esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 5 Overview on Semi-fragile Watermarking Algorithms
### Recoverable Semi-Fragile Watermarking Algorithms
#### 5.2.3 Other Transform Domains
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Domain** & **Size of Host** & **Block Size or** & **Content-Based** & **Embedded Position** \\  & & **Image** & **Layers** & **Watermark** & **L**H**\({}_{2}\) \\ \hline \multirow{2}{*}{[26]} & \multirow{2}{*}{DCT} & \(256\times 256\) & \(8\times 8\) & Yes & DCT coefficients \\ \cline{3-5}  & & \(256\times 256\) & \(8\times 8\) & Yes & Low frequency coefficients in DCT \\ \hline \multirow{2}{*}{[28]} & \multirow{2}{*}{} & \(512\times 512\) & \(8\times 8\) & Yes & Middle frequency coefficients in DCT domain \\ \hline \multirow{2}{*}{[29]} & \multirow{2}{*}{} & \(512\times 512\) & \(2\) layers & Yes & Authentication watermark\(\rightarrow\)LH\({}_{2}\) \\  & & & & & Recovery watermark\(\rightarrow\)HH\({}_{1}\), HH\({}_{2}\) \\ \hline \multirow{2}{*}{[30]} & \multirow{2}{*}{DWT} & \(256\times 256\) & \(4\times 4\) & Yes & High frequency coefficients in the \\  & & & & & DWT domain \\ \hline \multirow{2}{*}{[31]} & \multirow{2}{*}{} & \(512\times 512\) & \(2\) layers & Yes & Authentication watermark\(\rightarrow\)LH\({}_{2}\), HH\({}_{2}\), Recovery \\  & & & & & watermark\(\rightarrow\)LH\({}_{1}\), HL\({}_{1}\), HH\({}_{1}\) \\ \hline \multirow{2}{*}{[32]} & \multirow{2}{*}{} & \(512\times 512\) & \(16\times 16\), & Yes & LH\({}_{2}\), HL\({}_{2}\), HH\({}_{2}\), HH\({}_{2}\) \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & & watermark\(\rightarrow\)L\({}_{1}\), HL\({}_{1}\) \\ \hline \multirow{2}{*}{[9]} & \multirow{2}{*}{ST} & \(512\times 512\) & \(8\times 8\) & Yes & Authentication watermark\(\rightarrow\)L\({}_{1}\), HL\({}_{1}\) \\  & & & & & Recovery watermark\(\rightarrow\)L\({}_{1}\)SB \\ \hline \multirow{2}{*}{[35]} & \multirow{2}{*}{DCT and IWT} & \(512\times 512\) & \(3\) layers & Yes & Authentication watermark\(\rightarrow\)LL\({}_{3}\) \\  & & & & & Recovery watermark\(\rightarrow\)LH\({}_{2}\), HL\({}_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic information of various recoverable algorithms. Compared with spatial domain algorithms, the image watermarking algorithms in the transform domain have better invisibility and robustness. Therefore, they have been widely used in many situations. In summary, the research status of semi-fragile watermarking algorithms for image content authentication is shown in Figure 3.
# Numerical Study of Geotextile-Reinforced Flexible Pavement Overlying Low-Strength Subgrade
###### Abstract  
Construction of low-volume flexible pavements on low-strength subgrade poses design, construction, and maintenance challenges. While researchers have generally acknowledged the potential for geosynthetics as reinforcement material, they mainly focused on permanent deformation. Therefore, this paper presents a numerical study of low-volume flexible pavement reinforced with geotextile material under static loading to determine the improvement due to reinforcement based on three criteria: rutting performance, geosynthetic placement location, and base course thickness reduction. Based on the Finite Element Method (FEM), three-dimensional modeling using Abaqus/CAE software was performed. From the study, a significant decrease in rutting of up to 25.2% for the unreinforced pavement system was attained with geotextile reinforcement at base-subgrade and AC-base interfaces. The deflection response behavior of the pavement system is affected by the elastic modulus of the geosynthetic material, placement location, and the number of reinforcement layers. As a result of reinforcement, a base course thickness reduction of up to 30% was achieved without sacrificing the pavement's structural integrity.  
low-strength subgrade; geosynthetics; reinforcement; ABAQUS; flexible pavement; static loading +
Footnote †: journal: Applied  
0000-0001-5999-4699-3334]X. [PERSON]; [PERSON]; [PERSON]; [PERSON].
