esa-sceva/satcom-chunk-collection huggingface dataset:
# Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging
## 2 Theory  
### Hyperspectral Image  
The fundamentals of spectral imaging are based primarily on the interaction of light with matter [28]. An imaging spectrometer is used on hyperspectral images for imaging in narrow spectral intervals to obtain hyperspectral images in the visible, near-infrared, mid-infrared, and far-infrared bands. Hyperspectral images contain the spatial and spectrum information of the observation scene and consequently the \"pattern integration\" characteristic. In addition to using the spatial information of the image to determine the location of the area to be detected, it can also use the spectral characteristics of the substances with different chemical compositions [29]. In the application of HSI, the primary wavelength range is visible light to far-infrared light (0.38 \(\upmu\)m to 14 \(\upmu\)m) and can be divided into two types based on the radiation sources, namely the reflected radiation area (reflected radiation) and emissive radiation area (emissive radiation) [30]. The wavelength range is between 0.38 and 2.5 \(\upmu\)m in the reflected radiation, as the main source of radiation is from solar radiation [31; 32]. The relationship between the reflectivity (\(\rho\)) and emissivity (\(\epsilon\)) of the substance is shown in Equation (1) [33]:  
\[\rho+\epsilon=1 \tag{1}\]  
When a molecule receives solar radiation, it will reflect a part of the solar radiation based on its reflectively. These reflected radiations can be measured by a spectrometer with a wavelength range of 0.38 \(\upmu\)m to 2.5 \(\upmu\)m, while the emissivity can be measured by a spectrometer with a wavelength of 8 \(\upmu\)m to 14 \(\upmu\)m. However, in practical applications, the effects of diffuse reflection, scattering, and atmospheric absorption must be considered (see Supplementary Materials for a detailed description of the atmospheric absorption principle). The scattering and absorption effects can be combined into an extinction effect, which can be expressed by the Beer-Lambert law, as shown in Equation (2) [34]:  
\[T=\frac{I\times e^{-(S+K)L}}{I_{0}} \tag{2}\]  
where \(I_{0}\) is the incident light intensity, \(I\) is the light intensity at the required distance, \(S\) is the scattering coefficient, \(K\) is the absorption coefficient, and \(S+K\) is the extinction coefficient (\(E\)).
## 3 Results and Discussion
### Effect of NaSt on the Hydration Products and Pore Structure of AASRSS
#### 3.3.3 MIP  
The results of the MIP tests are shown in Figure 8. As shown in Figure 8a, the AASRSS with 0.5 wt.% NaSt added showed a comparable level of total cumulative pore volume to the control group, and the incorporation of 1.5 wt.% NaSt resulted in a larger porosity of the AASRSS. It is thus plausible that the combination of the increased porosity and reduced hydration products mainly accounted for the decrease in the compressive strength of the AASRSS due to the NaSt.  
Figure 7: FTIR results of AASRSS samples at the age of 28 days.  
In addition, from Figure 8b, it can be seen that the threshold pore size of AASRSS with the incorporation of 1.5 wt.% NaSt moves towards a higher value, and the distribution of pore size in the range of the mesopore scale was changed. The details of the representative pore size distribution percentages are shown in Table 3, from which it is clear that the pore size of AASRSS was coarsened by NaSt in the mesopore region. This is beneficial to a reduction in capillary suction, which is one main driving force for water sorptivity according to the Young-Laplace equation.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Volume Fraction (\%)**} \\ \cline{2-5}  & **<9.1 nm** & **9.1–21.1 nm** & **21.1–95.3 nm** & **>95.3 nm** \\ \hline AASRSS/Control & 88.7 & 5.6 & 1.1 & 4.6 \\ AASRSS/0.5\% & 82.3 & 6.1 & 1 & 10.6 \\ AASRSS/1.5\% & 75.9 & 13.8 & 2.3 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pore distribution of AASRSS pastes with/without NaSt at 28 days.  
Figure 8: Pore structure characteristics of AASRSS samples at the age of 28 days.
## 2 Background and Challenges
### GPS-Based Localization  
As the earliest and most representative GNSS, GPS has been in operation for more than 20 years, and it has been continuously optimized at both higher and lower levels since then. In order to improve energy efficiency, both EnTracked [18] and RAPS [19] add adaptivity by leveraging the estimation/prediction from historical GPS readings. Others (e.g., A-Loc [20]) also suggest fusing other sensing information to assist GPS so as to reduce its energy consumption. Thanks to these proposals and related engineering efforts such as A-GPS [21], the latest GPS modules in our smartphones have become much more energy efficient than those studied a decade ago [22], especially when only GNSS measurements are retrieved, as we will show in Section 5.5 using real-life data.  
