esa-sceva/satcom-chunk-collection huggingface dataset:
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 2 Related Works
### Asynchronous BFT Protocols  
Research on asynchronous BFT protocols [23; 24; 25; 26] also began early, but due to practical constraints, many of these investigations have remained theoretical. It was only relatively recently that HoneyBadgerBFT [9] was introduced. HoneyBadgerBFT employs reliable broadcast (RBC) and asynchronous binary agreement (ABA) to achieve asynchronous atomic broadcast (ABC). This approach has been continued in subsequent research, including BEAT [27], DispersedLedger [28], EPIC [29], etc. BEAT implements ABC with components of superior performance, offering multiple solutions tailored to different application domains and focal points. DispersedLedger introduces AVID-M, enhancing the parallelism of the ABC structure. EPIC focuses on addressing the liveness issue and achieves adaptive security through the practical application of Cobalt ABA. A different asynchronous consensus approach, exemplified by [PERSON] [30], VABA [31], and [PERSON] [32], employs MVBA to achieve asynchronous atomic broadcast. That is, [PERSON] reduces the number of ABAs required for MVBA through a committee model. The basic abstractions such as RBC [33; 34], ABA [35; 36], and MVBA [30; 31] have also seen continuous improvement. These asynchronous BFT protocols exhibit robustness, ensuring effectiveness even in asynchronous network environments. However, due to the constraints of the famous FLP impossibility states [37], asynchronous BFT protocols often require the introduction of randomness, resulting in higher latency.
# Lightweight Object Detector Based on Images Captured Using Unmanned Aerial Vehicle
## 3 Results
### Indicators for Model Evaluation  
Commonly used optimizers for model training include SGD [30], Adam [31], RMSProp [32], etc. The performance of an optimizer affects both the convergence speed and stability of the training process. In the experiments, the SGD optimizer was utilized to expedite the model's convergence. To ensure fairness, each experiment was conducted for 450 epochs. The experimental hardware and software environments are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Detailed Information** \\ \hline Operating system & Ubuntu 16.04 \\ Display card (computer) & NVIDIA RTX 2080 ti \\ CUDA & 10.2 \\ Deep learning frameworks & Pytorch 1.12.0 \\ Language & Python 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware and software environments used for model training and evaluation.  
Figure 6: Structure of C2f-Faster-SWCA (Fasternet-SWCA replaces Bottlenet).  
When evaluating the model's detection effectiveness for small objects, it is important to note that small objects tend to have a lower pixel percentage and present more complex background interference. In order to comprehensively and accurately measure the detection performance of the model, the mean average precision (mAP) is often adopted as the core evaluation metric. Specifically, mAP@50, which sets the intersection-over-union (IoU) threshold to 0.5, focuses on evaluating the model's detection ability under a more relaxed matching criterion. However, this may not sufficiently reflect the model's performance in dealing with small objects and complex scenes. Therefore, to more rigorously evaluate the model's detection accuracy for small objects, we used mAP@50:95, which calculates the average of AP over ten IoU thresholds ranging from 0.5 to 0.95 (with a step size of 0.05). This metric better reflects the model's ability to handle small objects and finely tuned detection tasks.
## 3 Geophysical Data and Methodology
### P-Wave Tomography  
We used the academic 3D tomographic code Pstomo_eq [68,69] to run the inversion of the first arrival times. The forward modeling part of the algorithm, responsible for the travel time computations and ray tracing in the isotropic model, consists of a first-order finite difference approximation of the [PERSON]'s equations [70,71]. Once the travel-times to all shots or receivers are calculated, the ray paths are determined by tracing backward from the shot or receiver locations that are perpendicular to the isochrons map [72]. The software has two different schemes implemented to carry out this forward modeling based on Hole and Zelt [73] and Tryggvason and Bergman [71]. The inversion is performed with the conjugate gradient solver LSQR (least square) that iteratively solves the velocity model updates.  
For the P-wave tomography study, we semi-automatically picked the seismic first arrivals using the commercial software Globe Claritas (Figure 6). The high quality of the recorded data allowed us to pick up 90% of the travel times, which include all the offset ranges, reaching to maximum offsets of 480 m. This high percentage of successful picked first arrival, even in areas with some anthropogenic activity-related noise, ensures a good lateral and depth resolution for the top 100 m.  