## 1 Introduction
### Prediction Experiment of Optimal Configuration of Plant Landscape
#### 2.3.5 Data Processing
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **Sum of** & **df** & **Mean** & **F** & _p_**-Value** & \\  & **Squares** & **Square** & **Value** & **Prob > F** & \\ \hline Model & 29.24 & 14 & 2.09 & 8.38 & \textless{}0.0001 & **\\ A—Color & 0.2 & 1 & 0.2 & 8.14 & 0.0128 & * \\ B—Height & 3.98 & 1 & 3.98 & 159.69 & \textless{}0.001 & **\\ C—Roadside & 0.12 & 1 & 0.12 & 4.74 & 0.0471 & * \\ Distance & 1.03 & 1 & 1.03 & 41.44 & \textless{}0.001 & ** \\ AB & 0.12 & 1 & 0.12 & 4.78 & 0.0463 & * \\ AC & 0.012 & 1 & 0.012 & 0.49 & 0.4973 & \\ AD & 0.016 & 1 & 0.016 & 0.63 & 0.4416 & \\ BC & 0.014 & 1 & 0.014 & 0.58 & 0.4597 & \\ BD & 0.13 & 1 & 0.13 & 5.2 & 0.0387 & * \\ CD & 0.13 & 1 & 0.13 & 5.06 & 0.0411 & \\ A\({}^{2}\) & 1.1 & 1 & 1.1 & 44.31 & \textless{}0.0001 & \\ B\({}^{2}\) & 12.07 & 1 & 12.07 & 484.23 & \textless{}0.0001 & \\ C\({}^{2}\) & 2.12 & 1 & 2.12 & 84.98 & \textless{}0.0001 & \\ D\({}^{2}\) & 15.69 & 1 & 15.69 & 629.55 & \textless{}0.0001 & \\ Residual & 0.35 & 14 & 0.025 & & & \\ Lack of Fit & 0.22 & 10 & 0.022 & 0.7 & 0.7052 & not significant \\ Pure Error & 0.13 & 4 & 0.032 & & & \\ Cor Total & 29.59 & 28 & & & \\ \hline \end{tabular} Note: * \(p\) < 0.01 indicates that the difference is extremely significant: * \(p\) < 0.05 indicates significant difference.  
\end{table} 
Table 6: Results of the ANOVA of velocity mean standard deviation.
###### Abstract  
A novel intensity-modulated Sagnac loop sensor based on polarization-maintaining photonic crystal fiber (PM-PCF) in a setup with a dense wavelength division multiplexer (DWDM) for strain measurement is presented. The sensor head is made of PM-PCF spliced to single-mode fibers. The interferometer spectrum shifts in response to the longitudinal strain experienced by the PM-PCF. After passing the Sagnac loop, light is transmitted by a selected DWDM channel, resulting in a change in the output optical power due to the elongation of PM-PCF. Hence, appropriate adjustment of spectral characteristics of the DWDM channel and the PM-PCF Sagnac interferometer is required. However, the proposed setup utilizes an optical power measurement scheme, simultaneously omitting expensive and complex optical spectrum analyzers. An additional feature is the possibility of multiplexing of the PM-PCF Sagnac loop in order to create a fiber optic sensor network.  
