esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Changes in pH, TA, TSC, LAB Viability and Color
The deacidification observed on day 28 could be attributed to the changes in buffering capacity, also observed in the case of other plant-based products as reported by [PERSON] et al. for soya yogurt [39]. The TSC of the samples significantly increased as a result of fermentation (_p_ < 0.05); however, during refrig  
Figure 1: Lactic acid bacteria (LAB) counts during storage time. marked changes were noticed in either variant (\(p>0.05\)). It should also be pointed out that the TSC of the CPC-15% sample was similar to the TSC reported for dairy full milk yogurts [40], whereas the TSC of CPC-20% was slightly lower than that reported for the Greek type of yogurt [41; 42]. The changes in color parameters as well as total color difference (\(\Delta\)E) are summarized in Table 2. It was observed that fermentation significantly decreased the color attributes of both variants (\(p<0.05\)). The most noticeable change was observed for a * (redness) value. The color of fermented beverages is frequently associated with the presence of pigments in the raw material; also, changes in the storage time and pH can affect the color of fermented foods [8; 19]. In fact, as shown in Table 1, the fermentation of CPC resulted in significant acidification of the matrix, thus, the observed a * decrease can be presumably linked with the stability of carotenoids [1]. [PERSON] et al. also reported significant decrease in a * value in solid-state fermented camelina meal [8]. A similar reduction in redness as a result of fermentation was also reported for flaxseed oil cake [19] as well as for fruit juices [43]. The total color difference (\(\Delta\)E) \(\geq\) 1 is considered perceptible to the human eye. It was noticed that on day 1 the \(\Delta\)E values of the samples were \(\leq\) 1.00 (1.00 \(\pm\) 0.01 and 0.87 \(\pm\) 0.01 for samples CPC-15% and CPC-20%, respectively); thus, only for sample CPC-15% were the changes in color attributes visually noticeable. On the other hand, the highest \(\Delta\)E value (4.55 \(\pm\) 0.01) was observed for sample CPC-15% on day 14.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 10 Mitigation Techniques of Beam Systematics at the Neutrino Detectors
### The PRISM Technique  
The PRISM concept [155] is based on a movable ND located in the proximity of the source. The motion can be in any axis perpendicular to the beam axis. Both DUNE and HK envisage the use of this method: DUNE with an ND complex moving in the horizontal plane, HK with a water Cherenkov detector moving vertically in a shaft. In this way, the ND can record the spectrum as a function of the off-axis angle \(\theta\). Due to the two-body kinematics, a neutrino emitted at an angle \(\theta\) with respect to the beam axis and produced by a pion decaying along the beam axis has an energy of:  
\[E_{\
u_{\mu}}\ =\frac{m_{\pi}^{2}-m_{\mu}^{2}}{2(E_{\pi}-p_{\pi}\cos\theta)} \tag{14}\]  
As a consequence, for any \(\theta\) we can find the maximum neutrino energy differentiating Equation (14). This energy is:  
\[E_{\
u_{\mu}}^{Max}=\frac{m_{\pi}^{2}-m_{\mu}^{2}}{2E_{\pi}\sin\theta} \tag{15}\]The pion distribution is not monochromatic, and the wider the band of the beam, the wider the number of neutrinos at an angle \(\theta\) that have an energy far from \(E_{\
u_{\mu}}^{Max}\).  
PRISM traces the energy spectrum of the wide-band beam as a function of \(\theta\). Each detector position provides a spectrum and the beam is sliced in many distributions, whose mean energy is given by Equation (15) with a width smaller than the original band. In a sense, a single PRISM point records an approximate monochromatic beam where the energy of the incoming neutrino is known a priori with a smaller width. All these slices are thus combined to provide the correct migration matrix at FD, i.e., the probability that a \(\
u_{\mu}\) with energy \(E\) is seen at a reconstructed energy of \(E^{\prime}\) due to the biases of the final state particles. This approach mitigates the problem of energy unfolding and the corresponding systematics on the oscillation parameters.
