esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### The Process of the ADMTCF
#### 3.1.1 HSV Color Space and LBP Encoding  
According to the literature [34; 35], there are two key points. Firstly, in HSV color space, the hue is less sensitive to illumination changes. Secondly, having sufficient object features can obtain high tracking accuracy. When the tracking object encounters illumination changes, the ADMTCF converts the image from RGB to the HSV color space and then uses hue to perform LBP encoding to maintain good tracking accuracy. In the ADMTCF, we select the object by using the ground truth of the first frame. This image is as in Figure 3a. We convert the image of the object from the RGB to the HSV color space. This image is as in Figure 3b. We use LBP to encode the luminance of the image. This image is as in Figure 3c.
## 3 Results and Discussion
### Synthesis and Characterization of HB-AuNPs
#### 3.1.3 HRTEM Analysis of 2 mg/mL_HB-AuNPs  
The smaller-sized HB-AuNPs obtained with 2 mg/mL HBE concentrations were selected for HRTEM analysis to acquire insight into the shape and core size distribution of the NPs. The micrograph in Figure 4A shows that HBE formed spherical- and pentagonal-shaped AuNPs with an average core size of 37 nm (Figure 4B). AuNPs synthesized using plant extracts often exhibit polydispersity, which could be due to the fact that the extracts contain a variety of phytochemicals acting synergistically in the reduction and stabilization of AuNPs [4; 19; 36]. The size of the NPs is of critical importance since it influences their bioavailability, bioaccumulation, and toxicity in a biological system [38]. The EDX spectroscopy in Figure 4C further confirmed the formation of HB-AuNPs through the presence of elemental gold (Au) atoms around 2.30, 8.10, 9.40, and 11.30 keV. The crystalline nature of the HB-AuNPs was illustrated by the selected area electron diffraction (SAED) pattern (Figure 4D) corresponding to (111), (200), (220), and (311) reflections of face-centered cubic gold. These findings were consistent with the previous observations reported for AuNPs  
Figure 3: Hydrodynamic size distribution of HB-AuNPs synthesized at various HBE concentrations at 70 \({}^{\circ}\)C.  
synthesized from _Memecylon umbellatum_[42], _Terminalia mantaly_[36], _Salvia africana-lutaa_, and _Sutherlandia frucescens_[23].  
The 2 mg/mL_HB-AuNPs synthesized from _Cyclopia genistoides_ had a similar profile to the ones previously synthesized from _C. intermedia_. These two species (_C. genistoides_ and _C. intermedia_) out of the twenty-three registered HB species are so far the only ones to have been used to synthesize AuNPs. The HB-AuNPs from _C. intermedia_ (HB-AuNPs_ci_) were synthesized following the optimal conditions used to synthesize 2 mg/mL_HB-AuNPs by _C. genistoides_ and had similar physicochemical properties: SPR of 540 nm, hydrodynamic size of 67 nm, 0.57 PDI, and a zeta potential of \(-\)23 mV [4]. These similarities suggested that the two species most probably have the same phytochemical profiles, which might be responsible for HB-AuNPs synthesis.
## 3 Results and Discussion
### \(pH\)  
The pH value spiked after one week of storage, as can be seen in Figure 7. Such an increase was also observed by [PERSON] et al. (2014) and colleagues on the 15 th day after slaughter [24]. Following this, in our measurements, the pH value began to steadily decrease, a trend that was observable until the end of the aging process. This continuous decrease in pH is attributed to the production of lactic acid by anaerobic microorganisms during meat maturation. This decrease in pH was evident even upon unpacking the meats, manifesting as an unpleasant odor. The initial increase in pH may be related to the concurrent retention of moisture, as discussed in the earlier sections.  
Figure 6: Photos of samples after the four-week aging experiment.  
Figure 7: pH [-] results of different sample groups (I., II., III., IV.) at each measurement day (0, 7, 14, 21, 28) (lowerase Latin letters indicate significant differences according to sample groups and uppercase Latin letters indicate significant differences according to storage time).  
Contrary to our findings, [PERSON] and [PERSON] (1993) observed a trend of continuous increase in the pH values with increasing aging time, which they attributed to charge separation caused by proteolytic enzymes ([PERSON] and [PERSON], 1993). However, they also pointed out that the pH values may not necessarily increase in vacuum-packed means, as the lack of oxygen transfer can lead to increased acidity. It is highly likely that this also contributed to the decrease in pH observed in this case.  
