10(b).  
Figure 10: The power spectrum density of modulated signals and its filtered signals, respectively, in (**a**) and (**b**) for N = 5.  
Figure 9: The BER performance comparison of the SVM with threshold decision by different sampling rate. We use dashed-dotted lines for the SVM BER, solid lines for the threshold decision BER. We represent the BER for \(f_{s}=4f_{c}\) with \(\lozenge\), \(f_{s}=6f_{c}\) with *, and \(f_{s}=10f_{c}\) with \(\circ\), respectively.  
The performance comparison of SVM detector and TD are presented in Figure 11. The SVM detector can improve the quality of the receiver significantly. For the SVM, the SNR gain over the NB-SVM is around 2 dB with the BER = \(10^{-3}\) and for the TD, the gain over the NB-TD is around 4 dB with the BER = \(10^{-3}\).  
This can be explained by the fact that when the signal was filtered by the NBPF it was distorted significantly and it was difficult to detect the signal through TD, but for the SVM detector which can make full use of the characteristics of SIF output signals, such as energy and waveforms, thus the SVM-NB is about 2 dB from the performance achieved by the SVM. Moreover, the NB-SVM is even better than TD and the former outperforms the latter by about 3 dB and outperforms the NB-TD by 7 dB with the BER = \(10^{-3}\). This demonstrates that the performance can be improved by using the SVM detector in a band efficient transmission system. We have shown that there are more advantages in the SVM method than TD and the BER performance was significantly improved by the former compared with the latter. Moreover, the SVM method is not as sensitive to the sampling rate as the threshold method. Thus, SVM is an effective method for EBPSK detection.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 3 Simulation Environment and Settings
### Simulation Control and Setup  
The simulation setup is the same, with all four selected routing protocols being tested, for comparison between each protocol, given various constraints and parameter conditions. These conditions include the mobility of nodes, fixed or mobile, and the radio transmission ranges. Because each network must be heterogeneous, each node must be configured differently in each scenario. By configuring the mobility speeds of each node and their transmission ranges, we could achieve the heterogeneity of the nodes. To ensure the reliability of the simulation results, a warm-up period of 100 s was included at the start of each simulation run. This allowed the random number generator to stabilize, ensuring that the results were not affected by transient initial conditions. To evaluate the impact of the node density on the protocol performance, we conducted additional simulations with varying numbers of nodes (ranging from 12 to 50). The initial placement of the nodes was determined by the simulator using a uniform random distribution within the simulation area. This approach ensured that the results were not biased by manual intervention and provided insights into the scalability of the routing protocols. Table 2 lists the parameters used in the simulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 2 PDA (fixed node), 5 laptops, and 5 mobile phones \\ \hline Node type & PDA (fixed); mobile hosts: 10 to 15 Mbps; laptops: 1 to 2 Mbps \\ \hline Simulation time & 600 s \\ \hline Simulation trajectory & 600 \(\times\) 600 m \\ \hline Routing protocols & AODV, OLSR, BATMAN, DYMO \\ \hline MAC protocol & IEEE 802.11 g; 2.4 GHz \\ \hline Mobility type & Random waypoint mobility \\ \hline Mobility waiting time & 0.1 sec (default) \\ \hline Communication port & UDP \\ \hline Transmitter power & 2 to 5 mW (milliwatts) \\ \hline Bitrate & 54 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the simulation.