Optimization at the lower level focuses on signal processing and computation. In particular, signal sparsity has been exploited in both [2; 23] to speed up computation and thus save energy. To improve localization accuracy with low-cost receivers, collaboration computation has been introduced by [24] for a scalable network of single-frequency receivers. Further computation reduction can be achieved by cloud offloading [25]; the GPS receiver collects only a small number of raw GPS readings while moving the location estimation to the cloud. In fact, the raw GPS readings contain a fairly large amount of information; simply summarizing them into three-axis location indicators using model-based methods almost surely causes information loss. Our attempt to process these raw data using machine learning schemes is the first step towards their more holistic utilization.
## 5 Test Experiments
### Test of the Installation Eccentricity Error of Test Rods  
Experiments were carried out according to the detection process mentioned in Section 4.1, and the same test was repeated three times. When the transit axis rotated 360\({}^{\circ}\) at equal intervals of 30\({}^{\circ}\), the central axis offset of A and B along the X direction and Y direction can be measured by the telecentric measurement system, as shown in Figure 11.  
By analyzing the measurement data shown in Figure 11, the installation eccentric angle (\(\theta_{1}\)) of test rod A was 30\({}^{\circ}\), and the installation eccentric angle (\(\theta_{2}\)) of test rod B was 60\({}^{\circ}\).  
Figure 12 shows the trajectory of the central axis offset of test rod A and B. The average values of the central axis offset of test rod A and B are shown in Table 1.  
According to Equation (6), the installation eccentricity error of test rod A was found to be 0.015 mm, and the installation eccentricity error of test rod B was 0.013 mm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Elevation angles/deg**} & \multicolumn{2}{c}{**Test Rod A**} & \multicolumn{2}{c}{**Test Rod B**} \\ \cline{3-6}  & & **X/mm** & **Y/mm** & **X/mm** & **Y/mm** \\ \hline
1 & 0 & 0.000 & 0.000 & 0.000 & 0.000 \\
2 & 30 & \(-\)0.001 & 0.010 & 0.005 & \(-\)0.001 \\
3 & 60 & \(-\)0.005 & 0.006 & 0.006 & \(-\)0.003 \\
4 & 90 & \(-\)0.014 & 0.020 & 0.005 & \(-\)0.009 \\
5 & 120 & \(-\)0.022 & 0.016 & 0.002 & \(-\)0.015 \\
6 & 150 & \(-\)0.027 & 0.001 & \(-\)0.001 & \(-\)0.019 \\
7 & 180 & \(-\)0.020 & \(-\)0.009 & \(-\)0.011 & \(-\)0.020 \\
8 & 210 & \(-\)0.006 & \(-\)0.008 & \(-\)0.017 & \(-\)0.013 \\
9 & 240 & \(-\)0.003 & \(-\)0.006 & \(-\)0.016 & 0.002 \\
10 & 270 & \(-\)0.002 & \(-\)0.004 & \(-\)0.007 & 0.006 \\
11 & 300 & \(-\)0.001 & \(-\)0.003 & \(-\)0.004 & 0.007 \\
12 & 360 & 0.000 & \(-\)0.003 & \(-\)0.002 & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average values of the central axis offset of test rod A and B.  
Figure 11: The central axis offset of test rod A and B at different elevation angles.
