esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Experimental Design and Optimization
#### 3.1.3 Optimization of Extraction  
There have been some previous studies regarding different optimization of extraction techniques applied to CS, aiming to optimize the extraction yield of flavonoids [44], polysaccharides using simple extraction [45], or pulsed electric field extraction [46]. Furthermore, we have chosen to develop a mathematical model using two dependent variables, specifically TFC and ABTS, aiming to maximize (a) the total yield of flavonoid species and (b) the total antioxidant activity of the phytocompounds that are successfully extracted.  
To achieve this optimization, we employed the optimizer function within the MODDE 13.0 software, specifying the objective to maximize both TFC and ABTS responses. The predetermined maximal values were set at 4.71 mg/g for TFC (with a probability of failure of 8.1%) and 18.16 mg/g for ABTS (with a probability of failure of 3.4%). Through this optimization process, we identified a set of independent factors that could yield the highest outcomes. Specifically, the optimal combination involved a 37.89% ethanolic concentration, a 48.58% ultrasound amplitude, and an extraction time of 38.72 min. As it has been mentioned, this optimal time period was used for mapping the RSP in Figure 3.  
Ethanol concentration, ultrasound amplitude, and extraction time are crucial factors in the extraction process. A specific ethanol concentration of 37.89% was deemed optimal, representing a balance between insufficient and excessive extraction. The chosen amplitude of 48.58% optimized mechanical forces for effective mass transfer of bioactive compounds, such as flavonoids. An extraction time of 38.72 min achieved a balance of extraction, allowing sufficient penetration of the solvent into plant material without risking unwanted extraction or degradation. This combination of parameters represented an optimal set of  
Figure 3: Three-dimensional response surface plot (RSP), showing the influence of model parameters on the two dependent variables (TFC and ABTS values) at the time period that was determined to be optimal (38.72 min).  
conditions, systematically determined to maximize extraction efficiency for both flavonoid content using AlCl\({}_{3}\) (abbreviated TFC) and Trolox equivalent antioxidant capacity using the ABTS assay.
# Resonant Airborne Acoustic Emission for Nondestructive Testing and Defect Imaging in Composites
## 4 RACE Scanning Mode  
As shown above, the RACE signal is a footprint of the airborne emission from the damaged area, and therefore, can be employed naturally for its imaging [19]. An example of the RACE application to damage imaging is shown in Figure 6 for a heat-induced, elliptical, local defect in the CFRP plate (300 \(\times\) 300 \(\times\) 4 mm\({}^{3}\)) Figure 6a. LDR frequency for (\(\approx\)10 \(\times\) 20 mm\({}^{2}\)) the visible part of heat-induced delamination was found to be 10,600 Hz and was used for the defect's resonant excitation. The RACE image (Figure 6b) clearly reproduces the defect size and shape and demonstrates a fairly high (\(\approx\)20 dB) signal-to-noise ratio (SNR).  
Figure 5: Airborne AE from impact damage in CFRP plate (**a**): standing wave pattern at 40 kHz (**b**); RACE at LDR frequency (**c**,**d**).  
Figure 6: Heat-induced damage in CFRP (**a**) and its RACE image at LDR frequency 10600 Hz (**b**).  
Figure 7 demonstrates the application of RACE to imaging of hidden defects (20 Joule impact in the non-crimp CFRP plate (150 \(\times\) 100 \(\times\) 4.5 mm3)). The impact produces mainly internal damage that is barely visible (BVID) on the reverse side of the specimen (Figure 7a). The interior damage induced by the impact has a complex structure of multiple delaminations of different sizes and values of LDR frequency [20]. The RACE image obtained at one of the fractional LDR frequencies (18 kHz) is shown in Figure 7b, and evidently depicts complicated defects such as BVID.  