The setup of initial 2D velocity models used in the inversion were based on surface geology and on the well-logging data of the FAM-1 well, located next to the La Torrecilla area (Figure 1) [51]. These starting velocity models assure a good convergence of the tomographic inversion providing an average reduction of the RMS misfit of 80%, providing final RMS travel time residuals ranging from 2-16 ms. The high-resolution seismic data acquired in the area led us to velocity models with final inversion cell sizes of around 2 \(\times\) 2 m.
# Switching Control of Closed-Loop Supply Chain Systems with Markov Jump Parameters
###### Abstract  
The switching system model of a closed-loop supply chain with Markov jump parameters is established. The system is modeled as a switching system with Markov jump parameters, taking into account the uncertainties of the process and the inventory decay factors. The Markov switching idea is applied to the controller design and performance analysis of the system to effectively suppress the bullwhip effect while ensuring the stability of the closed-loop supply chain system. Simulation examples are presented to illustrate the validity of the results obtained.  
switching system; closed-loop supply chain system; Markov jump parameter; robust \(H_{\text{o}}\) control; bullwhip effect
###### Abstract  
The non-contact detection of buried ferromagnetic pipeline is a long-standing problem in the field of inspection of outside pipelines, and the extraction of magnetic anomaly signal is a prerequisite for accurate detection. Pipeline defects can cause the fluctuation of magnetic signals, which are easily submerged in wide-band background noise without external excitation sources. Previously, Variational Mode Decomposition (VMD) was used to separate modal components; however, VMD is based on narrow-band signal processing algorithm and the calculation is complex. In this article, a method of pipeline defect signal based on Variational Specific Mode Extraction (VSME) is employed to extract the signal of a specific central frequency by signal modal decomposition, i.e., the specific mode is weak magnetic anomaly signal of pipeline defects. VSME is based on the fact that a wide-band signal can be converted into a narrow-band signal by demodulation method. Furthermore, the problem of wide-band signal decomposition is expressed as an optimal demodulation problem, which can be solved by alternating direction method of multipliers. The proposed algorithm is verified by artificially synthesized signals, and its performance is better than that of VMD. The results showed that the VSME method can extract the magnetic anomaly signal of pipeline damage using experimental data, while obtaining a better accuracy.  
defect signal recognition; buried pipeline; magnetic anomaly detection; variational specific mode extraction; non-contact +
Footnote †: journal:  
Article
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
###### Abstract  
Precise positioning of non-cooperative targets is important for maintaining spacecraft operational environments in orbit. In order to address the challenges of non-cooperative target localization during non-coplanar rendezvous, this study develops a distributed cooperative localization scheme. First, a three-line-of-sight positioning method for long-range targets in non-coplanar orbits is proposed. Second, a distributed extended Kalman filter based on a consensus algorithm is designed, which reduces observation dimensions and increases system robustness. Subsequently, the rendezvous configuration optimization problem for long-range non-coplanar targets is transformed into a numerical optimization problem. Finally, an improved NSGA-III algorithm is proposed by introducing normal distribution crossover (NDX) and a cosine-like mutation distribution index to optimize the rendezvous configuration. A simulation shows that the methods proposed are effective, and the improved NSGA-III is superior to traditional algorithms in terms of search range and convergence speed. After configuration optimization, the performance of the system has been greatly improved, with better positioning accuracy and stronger robustness.  
non-cooperative target; line of sight; NSGA-III Article
## 3 Control Strategy Approach
### DC-Link Voltage Controller  
This DC-Link Voltage controller provides the direct axis reference current '\(I_{1d}^{*}\)' by keeping the constant DC Voltage '\(V_{1 dc}\)' with the reference DC Voltage '\(V_{1 dc}\)'. The DC-Link Voltage controller is responsible for maintaining the constant DC Voltage '\(V_{1 dc}\)' at the reference DC Voltage '\(V_{1 dc}\)'. The proportional-integral (PI) controller is used for this purpose. The output of the PI controller accounts for the losses in the STATCOM, and it is considered to be the real power component of the current '\(I_{1d}^{*}\)'. The real power component of the current '\(I_{1d}^{*}\)' is fed to to the decoupled controller. The DC-Link voltage controller maintains the DC-link voltage at the reference value using a small amount of active power  
Figure 2: Control strategy for the proposed STATCOM.  