Keywords:fiber optic sensor; Sagnac loop; intensity-modulated; DWDM; strain sensor +
Footnote †: journal: Applied
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 3 Method
### Methodology
#### 3.2.2 Extrinsic Parameters Calibration between Camera and Flash LiDAR
Knowing the intrinsic parameters of the camera in Section 3.2.1, we can obtain the rotation matrix \(\mathbf{R}_{i}\) and translation vector \(T_{i}\) from each chessboard to the camera coordinate system by Equation (5). Then, the origin and unit normal vector of chessboard \(i\) can be transformed to \(O_{\mathbb{C}}-X_{\mathbb{C}}Y_{\mathbb{C}}Z_{\mathbb{C}}\) by the following:  
\[N_{i}^{\prime}=\mathbf{R}_{i}[0,0,1]^{T}, \tag{13}\]  
\[O_{i}=T_{i}, \tag{14}\]  
where \(N_{i}^{\prime}\) represents unit normal vector of chessboard \(i\), and \(O_{i}\) denotes the origin coordinate of chessboard \(i\). The expression of plane \(i\) in \(O_{\mathbb{C}}-X_{\mathbb{C}}Y_{\mathbb{C}}Z_{\mathbb{C}}\) can be acquired:  
\[N_{i}^{\prime}{}^{T}(P^{\mathbb{C}}-O_{i})=0, \tag{15}\]  
where \(P^{\mathbb{C}}\) is an arbitrary point on plane \(i\). Similar to Equation (10), the coordinate of origin \(O_{W}{}^{\prime}=\left[X_{W}{}^{\prime},Y_{W}{}^{\prime},Z_{W}{}^{\prime} \right]^{T}\) is as follows:  
\[\left[N_{1}{}^{\prime},N_{2}{}^{\prime},N_{3}{}^{\prime}\right]^{T}O^{\prime} {}_{W}-\left[N_{1}{}^{\prime T}O_{1},N_{2}{}^{\prime T}O_{2},N_{3}{}^{\prime T}O_{3} \right]^{T}=0, \tag{16}\]Normal vectors of three axes \(N_{X}{}^{\prime}\), \(N_{Y}{}^{\prime}\), and \(N_{Z}{}^{\prime}\) can also be determined by Equation (11). Hence, the rotation matrix \(\mathbf{R}_{WC}\) and translation vector \(T_{WC}\) from \(O_{W}-X_{W}Y_{W}Z_{W}\) to \(O_{C}-X_{C}Y_{C}Z_{C}\) are as follows:  
\[\left\{ \begin{array}{l}\mathbf{R}_{WC}=\big{[}N_{X}{}^{\prime},N_{Y}{}^{ \prime},N_{Z}{}^{\prime}\big{]}\\ T_{WC}=\big{[}X_{W}{}^{\prime},Y_{W}{}^{\prime},Z_{W}{}^{\prime}\big{]}^{T}\end{array}  \right., \tag{17}\]  
Then, we can obtain rotation matrix \(\mathbf{R}_{raw}\) and translation vector \(T_{raw}\) from \(O_{L}-X_{L}Y_{L}Z_{L}\) to \(O_{C}-X_{C}Y_{C}Z_{C}\) by coordinate system transformation:  
\[\left\{ \begin{array}{l}\mathbf{R}_{raw}=\mathbf{R}_{WC}\mathbf{R}_{WL}^{-1} \\ T_{raw}=\mathbf{R}_{WC}(T_{WC}-T_{WL})\end{array} \right., \tag{18}\]  
where \(\mathbf{R}_{raw}\) and \(T_{raw}\) are the initial rigid transformation of system. However, there are errors caused by LiDAR point cloud and camera image. To reduce errors, we project point cloud of the triangular pyramid to the camera coordinate system and minimize the distance between all points and corresponding chessboards. \[\text{argmin}\sum_{i=1}^{3}\sum_{j=1}^{M(i)}\|N_{i}^{T}(\mathbf{R}P_{i,j}^{L}+ T)-d_{i}\|, \tag{19}\]  
where \(M(i)\) represents the amount of inlier points belonging to plane \(i\) and \(P_{i,j}^{L}\) denotes the \(j\)th point of plane \(i\). This non-liner optimization problem can be solved by the Levenberg Marquardt algorithm [31].
## 2 Nanostructure Materials for Arsenic Removal
### Application of Nano-Scaled Activated Carbon Modified by Iron and Manganese Oxides for As(V) Removal  
Recent developments include the successful removal of arsenic by activated carbon altered by iron and manganese oxides [39]. According to this study, a microporous activated carbon was impregnated with iron, iron/cobalt and iron/manganese mixtures by applying a novel and simple impregnation method, used for the preparation of magnetic activated carbons containing Fe\({}_{3}\)(or Mn\({}^{2+}\))O\({}_{4}\). The modified carbons were assessed for As(V) removal and showed enhanced capabilities. The best As(V) adsorption capabilities were obtained for the Fe\({}_{3}\)-xMn\({}_{x}\)O\({}_{4}\)-modified activated carbon, where Mn ions were also integrated in the magnetite lattice. The inclusion of Mn influenced the textural properties of the carbon, which was concluded because of the decrease of the surface area, as well as of surface pH and the pH\({}_{\rm{pzc}}\) values and the rise of crystallinity values. It was indicated that arsenate, considered to be a Lewis base, becomes selectively adsorbed through the creation of inner-sphere bidentate complexes on the modified carbon surface. The impregnation of activated carbon with Fe gave an increment of As(V) maximum adsorption capability (Q\({}_{\rm{max}}\)) from about 4 mg g\({}^{-1}\) (for the raw carbon) up to 11 mg g\({}^{-1}\), while the inclusion of Mn caused an additional improvement in the adsorption capacity up to 19 mg g\({}^{-1}\).  