After checking the equality of error variances by [PERSON]'s test [pH: F(4,103) = 1.496, \(p\) = 0.013] and normality of residuals with the d'Agostino test [the sum of squares of the ratio of skewness and standard error of skewness and peak and standard error of peak is compared to a Khi-squared distribution with 2 degrees of freedom (\(p\geq\) 0.001 in case of unstandardized residuals of pH)] ANOVA was carried out. Storage time had a significant effect (\(p\) = 0.05) on pH. The pH showed a decreasing trend with storage time. Four homogeneous groups could be separated by [PERSON]'s test, in ascending order of pH: (1) 4-week and 3-week samples; (2) 3-week and 2-week samples; (3) 0-week samples and (4) 1-week samples.
## 3 Results and Discussion
### Effect of QS Signaling on Biofilm Formation on Microplastic Surfaces
#### 3.4.2 Injection of QQ Enzyme  
After confirming that exogenous AHL did not affect PAO1 biofilm formation, the effect of AHL removal on biofilm formation on the microplastic surface was investigated. The removal of AHL was achieved by adding a QQ enzyme extracted from _E. coli_ AiiO.  
As shown in Figure 6, the overall biofilm formation was reduced by the addition of the AiiO enzyme extract (\(p=0.0138\)). There was no significant difference in the number of biofilms formed on the PE microplastic surface in the control group (Empty) and the QQ treatment group (AiiO) in the 2-6 h period. However, the QQ treatment reduced biofilm formation by 30.1% (\(p=0.019\)) after 8 h. This is consistent with previous reports, which found that the effect of AHL on the biofilm formation of microorganisms is greater at the biofilm growth stage than at the initial attachment stage [36]. Therefore, the biofilms formed on the moving microplastic surface showed slightly different tendencies from those formed on the fixed surface. However, it was confirmed that QS remains an important mechanism for biofilm formation on microplastic surfaces. This suggests that the QS mechanism must be considered in future studies on biofilm formation on microplastic surfaces.
## 4 Experimental Design and Results
### Results
#### 4.5.4 Graphical Analysis  
Graphs were generated based on the average results of six images taken from each of the four different datasets. These graphs present data from the comparison algorithms, the proposed innovative DEA, and the S+DEA.  
The graphs are organized as follows:  
* Figure 10 presents the CR (Compression Ratio) values,
* Figure 11 displays the BPP (Bits Per Pixel) values,
* Figure 12 illustrates the SS (Space Saving) values.  
These visual representations facilitate a clearer comparative analysis, highlighting the performance of the proposed methods against well-established comparison algorithms.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 4 Experimental Results
### Time Analysis  
Encrypting the 128-bit data with AES encryption would take 68.45 nanoseconds (ns). Indeed, the AES encryption and decryption are pretty fast. Thus, the most time consuming part of the proposed scheme is calculating the decryption key. To see how long the decryption key calculation takes, let us take five authorized classes. Figure 10 shows an example of how fast the decryption key calculation with five authorized classes runs. We measured the average time taken to perform the decryption key calculation for 1000 times. We can see that it takes only 2.551 microseconds (\(\upmu\)s) for \(C_{1}\), and 112.997 \(\upmu\)s for \(C_{5}\) since \(C_{1}\) computes one decryption key and \(C_{5}\) computes five decryption keys. Longer calculation time could be taken by further increasing the number of authorized classes. Figure 10 also gives the average time of calculating one decryption key for each authorized class. We note that it would take approximately 5 \(\upmu\)s for each additional decryption key calculation. Consequently, the proposed encryption and decryption are more computationally efficient, making the proposed scheme suitable for practical applications.
## 3 Results
### Multi-Parameter Ionospheric Anomalies
#### 3.1.3 Ne and Te Anomalies
Negative Te anomalies continuously occurred from 30 September and 3 October, strong positive anomalies occurred on 19-20 and 24-25 October, and on 26 October, a negative anomaly was observed passing over the earthquake epicenter for the daytime trajectory, as explained in Table 1 and shown in Figures 8 and S3. The other earthquake happened as a group of earthquakes on 7, 11, and 15 October 2023; the \(\Delta\)Ne thresholds were 4.97 \(\times\) 10\({}^{11}\) m\({}^{-3}\) and 2.9 \(\times\) 10\({}^{10}\) m\({}^{-3}\) during the night and day periods, respectively, and \(\Delta\)Te thresholds were 1252 K and 739 K.  