transfer between the AC system and the DC-DC-Link of STATCOM. The low value active power flows to DC side to hold DC reference voltage, as expressed by Equation (8):  
\[I_{1d}^{*}=K_{1P}(V_{1 dc}^{*}-V_{1 dc})+K_{1I}\int(V_{1 dc}^{*}-V_{1 dc})\ dt \tag{8}\]
## 5 Simulation Results
### Single-Point Control  
In the first experiment, a single control point situated at the end effector of the continuum robot was used to actuate the robot towards a desired target pose. Initially, only the last 2 joints of the robot were enabled. Gradually, an extra joint was enabled, increasing the robot's degrees of freedom. The robot began from the home pose where the control-point's task-space position was (0, 0, 0.4500), and orientation was (0, 0, 3.1416). Orientations in this paper are specified in ZYX Euler angle representation for simplicity. The target position was (0.4218, 0.0240, 0.0605) and orientation was (0.6202, \(-\)0.6009, 2.7578). The results are shown in Figure 3 and tabulated in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link** & \(\mathbf{d}_{i}\) & \(\mathbf{\theta}_{i}\) & \(\mathbf{a}_{i}\) & \(\mathbf{\alpha}_{i}\) \\ \hline
1 & 0 & 0 & 0.05 & 1.5708 \\
2 & 0 & 0 & 0.05 & 1.5708 \\
3 & 0 & 0 & 0.05 & \(-\)1.5708 \\
4 & 0 & 0 & 0.05 & 1.5708 \\
5 & 0 & 0 & 0.05 & \(-\)1.5708 \\
6 & 0 & 0 & 0.05 & 1.5708 \\
7 & 0 & 0 & 0.05 & \(-\)1.5708 \\
8 & 0 & 0 & 0.05 & 1.5708 \\
9 & 0 & 0 & 0.05 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Denavit–Hartenberg parameters of redundant robot.  
Figure 2: The simulated 9-degrees-of-freedom continuum robot.  
Figure 3: The controller performed the task of reaching, bringing a single control point at the end effector to a desired target pose. As more joints were enabled, the performance was degraded. In particular, in the 2-degrees-of-freedom case, the robot reaches the target in 21 steps, while, in the 9-degrees-of-freedom case, the robot reaches the target in 150 steps.  
In the simple 2-degrees-of-freedom case, the control point converged to the target pose within 25 steps. When increasing the degrees of freedom, it is noted that the controller takes longer to converge. Although it is evidently possible to reach the target pose within 25 steps, the added redundancies introduced additional nonlinearities that require excessive exploratory behavior. In the case where all 9 joints of the robot were enabled, the control point reached the target pose in 150 steps, a significant decrease in performance. With the stopping condition (\(DQdist<\sigma\)) applied, it can be seen that the controller can terminate before fully reaching the target pose.
## 2 Materials and Methods
### Field Trials  
#### 2.4.1 Test Site and Materials  
A field test site was established at an open-pit lignite mine in North Rhine-Westphalia, Germany (Figure 3c), where field experimentations were conducted between July and August 2020. Initially, the test site was cleared of vegetation and topsoil. Then, 18 test plots were constructed using wooden frames (length 700 mm, width 400 mm, height 56 mm) and geotextile fabric, which was attached to the bottom surface of each frame to separate the test substrate from the underlying soil and to allow for water drainage (Figure 3a).  
Figure 2: Setup of laboratory experiments. Constructed wind tunnel includes different components to produce a laminar airflow: (**a**) fan, (**b**) diffuser, (**c**) honeycomb slits, (**d**) two screens, (**e**) contraction cone, and (**f**) test section. A dGAL4-30/30 axial fan (Korfmann Lufttechnik, Witten, Germany) with a diameter of 400 mm driven by a 1.5 kW electric motor was used to generate a constant wind speed of 13.57 m/s.  
Then, each test plot was filled with mine soil up to the top of each wooden frame. Each soil surface was then levelled, and finally, soil surfaces were treated with selected additives. The reagent solutions' preparation and application on the substrate surface were identical to the procedure described in Section 2.3.1. Untreated mine soil and soil only treated with water were set up as a control group to establish the dust control effectiveness of selected biomaterials.  