Table 1 shows the reviewed nanomaterials when utilized for removal of As(III) and As(V) from aquatic sources and provides some more information about their structural characteristics and applications in water treatment. As shown in this Table, most of the proposed materials are efficient at pH values around 7, for the removal of both As(III) and As(V) species. It is worth noting that the Fe-Cu binary oxide material was found to be effective for removing both As(V) and As(III) species, providing an adsorption capacity of 122 mg g\({}^{-1}\) at pH 7 for As(III), while the hierarchically porous CeO\({}_{2}\)-ZrO nanospheres showed an adsorptive ability of only 9 mg g\({}^{-1}\) for the case of As(III). An increased  
adsorption capacity was achieved when graphene oxide was combined with hydrated zirconium oxide (GO-ZrO(OH)\({}_{2}\)) and the obtained adsorption capacity was found to be greatly increased (up to 95 mg g\({}^{-1}\)). The latter material presents also a substantially larger surface area (421 m\({}^{2}\) g\({}^{-1}\)), when compared with the 30 m\({}^{2}\) g\({}^{-1}\) of CeO\({}_{2}\)-ZrO\({}_{2}\) nanospheres. In addition, the Fe-Cu binary oxides material presented specific surface area 282 m\({}^{2}\) g\({}^{-1}\).  
Table 2 provides the comparison of Langmuir monolayer sorption capacities between different adsorbents, when applied for the removal of arsenic (III or V). As can be observed by comparing the relevant data of Table 2 with those in Table 1, the sorption ability of the previously described nanostructured materials (Table 1) was generally substantially higher.
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
###### Abstract  
Low-cost and high-performance materials or techniques that could synergistically remove phenanthrene (PHE) in a simple manner were highly desired. Herein, we reported an alkali-activated slag (AAS) that proved applicable in both construction and environmental protection efforts. AAS was synthesized by mixing ground granulated blast furnace slag (GGBFS) and an alkaline solution. The prepared AAS mortar achieved the highest mechanical strength when using an alkaline activator with a Na\({}_{2}\)O concentration of 8% by slag weight. Moreover, AAS exhibited excellent sorption performance towards PHE, with the highest sorption performance reaching 44.0 mg/g, which was much higher than that of GGBFS. Sorption of PHE reached equilibrium within approximately 120 h and fit well with the pseudo-second-order model. Furthermore, the primary sorption mechanisms for PHE on AAS were attributed to cation-\(\pi\) interactions, hydrogen bonding, and flocculation. The strategy of using AAS not only met the requirements for high-performance and low-cost materials but also addressed the challenging issues of developing an all-in-one treatment for PHE pollutants, which was of great significance to wastewater purification.  
sorption; phenanthrene; alkaline activator; ground granulated blast furnace slag; mechanical strength +
Footnote †: journal: Applied
## 4 Reduction of Mutual Coupling by a Neural Network Approach
The minimum mutual coupling effect obtained by resonator plane is illustrated in Figure 6 and the cross dipole antenna was investigated with and without a resonator plane, which is shown in Figure 7. Moreover, the cross dipole antenna was tested in a microwave simulator with and without a resonator plane and transmission values between each antenna element was obtained as shown in Figure 4.0 According to the transmission graph, the plane resonator decreases the mutual coupling effect by nearly 2 dBi in the 2.2-2.7 GHz band. Figure 4.1 Comparison of an artificial neural network and Finite Integration Technique (FIT) based microwave simulator. Figure 4.2 A resonator plane designed by a neural network. Figure 4.3 Cross dipole antenna (**a**) with a resonator plane and (**b**) without a resonator plane.