The case studies presented above are based on preliminary measurements of LDR frequency, which are then used for a monochromatic, resonant activation of defects. To avoid additional vibrometry measurements of LDR frequency, a wideband (1-100 kHz, noisy signal) excitation can be combined with C-scanning. In this noisy mode, all fundamental and fractional LDR (if they are inside this frequency range) are excited. Since the phases of the excited resonant vibrations are different, the image has a noisy structure.  
Two examples illustrating multiple-defect imaging via noisy RACE mode are shown in Figure 8. The white noise input voltage for a vacuum-attached piezo-transducer is produced by an arbitrary signal generator (Stanford research systems Model DS 345) and covers the range 0-20 MHz. Overall acoustic bandwidth generated by the transducer is, however, limited by its frequency response and demonstrates quite an inhomogeneous spectral distribution, mainly within the 100 kHz bandwidth. This bandwidth, nevertheless, covers the fundamental and majority of the higher-order LDR for the specimens studied: a set of four circular FBH (2 cm diameter) and different depths in PMMA (Figure 8a), and four artificial square delaminations (\(\sim\)27 \(\times\) 27 mm2) at different depths in CFRP plate (Figure 8b). As a result, resonant AE enables us to visualize the defects in a single, noisy-mode experiment without preliminary knowledge of LDR frequencies.
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
## 3 Public-XOR-Key Scheme for QKD Network  
In this section, we extend the chain structure of the public-XOR-key scheme with two endpoints to the complex network with multiple endpoints. The scheme is secure as long as the XOR keys are generated securely. In this scheme, there are no re-generation keys and the relay nodes need not perform the encryption operation or store any key material, which reduces the complexity of the system. There is no need to communicate between adjacent relay node, which avoids communication failure caused by network congestion.
# Risk Identification Method and Application of Roof Water Inrush Under Multi-Working Face Mining
###### Abstract  
Adjacent, multi-working face mining can expand the range of disturbed overburden, increasing the risk of triggering roof water inrush, which threatens the safe operation of coal mines. In this paper, we propose a risk identification method for roof water inrush under multi-working face mining conditions based on the theory of Key Strata and Full Mining Disturbance. Firstly, the key strata of the overburden are determined based on lithological and structural data from exploration boreholes. A formula is then derived to calculate the critical dimension (L) of the working face that could induce a fracture in the key stratum. The relationship between L and the combined width of the preceding and adjacent working faces is analyzed to assess whether the key stratum is fractured and how it affects the preceding working face. Finally, the height of the water-conducting fracture zone is predicted. The impact of repeated disturbances from multi-working face mining is evaluated to determine whether the height of the water-conducting fracture zone in the preceding working face increases, thereby enabling risk identification for roof water inrush under multi-working face mining conditions. Taking the multi-working faces of the Banji Coal Mine in Anhui Province as a case study, the predicted height of the water-conducting fracture zone is 60 m, with no risk of delayed roof water inrush in the preceding working face. Both numerical simulation results and field measurements of the development height of the water-conducting fracture zone confirm the effectiveness of this method. It is capable of accurately predicting the development height of the water-conducting fracture zone under multi-working face mining conditions and identifying the associated risk of roof water inrush, thus providing a valuable reference for ensuring safe mining operations in multi-working face mining conditions.  
multi-working face mining; key strata theory; water-conducting fracture zone; numerical simulation; risk identification method of roof water inrush
Finally, energy/time costs are evaluated under different communication radii, and the scalability performance is examined by increasing the group size. A statistical method is applied to analyze the energy/time measurements and scalability effects. The results indicate that the flocking protocol adopting the minimally connected communication radius is considered as the most promising swarm from the perspective of energy savings and the rate of convergence. This conclusion tactfully echoes from the biological evidence that each starling interacts with only six or seven nearest-neighbors most often at the same time. This paper is organized as follows. Materials and methods are given in Section 2. Section 3 illustrates the effectiveness of the proposed solution for UAV flocking. Section 4 shows the energy/time measurements and scalability effects in a quantitative way. Finally, this paper is concluded in Section 5.