Three measurement series were carried out on each sampling day to determine the average background particulate matter concentration at the test site (cf. Table 11, Appendix A). Meteorological data were obtained from a nearby weather station for the duration of the study (12:00, 17 July 2020-12:00, 14 August 2020). Daily mean data included temperature (\({}^{\circ}\)C), solar radiation (W/m\({}^{2}\)), humidity (%) and precipitation (L/m\({}^{2}\)). A summary of the recorded meteorological data is given in Table 12, Appendix A. The lowest  
Figure 3: Images of field test site: (**a**) construction design of individual test plot showing wooden frame and geotextile base, (**b**) experimental setup of a single test plot with electric blower, isokinetic sampling probe and dust monitor DustTrak DRX 8533 (TSI, Shoreview, USA), and (**c**) entire test site with installed soil plots and nearby weather station.  
daily mean temperature for the study period was 16.04 \({}^{\circ}\)C, with the highest daily mean temperature of 28.04 \({}^{\circ}\)C. In total, a precipitation amount of 39.44 L/m\({}^{2}\) was measured. The highest rainfalls were recorded at 9, 23, 26 and 27 days after soil treatment with 4.8 L/m\({}^{2}\), 4.25 L/m\({}^{2}\), 17.48 L/m\({}^{2}\), and 8.84 L/m\({}^{2}\), respectively.
## 3 Results and Discussion
### Effect of BZ25 Addition on FAAs and Bitter Amino Acids  
The FAA contents of four samples were detected and shown in Table 2. FAA content affects the quality of food products [18]. Bitter amino acids are rated as the leading influencing factor of poor taste [52]. FAAs in food, such as lysine, threonine, and methionine, affect the protein quality and flavor of food [53]. Eight bitter amino acids were detected in NMBB and NMB. Except for His, the TAV of bitter amino acids (NMBB) was lower than that of the NMB. In both groups of samples, Lys had the highest TAV, which was 7.0 and 9.9, respectively. In natto, the acid produced by BZ25 reduced pH. It also decreased the capability of protease to hydrolyze protein into FAAs by inhibiting protease activity. The content of FAAs can reflect the degree of protein degradation in soybean samples [54]. Proteases were produced during the growth and metabolism of GUTU09. These enzymes promoted the hydrolysis of proteins into various peptides, such as short peptides and amino acids. These substances could be decomposed and oxidized by BZ25 and used as nitrogen sources for bacterial growth, which led to the reduction in FAA content. This effect could be another reason for the reduction in FAA content. The above data showed that two-strain mixed fermentation could decrease the content of bitter amino acids.  
Figure 3: Net chart of sensory properties in natto fermented by two-strain (NMBB) and single-strain fermentation with GUTU09 (NMB).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**RT (min)**} & \multicolumn{6}{c}{**Free Amino Acids Content (mg/g)**} & \multirow{2}{*}{**Threshold**} & \multicolumn{3}{c}{**Taste Activity Value (TAV)**} \\ \cline{2-2} \cline{5-10}  & & \multicolumn{2}{c}{**SS**} & \multicolumn{2}{c}{**SFR**} & \multicolumn{2}{c}{**NMB**} & \multicolumn{2}{c}{**NMBB**} & \multicolumn{2}{c}{**Value (mg/g)**} & \multicolumn{2}{c}{**SS**} & \multicolumn{2}{c}{**SFR**} & \multicolumn{2}{c}{**NMB**} & \multicolumn{2}{c}{**NMBB**} \\ \hline His & 7.32 & 0.08 \(\pm\) 0.02\({}^{\circ}\) & 0.4 \(\
## 3 Mathematical Model  
### Computational Domain, Grid and Boundary Condition  
The computational domain (Figure 4) was built to simulate the external environment, with a size of 1425 m (length) \(\times\) 945 m (width) \(\times\) 302 m (height). The dimensions were defined based on the recommendation relationship [37] between the height of the tallest building and the dimensions. The inlet boundary wall is set at 6H from the exterior building surface, with the outlet boundary 15H downstream of the building, to allow for full development of the wake-flow. The distances between the lateral boundaries and the building are 5H when the height of the computational domain is 5H.  
The areas adjacent to the exterior building surfaces and the gabions are key areas that are finely meshed using an unstructured mesh. The far-field area is sparsely meshed, whereas the closer one is to the gabions, the finer the mesh is divided. Moreover, the fineness of the mesh in the near-wall region matches the near-wall turbulent flow model.  
The boundary conditions of the computational domain are given in detail in Table 2. To simulate the variation in wind speed with height and underlying surface situation in  
Figure 3: (**a**) Building physical model; (**b**) exterior surface numbers of the analyzed building.  
Figure 2: Schematic of the gabion building envelope.  
the external environment, an exponential profile is chosen as the incoming wind velocity profile for the inlet of the domain and the equation is shown as follows:  
\[v=v_{ref}\bigg{(}\frac{z}{z_{ref}}\bigg{)}^{0.14} \tag{1}\]  
where, \(v\) and \(v_{ref}\) are the velocity for the inlet and reference point, respectively. \(z\) and \(z_{ref}\) are the vertical height for inlet and reference point, respectively. Moreover, the standard wall function is used for the near-wall areas. The temperature of the exterior building surface under simulation conditions can be calculated based on the basic principles of heat transfer.