## 3 Experimental Verification
### Facade Image (Orthophoto) Generation  
To estimate the projection matrix for each of the collected images, we used VisualSfM [51]. VisualSfM is an open-source SfM software that provides a user-friendly graphical user interface (GUI) to monitor the intermediate steps of the SfM process, such as keypoint feature matching and camera pose estimation. VisualSfM highly improved the speed of the SfM computations by implementing the SiftGPU Library and parallel processing using graphics processing units (GPUs) [44; 45; 46; 47; 48; 49; 50; 51]. Finally, VisualSfM yielded two outputs that were important in this work; namely, the projection matrix of each collected image and the point cloud of the Church of Agios Nikolaos. The MLESAC algorithm for facade plane estimation from the point cloud and orthophoto generation was implemented in MATLAB [52; 53]. The average processing time for generating a total of four complete orthophotos from the 182 images was 1.5 h using the PC workstation mentioned earlier.  
Since the Church of Agios Nikolaos has four major obvious facades, we did not need to manually assign the parameter \(j\). If a TOC has a complicated shape of facades, the value of \(j\) can be set manually. The algorithm here was able to successfully estimate the four facade planes, and then the four orthophotos were generated. Figure 9 depicts the point cloud and one orthophoto generated in May 2018. We set \(\Theta<15^{\circ}\) and \(\delta<5\) m to reduce the number of projection images for each orthophoto. These threshold values are subject to different cases, but for applications involving buildings in a community, they are likely a good choice. A total of 98 images were automatically chosen for reconstructing all four facades. A representative result showing an orthophoto of a facade plane is provided in Figure 9. Some distortions and discontinued scenes were observed at the edges, but these were outside of facade area, roof, and ground. Thus, they did not disrupt the graffiti detection process because those distortions were outside of the facade.
## 7 Comparative Analysis  
Comparative analysis primarily involves comparing the performance of the proposed PL-based approach with existing approaches in terms of the number of drift points for defect datasets. The work in [13] only presented the detection of CD points using the chi-square statistical test in software-fault datasets. A comparison of the proposed approach with the work in [13] is shown in Table 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Value of NormCost for** & **Value of NormCost for** \\  & **PL-NB** & **PL-RF** \\ \hline jm1 & 1.02 & **0.91** * \\ \hline KC1 & **0.59** & **0.91** * \\ \hline P-V4 & 1.06 & 1 \\ \hline P-V40 & **0.99** * & **0.99** * \\ \hline P-V44 & 1.07 & 1 \\ \hline P-V85 & 1 & **0.99** * \\ \hline P-V192 & **0.80** * & **0.99** * \\ \hline P-V236 & **0.93** * & **0.99** * \\ \hline P-V256 & 1 & 1 \\ \hline P-V318 & **0.98** * & **0.99** * \\ \hline P-V355 & 1 & 1 \\ \hline \hline \end{tabular} Note: bold with * denotes, the model is cost effective.  
\end{table}
Table 7: Normalized costs.  
We also performed a comparison of the proposed approach with similar work in reference to various parameters, as shown in Table 9.  
From the results shown in Tables 8 and 9, it can be deduced that the proposed PL-based approach performed significantly better than existing works for the detection of concept-drift points in software-defect datasets. Furthermore, to observe the accuracy of PL-based approach in handling the CD, we applied the base versions of naive Bayes and random forest classifiers with the same settings as their paired versions on the datasets selected for the experiments and computed the prediction performances of the base methods in terms of ROC-AUC score and accuracy measures. We then compared the performances of PL-NB and PL-RF with that of the performances of base NB and base RF classifiers, respectively. We performed a comparative analysis using the experimentation results reported in Section 5.
# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 3 Experiments and Discussions
### Model Evaluation Indexes  
In this paper, precision (_P_), recall (_R_), \(F\)1, and mean average precision (_mAP_) were used as the evaluation indexes of network model accuracy, and the calculation formulas are as follows.  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{5}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{6}\]  
\[F1=\frac{2 PR}{P+R} \tag{7}\]  
\[mAP=AP=\int_{0}^{1}P(R)dR\times 100\% \tag{8}\]  
In the equations, \(P\) denotes the proportion of positive samples among all samples; \(R\) denotes the proportion of samples predicted to be positive among all positive samples; _TP_ is the number of prediction frames correctly matched with labeled frames; _FP_ is the  
\begin{table}
\begin{tabular}{c c} \hline
**Component** & **Versions, Model Number or Size** \\ \hline CPU & Intel(R) Core(TM) i7-12700H \\ GPU & NVIDIA RTX 3060 \\ VRAM & 6 GB \\ RAM & 16 GB \\ OS & Windows \(11\) \\ CUDA & v11.6 \\ Pytorch & V1.13.0 \\ cuDNN & V8302 \\ Python & V3.9 \\ \hline \end{tabular}
\end{table}
Table 1: The computational resources required for the experiment.  
number of prediction frames predicted incorrectly; _FN_ is the number of labeled frames not predicted; \(F\)1 is the summed average of precision and recall; _AP_ denotes the average precision of a category, and _mAP_ indicates the mean value of all categories of _AP_, which is usually used as a comprehensive index for accuracy evaluation. The object to be detected in this paper is only one category of pointer meter, so _mAP_ is equal to _AP_. In addition to the previously described indexes, the size of the network model and the number of calculation parameters of the network model were also introduced as evaluation indexes.