## 1 Introduction
In this situation, [PERSON] [44] converted the estimated FR distribution to the vertical total electronic content (VTEC) map, and then applied this map to correct the ionosphere-induced interferometric phase delay. Then, he analyzed the factors which may affect the performance of ionospheric correction on SAR interferometry, including ionospheric condition, underlying SNR, number of looks, and geomagnetic latitude. His results proved the feasibility of ionospheric correction on SAR interferometry by using the FR-based method. However, the limitation of his method is that there is the remarkable residual long-wavelength phase signal in the corrected interferogram. In order to evaluate the performance of the FR-based method and the azimuth offset method in practical applications, [PERSON] et al. [45] made an investigation using L-band polarimetric SAR data, and found that the FR-based method was more sensitive to the noise than the azimuth offset method. Meanwhile, it was found that both methods present the residual ionospheric phase error in the corrected interferogram. To address limitations in the current ionospheric correction methods on SAR interferometry, an efficient FR-based method is employed to mitigate the ionospheric errors on full-polarimetric SAR interferometry. This method exploits the fact that ionospheric errors on an interferogram can be extracted based on the relationship between FR angles and VTEC (vertical total electronic content). Compared with the existing FR-based methods introduced in [44] and [45], the proposed method uses an integrated phase model, which can correct the ionospheric error as well as the residual ionospheric phase error caused by inaccurate parameters on SAR interferometry, and thus can improve the correction precision. The process is composed of three steps: (1) estimation of FR angles from full-polarimetric SAR images, (2) generation of VTEC maps from FR angles, and (3) joint correction of long-wavelength ionospheric phase errors on full-polarimetric interferograms. For performance testing of the proposed method, L-band ALOS/PALSAR full-polarimetric SAR images over high-latitude and low-latitude regions are processed. The FR angles and VTEC maps are estimated from SAR images,and then ionospheric errors of full-polarimetric interferograms are calculated and corrected from VTEC maps.
## 3 Results and Discussions  
### Verticillium wilt of horseradish Roots Depending on the Studied Factors  
Root diseases, in contrast to those occurring on leaves, directly affect the commercial value of horseradish, therefore, they pose a greater problem for agricultural producers. Fruit and vegetable processing plants, which in 90% are direct recipients of raw material from farmers, set requirements regarding horseradish root quality. Roots intended for the food industry should have smooth, light skin, and they should be free from defects such as cracks, deformation, or damage caused by pests, and internal discoloration is not tolerated either. A three-year study showed that horseradish cultivation in Poland is endangered by Verticillium wilt of horseradish roots. The intensity of this disease depends significantly on the conditions prevalent in a given growing year or season. Weather conditions in the years of the study were varied (Table 2). They are described using the Sielianinov (\(K\)) hydrothermal index:  
\[K=\frac{p}{0,1\cdot\sum t}\]  
where: \(p\)--precipitation total for the examined period (mm); \(t\)--mean daily air temperature (\({}^{\circ}\)C). Of the seasons covered by the study, 2015 was the driest year (see Table 2). As little as 311.9 mm of rainfall was recorded from March to October. In the first growing season (April-June), precipitation totaled 127.6 mm, and in the second (July-September), it amounted to 118.8 mm. In turn, the year 2016 can be considered optimal in terms of water supply (493.7 mm) and covering the needs of horseradish plants. However, after a very humid July, there was a significant deficit of rainfall in August and September, followed by excess precipitation in October. The study period from March to October 2017 was humid (682.7 mm of precipitation) and warm. In the period April-June, total precipitation was 243.6 mm, and in July-September, it totaled 300.0 mm. This means that the horseradish plants were sufficiently supplied with moisture. The organisms responsible for the development of Verticillium wilt attack the roots with more intensity during drought periods, while the temperature seems to be a factor of lesser importance. The total precipitation of 682.7 mm in 2017 resulted in a significant reduction of discoloration within the vascular bundles (Figure 1). In turn, a decrease in the amount of precipitation by 189 mm in 2016 resulted in a significant increase in the disease severity index (DSI). On the other hand, the significant water deficit (311.9 mm) in the first year of the study contributed to the increase in the DSI. As the disease symptoms intensify, darkening of the tissues of the core (on the inside) and the cortex (on the outside) is observed. Internal discoloration of the roots significantly reduces the commercial yield, often completely (100%) disqualifying them from being used for processing [32].