## 2 Structure and Mathematical Model of CTEHS
### Mathematical Model of CTEHS
#### 2.2.2 Resilience Model  
Since the resilience of the combined beam is complex, the resilience of the system is modeled by experimental measurement and polynomial fitting in this paper. The specific implementation process is as follows: first, one end of the combined beam is fastened, and the other end is pushed using an ergometer (YLK-10) to gauge the resilience data of the combined beam, then multiple measurements are taken and averaged, and finally, the resilience model of the system is obtained by polynomial fitting. The relationship between resilience \(F_{r}\) and displacement \(u(L,t)\) is expressed as follows  
\[F_{r}=k_{1}u^{3}(L,t)+k_{2}u(L,t) \tag{4}\]  
The coefficients \(k_{1}\) and \(k_{2}\) in Equation (4) are obtained by polynomial fitting the measured resilience and displacement data with MATLAB. The coefficient \(k_{1}=-54715.8\) N/m\({}^{3}\), \(k_{2}=-19.65\) N/m in Equation (4). Figure 3 shows the experiment data and fitting curve  
Figure 2: Geometric positions and relations of the magnet.  
of the displacement-resilience of the combined beam. It can be found from Figure 3 that the resilience of the combined beam is nonlinear, and the fitted curve is in good agreement with the experimentally measured data.
## 3 Result and Discussion
### Physicochemical Properties
#### 3.3.5 Odour Threshold and Relative OAV  
OAV is a ratio of the odour threshold concentration to the volatile compounds, indicating how the volatile profile of the measured sample is contributed by each compound. The greater the OAV value, the greater the overall odour. Due to the limited information on the OAV in similar studies, only the dominating odour/flavour profiles of the inoculated milk samples were compiled to Table 3. Values greater than their threshold concentrations (relative OAVs \(>\) 1) were identified as odour-active compounds. In the control sample, four carboxylic acids have shown relative OAVs of greater than 1, with butanoic acid being the dominating compound (OAV of 19.47). For aldehydes, 2-Nonenal (E) was the highest with OAV of 632, followed by nonanal, heptanal and 4-Heptenal, (Z). For ketones, 2-nonanone was the most dominant (OAV of 19.65) and for alcohols and esters, 1-octen-3-ol (OAV 45.45) and ethyl butyrate (OAV 51.60) were the dominating compounds. Overall, the odour compound of the control samples appeared to be fruity with a mushroom note.  
In contrast, the relative OAVs of volatile compounds found in milk inoculated with the thermo-tolerant strain had some similarity compared to that of the control. Butanoic acid was the dominating compound for carboxylic acid group, with similar OAV value, of 14.69. 2-nonenal (E) was again the dominating compound for aldehydes (OAV 156.25), but in much smaller value compared to that of the control. There were three dominant ketone compounds with 2-nonanone being the highest (OAV 376.31), four dominating compounds of alcohols and two esters with 2-heptanol (OAV 139.98) and ethyl butyrate (OAV 607.84) being the most significant. The overall description of the milk inoculated with the thermo-tolerant strain was more on the cheesy and oily with pineapple notes.  
Significant decreases in ethyl butyrate (by 7.7 times), heptanal (by 5.72 times), 2-nonenal (E) (by 4.05 times) and 4-heptenal (Z) (by 6.23 times) and significant increases in ethyl hexanoate (by 39.5 times) and 2-nonanone (by 19.15 times) were found in the OAVs, comparing the milk inoculated with the control to the thermo-tolerant strain. An alcohol compound, 2-heptanol, was absent in the control but found in high amount in the milk inoculated with the thermo-tolerant strain. As shown in Table 3, it was evident that these volatile compounds have contributed significantly in changing the overall odour of the inoculated milk from the control. Contribution of cheese-like flavour from the increase in FAAs, such as methionine, phenylalanine, threonine and branched chain amino acids (leucine, isoleucine and valine) [62] is also noteworthy (Table 1).