_Atmosphere_**2014**, \(5\), 699-719; doi:10.3390/atmos5040699  
_Atmosphere_  
ISSN **2073-4433**  
www.mdpi.com/journal/atmosphere  
Aerosol Optical Properties of a Haze Episode in Wuhan Based on Ground-Based and Satellite Observations  
**[PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 1,2 and [PERSON] 3**  
1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; E-Mail: [EMAIL_ADDRESS]  
2 International Research Center of Satellite Remote Sensing and Atmospheric Monitoring, Wuhan University, Wuhan 430079, China; E-Mail: [EMAIL_ADDRESS]  
3 Huazhong University of Science and Technology Wuchang Branch, Wuhan 430064, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-1592-7259-579.  
External Editor: [PERSON]  
_Received: 7 May 2014; in revised form: 9 September 2014 / Accepted: 24 September 2014 / Published: 15 October 2014_  
**Abstract:** A severe haze episode that occurred in Wuhan, central China, from 6-14 June 2012 was investigated using ground-based and satellite-derived observations, from which the optical properties and vertical distribution of the aerosols were obtained. The mass concentrations of PM\({}_{2.5}\) and black carbon (BC) were 9.9 (332.79 _versus_ 33.66 \(\upmu\)g\(\cdot\)m\({}^{-3}\)) and 3.2 times (9.67 _versus_ 2.99 \(\upmu\)g\(\cdot\)m\({}^{-3}\)) greater, respectively, on haze days than during normal weather. The large aerosol loading contributed to the high values of the scattering (2.32 km\({}^{-1}\)) and absorption coefficients (0.086 km\({}^{-1}\)). Particle size became larger, consistent with the reduced scattering Angstrom exponent. The high asymmetry parameter (0.65) and single scattering albedo (SSA) (0.97) observed in the haze, which coincided with the relatively low backscatter ratio (0.11) and up-scatter fraction (0.23), were related to the increased particle size, and could have had a heating effect on the atmosphere. Aerosols accumulated primarily below 3 km and according to CALIPSO, were regular in their shapes. At the surface, the aerosol extinction coefficient detected by satellite remained at \(\sim\)1 km\({}^{-1}\), very close to the ground-based observations. Aerosol optical properties measured at this downtown site could help further the understanding of the effects of aerosols on the air quality, city environment, and radiation balance.
## 2 Prototype Experiment
### Experimental Results  
#### 2.3.1 Structural Deformation  
As shown in Figure 7a, 15 displacement measuring points (from ZWT1-2 to ZWT1-1) are positioned around the structure. It should be noted that, based on the pre-test simulation analysis, measuring points are arranged to cover the top, bottom, and waists where structural deformations are supposed to be significant. The distance between adjacent measuring points is also kept from being too large to cover as many positions as possible. Such arrangement of measuring points can effectively reflect the overall deformation characteristics of the structure; this has been verified by multiple experimental studies of tunnel structures [29; 30; 32]. The deformation pattern of the structure remains consistent under three different buried depth conditions, with structural deformation gradually escalating. According to the test results, the structure predominantly experiences inward deformation. The inward part includes the F, T1, and C1 blocks at the top of the structure and the C2, T2, and C3 blocks at the bottom of the structure. Conversely, the outward deformation part of the structure is relatively limited and primarily includes the B3, B2, B1, and L blocks  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Loading Step** & **P1 (kN)** & **P2 (kN)** & **P3 (kN)** \\ \hline
1 & 25 & 23 & 22 \\
2 & 49 & 46 & 44 \\
3 & 74 & 70 & 67 \\
4 & 86 & 81 & 78 \\
5 & 98 & 93 & 89 \\
6 & 105 & 99 & 94 \\
7 & 111 & 104 & 100 \\
8 & 117 & 110 & 105 \\
9 (9 m service stage) & 123 & 116 & 111 \\
10 & 130 & 122 & 137 \\
11 (10 m service stage) & 137 & 129 & 164 \\
12 & 141 & 114 & 144 \\
13 & 146 & 100 & 125 \\
14 & 150 & 85 & 106 \\
15 & 157 & 110 & 119 \\
16 & 165 & 134 & 132 \\
17 (13 m service stage) & 172 & 159 & 145 \\ \hline \hline \end{tabular} \endsituated at the waist of the lining structure. Deformation zero points are located at four corners of the structure. At a buried depth of 13 m, maximum inward deformation is observed at measuring point ZWT1-2, with a deformation value of 63.82 mm. The maximum inward deformation at the bottom of the structure is 51.03 mm at measuring point ZWT2-1. Meanwhile, the maximum outward deformations at points ZWB2-1 and ZWB1-2 are nearly identical, measuring 22.48 mm and 22.55 mm, respectively, as shown in Figure 7a.  