Excluding solar and geomagnetically disturbed days, Ne anomalies started appearing on September 20 (\(-\)) and 29 (+), 30 (+), and 2 (+) of October; the remaining disturbed days were taken as geomagnetically disturbed days, which confirmed the presence of pre-seismic disturbances (Figure 9). Ne daytime anomalies, starting from 20 (+), 22 (\(-\)), 24 (+), 27 (+), 28 (\(-\)), 29 (\(-\)), and 30 (+) September, and 1 (+) and 3 (+) anomalies on 11 and 15 of October (\(-\)), on the days of the earthquake, can be observed. Figure 8: Ionospheric plasma Ne and Te anomalies prior to the 26 October 2015 earthquake for the nightside orbit (descending around 23:00) and the dayside orbit (ascending around 11:00). Black star represents the earthquake epicenter and gray line shows the costal boundaries. In contrast, in the nighttime Te data, there were negative anomalies that appeared on 21, 22, 23, 29, and 30 September and on 1 and 2 October, while the occurrence of strong negative anomalies from 4 to 15 can be seen, which can be regarded as pre-, co-, and post-seismic anomalies. The daytime Te anomalies continuously occurred from (+)20 and 28(\(-\)) September to (\(\pm\))1, (\(\pm\))2, and (\(\pm\))3 October, as explained in Table 2 and shown in Figure 9 and Figure S4. Figure 10 shows the correlation analysis of \(\Delta\)Ne and \(\Delta\)Te for Swarm A and Swarm C satellite data during the daytime and overnight periods. The plasma abnormalities show an increasing occurrence on the days preceding the earthquakes. This shows that there is a link between fluctuations in plasma anomalies and seismic activity. The data show a  
Figure 9: Ionospheric Ne and Te anomalies prior to the 7, 11, and 15 October 2023 earthquakes for the nightside orbit (descending around 23:00) and the diurnal orbit (ascending around 11:00). Black star represents the earthquake epicenter and gray line shows the costal boundaries. negative association between Ne and Te, which is most prominent during the nighttime observations. Furthermore, the graphs demonstrate that plasma abnormalities are more noticeable during the day, implying a possible diurnal fluctuation in the association between plasma anomalies and earthquake occurrence.
# Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image
## 3 The Proposed Approach  
In this section, we describe the structure and implementation of a multi-layer fusion 3D object detection network. The proposed network architecture is shown in Figure 1. The network architecture consists of three main parts: feature extraction network, regional suggestion network (RPN) and fusion network. Using RGB images and the bird's eye view (BEV) as inputs, the feature extraction network processes them to obtain the corresponding feature maps. In this paper, VGG [33] is used as a backbone network. In addition, 100 K anchors are preset as the initial input to the RPN in 3D space. The RPN network filters these anchor boxes to obtain the RoI. Then, the feature maps are combined with the RoIs generated by RPN, and the corresponding feature region is cut out and sent to the fusion network for the final parameter regression. In the fusion network, we adopt a novel multi-layer fusion method, D-fusion, which can effectively combine features from different perspectives and retain the semantic information of each layer of the network so as to achieve 3D bounding box regression. Finally, the fusion network outputs classification and regression results. Both the feature extraction and fusion methods of the network adopt the multi-layer fusion approach to achieve richer feature expression and more efficient data processing.
# Head-Related Transfer Functions for Dynamic Listeners in Virtual Reality
## 4 Experiment I: Static Localization
### Results  
Figure 3 collects the local azimuth and elevation unsigned errors (panels a to d) and quadrant errors percentage (panel e). The quadrant errors percentages display a tendency for lower errors for the modeled and measured HRTFs compared to the generic HRTFs, but the effect is not significant ([PERSON] test: \(\chi^{2}_{(2,N=8)}=4.96\), \(p=0.084\), [PERSON]'s \(W=0.31\) (moderate effect size)). The local azimuth and elevation error angles are calculated after the quadrant errors (\(>\)90\({}^{\circ}\)) are filtered from the data. The azimuth errors are collected across all elevation angles and elevation errors across all azimuth angles. In the front, for azimuth errors, no large differences can be observed apart from the modeled HRTFs yielding lower errors at the \(\phi=15^{\circ}\) angle compared to the two other HRTFs. On the side, the measured HRTFs result in lower azimuth errors than the modeled or generic HRTFs, while at the back orientations, no differences are observed. The average azimuth angular errors collated across all azimuth angles are observed to be 17\({}^{\circ}\) (Measured), 21\({}^{\circ}\) (Modeled), and 20\({}^{\circ}\) (Generic), with a non-significant main effect of the HRTF set ([PERSON] test: \(\chi^{2}_{(2,N=8)}=4.00\), \(p=0.135\), [PERSON]'s \(W=0.25\) (small effect size)).  