# Impact of Nitric Oxide on the Surface Properties of Selected Polymers
## 3 Results and Discussion
### Zeta-Potential and Dynamic Contact-Angle Measurements
The untreated PU samples showed a zeta-potential curve typical for uncharged polymers. The curve decreased steeply with increasing pH values and no plateau formation at higher pH values, indicating that surface charge was only generated through ion adsorption from the electrolyte solution. The IEP of the untreated sample was located at a pH of 3.6. The treated sample showed a flatter curve with the IEP shifting to a pH of 2.7. While the contact-angle measurements showed broadly similar behavior for MABS and PU apart from the contact-angle hysteresis, the zeta-potential measurements showed a clear difference in behavior between the materials. MABS samples were unaffected by the  
Figure 7: Zeta-potential measurements of (**a**) PMP, (**b**) MABS, and (**c**) PU. Each material was measured both in its untreated state (gray) and after treatment with NO (1000 ppm) for 17 h (red). gas treatment. PU, on the other hand, showed substantial changes. The potential curve was flattened to reflect the increased hydrophilicity of the sample surface [50]. The IEP moved from a pH value of 3.6 to 2.7. This suggested the formation of acidic species on the PU surface (Figure 7c) [51]. A comparison between the zeta-potential and the FTIR-ATR results of both compounds (Figure 2, Figure 3, and Figure 7b,c) led to the conclusion that the most influential was the adsorbed HNO\({}_{3}\). The attribution of the surface effects to the adsorbed HNO\({}_{3}\) was supported by the simultaneous increase in both acidity and hydrophilicity exhibited by PU (Figure 7c). This result also supported the attribution of contact-angle decrease to the HNO\({}_{3}\) adsorption in the paragraphs following Figure 6. The difference between MABS and PU likely resulted from different HNO\({}_{3}\) surface concentrations after the NO treatment. The differences in signal height development between both compounds are unlikely to have played a significant role, as the time frame of the zeta-potential measurements was much shorter than the liquid immersion experiments. A comparison of the signal heights at 3480/3443 cm\({}^{-1}\) showed a significantly higher signal with PU than with MABS (Figure 3a,c, and Figure 4a,c). The HNO\({}_{3}\) surface concentration of MABS was too low to influence the potential curve. In contrast, the surface concentration of PU was high enough to impact the zeta-potential measurements.
## 3 Results
### Solvent Extraction  
Ammonium sulphate did not precipitate the creation of any peptides from the culture supernatant. As displayed in Table 2, ethyl acetate was the optimum solvent producing a dry weight of 0.28 mg from one ml culture broth and inhibition zones of 14.4 and 14.1 mm against the _C. albicans_ ATCC10231 and _A. niger_ clinical isolates, respectively. Based on their TLC characteristics, 145 fractions were gathered and aggregated into 22 pooled fractions (PFs). The bioactivity assay revealed the highest antifungal activity for PFs 17, 18, and 19. Other PFs displayed little or no activity (Table 3).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Solvent**} & \multirow{2}{*}{**Extraction Yield (mg/mL)**} & \multicolumn{2}{c}{**Mean Inhibition Zones (mm) \(\pm\) SD**} \\ \cline{3-4}  & & _C. albicans_ & _A. niger_ \\ \hline Ethyl acetate & 0.28 & 14.4 \(\pm\) 0.32 & 14.1 \(\pm\) 0.58 \\ Chloroform & 0.26 & 14.11 \(\pm\) 0.16 & 14.0 \(\pm\) 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extraction yield and mean inhibition zones of different solvents.  
The elutes 121-126 forming PF 18 were obtained using a chloroform/ethyl acetate solvent system with a ratio of 15:85 and exhibited the strongest antifungal activity, as shown in the bioautography assay (Figure S6). Figure S7 confirms the purity through the visualization of a single spot on TLC plate under a UV lamp (UVitec(r) at 254 nm and 365 nm. Figures S8 and S9 show the HPLC chromatogram and the positive ESI-MS spectrum analysis of the isolated compound, respectively. Figure S10 shows the linear relationship between the various concentrations and the inhibition zones. The following equation was a representative of this linear relationship: y = 0.1109x + 9.1094 (Y = inhibition zone diameter in mm, X = concentration of the best active fraction in \(\mu\)g/mL).