By employing measuring points along the long and short axes, the development of convergence deformations of the short axis (top to bottom) and long axis (left waist to right waist) of the structure is obtained, as presented in Figure 7b. The three dotted lines of different colors in the figure represent the different design buried depths experienced by the structure, from 9 m to 13 m. At a buried depth of 13 m, the convergence deformation along the short axis is 108.64 mm, while that along the long axis is 49.03 mm, yielding a corresponding ratio of 2.22. Additionally, the convergence deformations along two axes develop linearly as the test load increases.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 4 Positioning and Communication for a Fleet of UAVs
### Derivation of the UAV Trajectories  
To derive the proposed positioning strategy including the backhaul constraints, an iterative optimization strategy based on [27] is applied. Specifically, at each iteration, the constraints defined in Equations (24) and (25) are evaluated as a first step. If all of them are fulfilled, the cost function defined in Equation (23) is increased by a gradient search algorithm, which leads to an increase of the lowest \(\overline{R}\) among all the coverage regions of the UAVs. Otherwise, one of the constraints is randomly chosen among all the unfulfilled constraints [27]. Then, the optimization variables are updated following the gradient of the selected constraint function. This procedure is then applied in an iterative way. Therefore, at each iteration, the implementation is applied through the calculation of the gradient of either the cost function or one of the constraint functions.
# A Data Transmission Method with Spectral Switches via Electroabsorption
## 1 Introduction  
There have been many studies on spectrum manipulation since the scaling law proposition (Wolf effect) in the mid-1980s [1, 2]. The scaling law proposition states that the spectral variations exist unless the degree of spectral coherence of the source meets that law. Since then, many mechanisms leading to spectra changes have been discussed, such as the EO effect [3] photorefractive material interactions [4], spectral correlation [5] and coherence change [6]. Some valuable results were found and applied to different areas, such as spectral switches [7], lattice spectroscopy [8], spectral anomalies [9], structured light [10] and manipulation [11]. Besides the spectra changes, other properties of light, such as the correlation [12] and polarizations [13] can also vary after propagation/or interaction with material through various mechanisms, such as the surface plasmon effect [14]. Some interesting monochromatic phenomena also found their polychromatic counterparts, such as singular optics [15] and phase singularities. Phase singularity is a phenomenon where light vanishes at points when three or more waves interfere. At these places where the intensity of the wave is zero, the phase is undefined (singular) and generally all 2 phase values occur around the zero point [16].  
A notable view called the spatial-spectral correspondence relationship for monochromatic light was proposed by [PERSON] [17], stating that for uniform incident light, the modulated monochromatic light intensity effects can be transferred into those of the corresponding polychromatic spectrum. It turns out that many of the above-mentioned results are based on this correspondence principle, although it was given a formal name later. Inspired by this relationship, we investigated how the electroabsorption effect changed the polychromatic spectrum, since the influence of this effect on monochromatic light has been studied. As shown below, we found that a spectral switch can be produced and it can be utilized as a digital data transmission scheme.
# Advanced Technologies in Optical Wireless Communications
## 4 Space Optical Communication  
Space optical communication is another emerging and important form of OWC that enables high-speed data transmission between various types of satellites, as well as between satellites and ground stations. These systems typically operate across different orbits, including geostationary Earth orbit (GEO), low Earth orbit (LEO), and high-altitude platform stations (HAPS), each offering distinct advantages in terms of coverage and latency. As a complementary technology to terrestrial wireless and optical fiber networks, space optical communication is expected to play an important role in the next generation of global connectivity [10]. However, it also faces several technical challenges, such as solar interference, atmospheric disturbances, and the complexities of accurate tracking and alignment in dynamic space environments.  