For elevation errors, negative elevation angles below the horizontal plane tend to yield greater unsigned localization errors than positive elevations. A tendency for the modeled HRTFs to yield larger errors than the other HRTF sets in the horizontal plane and below can be observed. The average elevation angular errors collated across all elevation angles are observed to be 21\({}^{\circ}\) (Measured), 26\({}^{\circ}\) (Modeled), and 21\({}^{\circ}\) (Generic), with a non-significant main effect of the HRTF set ([PERSON] test: \(\chi^{2}_{(2,N=8)}=4.00\), \(p=0.135\), [PERSON]'s \(W=0.25\) (small effect size)). Average duration to indicate a target was found after the stimulus playback was 5.9 s (SD = 2.0 s).  
Figure 3: Subjective scores from the static localization experiment for (**a**) local unsigned azimuth error, (**b**) local elevation unsigned error, (**c**) average local azimuth unsigned error, (**d**) average local elevation unsigned error, and (**e**) quadrant errors percentage. The error bars denote the bootstrapped 95% confidence intervals of the mean.
## 2 System Model
### AML Detection
* JML detection: An extension of the ML decision rule to cooperative DF MIMO systems. It considers both source-to-destination and relay-to-destination links. However, it suffers from high computational complexity because of an exhaustive search over all possible symbol combinations. * AML detection: Low-complexity approximation of JML detection. Its decision rule is derived from a tractable PEP expression, which significantly reduces the computational burden while preserving a near-optimal performance.
## 4 Validation of the Proposed SDoF Model
### Overall Dynamic Response Histories  
Figure 5a,b illustrates the velocity time history and the force-displacement relations resulting from the SDoF and numerical models.  
Figure 4: Spring-back and maximum contact force versus different mesh sizes.  
Figure 5: Comparisons of response characters between the SDoF and numerical models. **(a)** Velocity-time history of the striker. **(b)** Impact force-displacement curve.  
It transpires from Figure 5a that the analytical results correlate well with the numerical results in terms of the variation of the velocity of the striker with time. The velocity of the impact system decreases to zero until reaching the maximum displacement at the end of the loading stage and then increases to \(V_{r}\) with an opposite direction due to the elastic strain energy stored in the beam. To study the influence of the impact location on the variation of velocity with time, a parameter \(a_{v}=(V_{0}+V_{r})/t_{f}\), which represents the average rate of change of velocity over the response time, is introduced here. It is found from Figure 5a that with the increasing \(l_{1}/l\), \(a_{v}\) becomes smaller and so does its changing rate.  
One can see from Figure 5b that the proposed SDoF model can successfully predict the relevance of the impact force to the beam deflection at the impact point, except for a slight difference in the unloading path caused by the difference in the maximum deflection and the change of the transverse displacement profile assumed in Equation (28). The main characteristics of the predicted impact force-displacement responses can be summarised as follows. The growth of the impact force is almost linearly proportional to further increases in deflection for the sufficiently larger displacement behavior of a beam. Different from the almost overlapping loading paths for the central impact cases with different impact velocities analysed in [19], it is evident that the slope of the loading path becomes smaller with the increase of \(l_{1}/l\). To illustrate this phenomenon, a fully plastic beam with \(T=T_{p}\) is taken as an example. It is revealed from Equation (26) that as \(l_{1}\) approaches \(l_{2}\), the slope of the loading path decreases and gets its minimum in the central impact case with \(l_{1}=l_{2}\). Likewise, the variation of the slope of the unloading path with \(l_{1}/l\) shows the same trend as that of the loading path, which can be explained from the perspective of the elastic strain energy. With the approximation of the unloading path as a linear function of \(\Delta W\), the slope during unloading can be estimated as  
\[K_{u}=\frac{F_{m}^{2}}{2E_{e}}=\frac{F_{m}^{2}}{m_{0}V_{r}^{2}} \tag{38}\]  
where \(E_{e}\) is the elastic strain energy stored in the beam.  