## 4 Experiments
### Ablation Study
#### 4.6.2 Effectiveness of the Asymmetric Consistency Loss  
Furthermore, in addition to the performance comparison with the uncertainty loss, we also evaluated the impact of removing the asymmetric consistency loss (\(\mathcal{L}_{ac}\)) on UCDA's performance, as depicted in Figure 6 (UDA). For this purpose, we conducted additional experiments on the PCam dataset where we excluded the asymmetric consistency loss and analyzed the resultant changes in model performance.  
As illustrated in Figure 6, when the asymmetric consistency loss is incorporated into our method, UCDA exhibits superior performance compared to the model without the asymmetric consistency loss. Specifically, UCDA achieves improvements of 2.35% in accuracy and 2.86% in F1-score. These findings validate the effectiveness of the asymmetric consistency loss in promoting domain alignment within UCDA. By leveraging the asymmetric loss, UCDA accurately maps the feature space from the source domain to the target domain, facilitating enhanced alignment between the two domains. Consequently, UCDA demonstrates improved performance in lesion recognition, benefiting from the refined domain alignment achieved through the asymmetric consistency loss.
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 5 Conclusions  
Traffic assignment computations heavily rely on the link performance function. The ultimate computation results are significantly influenced by the parameters chosen. It is difficult to obtain the right values based only on experience because the physical importance of these characteristics is not always obvious. As a result, this research suggested a technique for investigating the calibration of link performance function parameters that are appropriate for the unique features of big cities. Numerous major cities' active traffic planning projects can greatly benefit from this information.  
In this study, the parameter calibration problem was solved using [PERSON]'s technique, BO, and the DE algorithm. The results showed that [PERSON]'s method was substantially more effective than DE and BO. Nonetheless, the DE approach is naturally parallelizable, and we greatly sped it up by parallelizing the work on a six-core CPU. Furthermore, we used a variety of methods to classify journeys into business and non-work travel categories in order to calculate the value of travel (VOT).  
Future studies should focus on using cutting-edge technologies, such as machine learning and deep learning, to assess the performance and impedance of roadways in order to increase the precision of path planning [52].  
Conceptualization, M.C. and K.H.; methodology, M.C. and J.W.; software, M.C.; validation, M.C. and J.W.; formal analysis, M.C. and W.L.; resources, Y.S. and W.L.; writing original draft preparation, M.C.; writing--review and editing, J.W., K.H. and W.L.; visualization, M.C. and K.H.; supervision, Y.S.; project administration, J.W.; funding acquisition, Y.S., J.W. and K.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Laboratory for Traffic and Transport Planning Digitalization Program (No. (2022)JH-F05), the Key Laboratory Open Fund of the Transportation Industry in Comprehensive Transportation Theory (MTF2023001) and the National Natural Science Foundation of China Youth Program (No. 72301066).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 2 Physiological Basis of Fitts' Law
### Connection between Fitts' Law and Nerves  
The rate at which afferent signals from the hand's muscles travel to the cerebral cortex is similar to the rate at which corticospinal different signals travel to the hand's muscles. The range of efferent conduction velocities is 11-18 m/s. There is a minor delay of around 10 ms when signals are transmitted from the cerebral cortex to the intercostal muscles as opposed to the hand muscles via corticomotor signals [10]. Fascinatingly, the cerebral connection pattern shown in the hand muscles is at odds with this delay. Reduced conduction speed inside the central nervous system or delayed peripheral conduction of intercostal muscle afferents may be the cause of the disparity in afferent and efferent cortical transmission times [11].  