This Special Issue presents three articles that explore different aspects of space optical communication. Like other OWC systems, the performance of the transmission link in space optical communication highly depends on the sensitivity of the photodetector used. Avalanche photodiodes (APDs) are well known for their high gain and stable performance and are therefore promising candidates for space applications. Contribution 9 investigates the performance of APDs by simulating their behavior under various channel impairments, including turbulence-induced beam scintillation, pointing errors, and AoA fluctuations, using a detailed channel model and by analyzing the theoretical symbol error rate (SER). Moreover, unlike RF wireless and fiber optics, the signal received in space optical communication can be highly distorted. In recent years, there has been a trend toward exploring the use of AI-related algorithms for signal equalization [11]. Contribution 10 studies a neural network (NN)-based approach for signal equalization and compensation of distortions caused by free-space channels. Finally, Contribution 11 presents a model that examines the optical power requirements for satellite constellations throughout their orbital periods, taking multiple system parameters into account.
## 4 GKL Algorithm of Active Suspension
### Simulation Analysis
#### 4.3.2 Safety Mode Performances  
The optimal solution of weighting coefficients of safety mode is as follows: \(q_{1}=99971\), \(q_{2}=7434\), \(q_{3}=1\). The comparison curves of time domain response of passive suspension and safety mode are shown in Figure 12a-c, and the RMS values of each performance index are shown in Table 12.  
As can be seen from Table 13, no matter how the road excitation changes, the safety mode of active suspension designed by the GKL algorithm has similar optimization effects for each performance. This is similar to the discussion in the former clustering evaluation. In the safety mode, the optimization rates of BA, SWS, and DTD are about 10.7%, 33.9%, and 10.7%. In reference [14], the optimization rates of BA, SWS, and DTD for the LQR controller designed based on PSO are around 6.2%, 31%, and 6.5%, respectively. Compared with the active suspension based on PSO-LQR control, there is better optimization in BA, SWS, and DTD. Compared with the active suspension based on GA-LQR control, the improvement effect of DTD is more obvious. This mode can effectively improve the vehicle's tire grip, which is suitable for aggressive drivers who like to change lanes and overtake.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Road Class**} & \multirow{2}{*}{**Suspension Type**} & \multicolumn{4}{c}{**RMS Values**} \\ \cline{3-5}  & & **BA** & **SWS** & **DTD** \\ \hline \multirow{3}{*}{A} & Passive & 0.3377 & 0.0034 & 0.0011 \\  & GKL (Safety mode) & 0.3012 & 0.0023 & 0.0010 \\  & Improvement/\% & \(-10.81\%\) & \(-33.04\%\) & \(-10.67\%\) \\ \hline \multirow{3}{*}{B} & Passive & 0.6755 & 0.0068 & 0.0023 \\  & GKL (Safety mode) & 0.6027 & 0.0043 & 0.0021 \\  & Improvement/\% & \(-10.79\%\) & \(-36.70\%\) & \(-10.56\%\) \\ \hline \multirow{3}{*}{C} & Passive & 1.3526 & 0.0135 & 0.0045 \\  & GKL (Safety mode) & 1.2016 & 0.0091 & 0.0040 \\  & Improvement/\% & \(-11.16\%\) & \(-33.04\%\) & \(-10.61\%\) \\ \hline \multirow{3}{*}{D} & Passive & 2.7168 & 0.0271 & 0.0093 \\  & GKL (Safety mode) & 2.4227 & 0.0182 & 0.0083 \\ \cline{1-1}  & Improvement/\% & \(-10.83\%\) & \(-32.82\%\) & \(-11.07\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: RMS values and improvement rate of performance indexes for safety mode.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Suspension Type**} & \multicolumn{4}{c}{**RMS Power/W**} \\ \cline{2-5}  & **A-Class Road** & **B-Class Road** & **C-Class Road** & **D-Class Road** \\ \hline GA-LQR control & 28.55 & 87.16 & 407.87 & 1668.42 \\ Safety mode & 27.25 & 83.41 & 388.06 & 1614.46 \\ Comprehensive mode & 32.58 & 100.51 & 460.66 & 1942.44 \\ Comfort mode & 51.10 & 168.48 & 745.41 & 3268.29 \\ \hline \hline \end{tabular}
\end{table}
Table 12: RMS values of power consumption of each suspension type under different roads.