It will be shown in Figure 6c that with an increase of \(l_{1}/l\), the rebound velocity \(V_{r}\) increases while the maximum impact force \(F_{m}\) decreases, with detailed analysis referring to Section 4.2. Thus, it follows from Equation (38) that the slope of the unloading path \(K_{u}\) decreases with an increasing \(l_{1}/l\).
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 4 Mathematical Model
### Objective Functions  
\[C=\text{min}c_{1}\sum_{j\in V/0}\sum_{k\in K}x_{k0j}+c_{2}\sum_{i\in V}\sum_{j \in V}\sum_{k\in K}e_{kij}x_{kij}+c_{2}\sum_{k\in K}\sum_{w\in V/0}\sum_{i\in V }\sum_{j\in V}\left(e_{u_{k}iw}+e_{u_{k}wj}+e_{u_{k}j}\right)y_{iwj}^{u_{k}} \tag{9}\]  
Equation (9) represents the objective function, which minimizes the total delivery cost. The first term corresponds to the vehicle dispatch cost, the second term represents the vehicle energy consumption cost, and the third term accounts for the drone energy consumption cost.
## 4 Results
### The Effect of the Proppant Concentration  
The greater the proppant concentration, the greater the initial width of the fracture, resulting in more proppant layers in the fracture. In this section, different maximum initial fracture widths are set in the calculation to represent different proppant concentrations. The maximum initial fracture width of the main fracture is twice that of the secondary fracture. As shown in Figure 5, the higher the proppant concentration, the greater the fracture width of the overall fracture network.  
As shown in Figure 6, the surface flow properties of the fracture decrease slightly with the increase in the proppant laying concentration. The reason is that, the larger the initial width of the fracture, the higher the deformation energy stored in the fracture surface deformation, which increases the effective closing stress of the fracture surface, thereby increasing the embedding of the propp  
\begin{table}
\begin{tabular}{c c c c} \hline
**Simulation Parameters** & **Values** & **Units** & **Symbols** \\ \hline Maximum horizontal principal stress & 50 & Mpa & \(S_{H}\) \\ Minimum horizontal principal stress & 45 & Mpa & \(S_{h}\) \\ Initial formation temperature & 410 & K & \(T_{0}\) \\ Thermal conductivity & \(3\times 10^{-11}\) & m\({}^{2}\)/(s\(\cdot\)K) & \(\beta_{T}\) \\ Interstitial convective heat transfer coefficient & 1000 & W/(m\({}^{3}\cdot\)K) & \(q_{sf}\) \\ Desorption rate of adsorbed gas & \(0.8\times 10^{-5}\) & 1/s & \(R_{d}\) \\ Adsorption rate of free gas & \(0.8\times 10^{-6}\) & 1/s & \(R_{f}\) \\ Effective stress coefficient & 0.8 & - & \(\alpha\) \\ Viscosity coefficient & \(1\times 10^{17}\) & Pa-s & \(\eta\) \\ Proppant geometric coefficient & 2, 4, 6 & - & \(c\) \\ Proppant elastic modulus & 5, 15, 25 & GPa & \(E_{p}\) \\ Proppant Poisson’s ratio & 0.2, 0.3, 0.4 & - & \(v_{p}\) \\ Proppant diameter & 0.2, 0.5, 1 & mm & \(d_{p}\) \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Effect of proppant concentration on the fracture width.  
Figure 6: Effect of proppant concentration on the dimensionless fracture surface crossflow coefficient.  
laying concentration maintains a larger fracture conductivity and drainage area, the increase in fracture width increases the fracture gas transportation capacity, thereby reducing the average gas pressure in the fracture, but increases the embedding amount of proppant, which reduces the crossflow coefficient of the fracture surface. After comprehensive consideration, the gas supply rate of the matrix to the fracture does not increase significantly with the increase in the fracture width. Therefore, although increasing the proppant laying concentration will reduce the degree of fracture closure and increase the fracture conductivity, blindly improving the fracture conductivity will not effectively increase shale gas production.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation
### Evaluation with Real Datasets
#### 5.2.1 Neighbor Count  
The average neighbor counts of the mobile nodes are compared in this section They are collected both using simulation and real-world traces in Figure 7. In the emergency room, the average neighbor counts for different types of assets are slightly different given the fact that assets are stored in different places based on what they are. As Figure 7 clearly shows, the neighbor count of a mobile node in the emergency room is higher than that of a mobile node in the simulation. This is because several anchor nodes outside the communication range of a mobile node can communicate with the mobile node in the emergency room due to RF reflection.