## 3 Results
### Study Characteristics  
#### 3.1.1 Methodology of the Studies  
In three studies (33.33%), experimental designs were employed wherein participants were divided into different experimental conditions [32; 37; 39]. Conversely, a cross-sectional design was utilized in six studies, wherein all participants completed all experimental conditions at a single point in time [1; 33; 34; 35; 36; 38]. The method of recruitment of participants was not specified in the majority of the reviewed studies [1; 34; 35; 36; 37; 38; 39]. The investigation undertaken by [PERSON] et al. [32] involved the participation of firefighters recruited from  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **iVR \({}^{*}\) Device** & **Brain Record** & **Main Brain Outcomes** \\ \hline [PERSON] et al., & HMD \({}^{*}\), not specified & fNIRS \({}^{*}\) (NIRSport 2) & Right dIPFC \({}^{*}\), mPFC \({}^{*}\), and left dIPFC exhibited differences between the encoding and retrieval phases. \\ \hline [PERSON] et al., 2022 [33] & Oculus Rift & EEG \({}^{*}\) (63-channel actiCHamp system) & Changes in alpha, beta, and theta oscillations in the OC \({}^{*}\) and the dIPFC during the visual stimulus presentation and test-response periods \\ \hline [PERSON] et al., & HTC Vive Pro & EEG (64-channel LiveAmp system) & CDA \({}^{*}\) increased with the memory load. \\ \hline [PERSON] and [PERSON], 2024 [35] & Hololens 2 & EEG (14-channel Emotiy EPOC +) & Highest mental stress was observed in the non-preferred space. \\ \hline \multirow{4}{*}{[PERSON] et al., 2023 [1]} & Oculus Quest 2 & \multirow{4}{*}{iEEG \({}^{*}\) (not specified)} & MTL \({}^{*}\) theta oscillations are modulated by successful memory retrieval and spatial positions within the environment. \\ \hline [PERSON] et al., & HTC Vive Focus & EEG (8-channel EEG amplifier (OpenBCI 32-bit Board Kit) & Significant variations in the theta range were observed at the Oz electrode location, particularly in the background distractor condition. Significant changes in EEG power within the beta and theta rhythms. \\ \hline [PERSON] et al., 2018 [37] & Oculus DK2 headset & EEG (Brain Products 32-channel actiCAP and BrainAmp DC amplifier) & Significant increases in fast spindle activity (12–15 Hz) in the frontal regions of the brain. \\ \hline [PERSON] et al., 2013 [38] & HMD, not specified & EEG (64-channel AtiveTwo) & Low frequency range of 2–8 Hz in the PC \({}^{*}\) was associated with spatial navigation and memory formation. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical and neurophysiological overview of the reviewed studies.  
a local fire station. Conversely, the research carried out by [PERSON] et al. [33] involved the utilization of a more expansive recruitment strategy, encompassing word-of-mouth methods and announcements disseminated through departmental email lists.
## 3 General System Design
### Module Introduction
#### 3.2.3 Rendering Module  
The quality of rendering directly impacts the realism of the simulation. This simulation system leverages the NANITE and LUMEN technologies of Unreal Engine 5, both of which deliver stunning visual effects.  
In the laboratory setting, numerous experimental instruments feature high-precision models, resulting in a vast number of polygons in the scene's mesh. Through the utilization of Nanite and virtual shadow mapping techniques, only the perceptible details are streamed and processed. This significantly alleviates the limitations on polygon counts and draw calls, ensuring a stable runtime frame rate.  
LUMEN technology provides a fully dynamic global illumination and reflection solution, allowing real-time adjustments to indirect lighting. Consequently, while manipulating experimental instruments, users can clearly observe the real-time changes in indoor lighting and shadows.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 4 Design of Applications
### Fiber Optic Oxygen Sensor Based on Phosphorescence Lifetime Measurement
#### 4.2.1 Optical Sensor Characterization  
The presence of dissolved oxygen frequently attenuates the light emission of a luminescent indicator because of the paramagnetic properties of molecular oxygen, which cause cross-system crossings and conversions of excited molecules to the triplet state, resulting in phosphorescent emission. For this chemical sensor, namely an Al-Ferron sensor with sol-gel support [52], the peak excitation is at 390 nm and the emission spectrum has a peak at 590 nm, as can be seen in Figure 17. The large separation between the two wavelengths simplifies the selection of the excitation source. On the other hand, a low level of phosphorescent emission is detected, which will also be attenuated using optical fibers, in turn informing the choice of photodetector.