[PERSON] law accurately predicted human movement, so [PERSON] et al. wondered if the target's size and distance would affect the body's ability to react quickly and accurately, as well as the rate at which electrical signals from the internal nervous system are transmitted. Based on [PERSON] et al.'s experiments to estimate Fitts' law coefficients by modeling brain currents with motor currents, we hypothesize that [PERSON] law does affect psychomotor latency in humans [12]. This hypothesis was supported by [PERSON] and [PERSON]'s 1988 demonstration of the invariance of motion using endpoint vector integral circuit modeling and the transformation of distance differences into changes in muscle contraction to produce synergies [13].
## 3 Hysteresis Curve Characteristics of GRS
### Effect of Number of Fines Content on the Hysteresis Curve Parameters  
The fines content in the GRS affects the shape of the hysteresis curve. Figure 6 depicts the variation of four hysteresis curve parameters with the fines content. According to the amount of fines content, it is divided into three ranges, i.e., low fines content (C0 to C10, Range I), medium fines content (C10 to C20, Range II) and high fines content (C20 to C30, Range III). When the fines content increases, \(S\), \(d\), and \(\varepsilon_{sp}\) of the hysteresis curve increase. They increase quickly in Range I and Range III but slowly in Range II. Additionally, the influence of fines content is greater at low-load cycles than at high-load cycles. Taking \(S\) as an example, the values of \(S\) under cyclic load are listed in Table 3. Under low load cycles (\(N=1\)-\(20\)), the average area of \(S_{\text{C30}}\) is 6.04, 2.5 times that of \(S_{\text{C0}}\). However, the average area of \(S_{\text{C30}}\) is only 1.4 times that of \(S_{\text{C0}}\) under high-loading cycles (\(N=1000\)-\(10\),\(000\)). The soil group of C0 has the maximum \(k\), and the increase of fines content makes \(k\) decrease. \(k\) has a noticeable decrease in Range I and Range III, but it has a small change at low load cycles and fluctuates at high loading cycles in Range II.  
Figure 5: Variation of hysteresis curve parameters with number of load cycles (T0).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loading Cycle \(N\)** & \(S_{\mathbf{CO}}\) & \(S_{\mathbf{CI0}}\) & \(S_{\mathbf{C20}}\) & \(S_{\mathbf{C30}}\) \\ \hline  
Average & 0.54 & 0.50 & 0.78 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hysteresis curve area \(S\) under cyclic load (T0, for example).  
Figure 6: Variation of hysteresis curve parameters with fines content (T0). Note: Number of load cycles * is the value after logarithmic processing. For example, when the number of loads is 1000, the number of load cycles * = log(1000) = 3.
## 4 Security Analysis and Performance Assessment  
This study used MATLAB R2024a to simulate and evaluate the principle, security, and efficiency of the proposed protocol. On a vast, unobstructed sea surface, the wave roughness generates significant multipath effects for signal propagation. To model the time-varying Rayleigh fading channel for mobile communication nodes, we utilized the Jakes model. Assuming no hardware errors or time synchronization inaccuracies, the simulation parameters were based on the Class A AIS technical standard specified in the ITU-R M.1371-5 technical document. The key simulation parameters are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Transmit power & 12.5 W \\ Carrier frequency & 161.975 MHz \\ Sampling frequency & 76.8 KHz \\ Modulation & GMSK \\ Modulation index & 0.5 \\ Data encoding method & NRZI \\ Time slot size & 26.67 ms \\ Spectral efficiency \(r\) & 0.8 bps/Hz \\ Simulated channel multipath number \(N\) & 8 \\ SNR value range & 0\(\sim\)40 dB \\ Probing signal size & 24 bit \\ Hash function & SHA-256 and BLAKE2 \\ Significance level \(\alpha\) & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The key simulation parameter settings.