## 3 Results
### Comparative Analysis of PSG Indices in Patients with OSAS with and without SB Episodes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **With SB Episodes** & **Without SB Episodes** & \(p\) \\  & **(\(u\) = 10)** & **(\(u\) = 90)** & \\ \hline TST (min) & 363.2 (72.7) & 331.6 (56.0) & 0.03 \\ SE (\%) & 86.2 (14.1) & 81.5 (14.7) & 0.14 \\ SL (min) & 3.5 (2.3) & 64.4 (6.5) & 0.25 \\ Stage N1 sleep (\% of TST) & 24.2 (13.3) & 34.3 (6.5) & 0.06 \\ Stage N2 sleep (\% of TST) & 50.0 (10.6) & 41.4 (11.5) & 0.03 \\ Stage N3 sleep (\% of TST) & 9.3 (7.0) & 7.7 (7.7) & 0.33 \\ Stage REM sleep (\% of TST) & 16.5 (6.7) & 16.6 (6.9) & 0.96 \\ ST in the nonspine position (min) & 46.9 (71.5) & 104.6 (86.9) & 0.04 \\ ST in the supine position (min) & 316.4 (92.8) & 227.2 (97.0) & 0.01 \\ AI (events/h) & 6.9 (9.3) & 21.0 (26.3) & 0.13 \\ AHI (events/h) & 25.5 (16.6) & 45.1 (28.2) & 0.03 \\ AHI of NREM sleep (events/h) & 23.4 (18.1) & 44.7 (30.2) & 0.02 \\ AHI of REM sleep (events/h) & 34.6 (19.8) & 45.2 (25.4) & 0.25 \\ AHI in the nonspine & 7.6 (17.2) & 23.1 (30.3) & 0.02 \\ AHI in the supine position & 28.3 (17.8) & 56.0 (30.7) & 0.01 \\ (events/h) & 94.8 (2.0) & 91.6 (5.1) & 0.03 \\ Lowest oxygen saturation (\%) & 83.8 (8.5) & 80.4 (9.6) & 0.26 \\ Mean oxygen desaturation (\%) & 14.5 (8.5) & 17.6 (9.5) & 0.28 \\ Sound intensity of snoring (dB) & 59.9 (9.1) & 60.3 (14.0) & 0.69 \\ \hline \hline \end{tabular} Results are presented as the mean value (standard deviation). OSAS, obstructive sleep apnea syndrome; SB, sleep bruxism; TST, total sleep time; SE, sleep efficiency; SL, sleep onset latency; NREM, nonrapid-eye movement; ST, sleep time; AI, apnea index; AHI, apnea–hypopnea index; REM, rapid eye movement.  
\end{table} 
Table 2: Univariate statistical analysis of polysomnographic indices in 100 patients with OSAS with and without SB episodes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Coefficient (\(\beta\))** & **SE** & **f** & **\(p\)** \\ \hline Stage N2 sleep (\% of TST) & 0.003 & 0.003 & 1.080 & 0.28 \\ ST in the supine position (min) & 0.001 & 0.001 & 2.871 & 0.01 \\ AHI (events/h) & -0.001 & 0.002 & -0.471 & 0.64 \\ Mean oxygen saturation (\%) & 0.006 & 0.010 & 0.574 & 0.57 \\ \hline \hline \end{tabular} R\({}^{2}\) = 0.137; adjusted R\({}^{2}\) = 0.101; SE, standard error; TST, total sleep time; ST, sleep time; AHI, apnea–hypopnea index.  
\end{table} 
Table 3: Multiple linear regression analysis of significant factors in univariate analysis. Figure 2: Receiver operating characteristic curve (ROC) suggesting the optimal sleep time in the supine position as a cut-off point in minutes for indicating its suitability to recognize SB episodes.
Sensors_**2008**, \(8\), 4529-4559; DOI: 10.3390/s8074529  
_Article_  
**An Energy-Efficient and High-Quality Video Transmission Architecture in Wireless Video-Based Sensor Networks**  
**[PERSON], [PERSON]*, [PERSON] and [PERSON]**  
Computer Engineering Department, Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G. C., Evin, Tehran, Iran; E-mails: [PERSON]ADDRESS], [PERSON]EMAIL_ADDRESS], [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; Email: [EMAIL_ADDRESS]  
_Received: 15 March 2008; in revised form: 9 June 2008 / Accepted: 9 June 2008 / Published: 4 August 2008_  
**Abstract:** Technological progress in the fields of Micro Electro-Mechanical Systems (MEMS) and wireless communications and also the availability of CMOS cameras, microphones and small-scale array sensors, which may ubiquitously capture multimedia content from the field, have fostered the development of low-cost limited resources Wireless Video-based Sensor Networks (WVSN). With regards to the constraints of video-based sensor nodes and wireless sensor networks, a supporting video stream is not easy to implement with the present sensor network protocols. In this paper, a thorough architecture is presented for video transmission over WVSN called Energy-efficient and high-Quality Video transmission Architecture (EQV-Architecture). This architecture influences three layers of communication protocol stack and considers wireless video sensor nodes constraints like limited process and energy resources while video quality is preserved in the receiver side. Application, transport, and network layers are the layers in which the compression protocol, transport protocol, and routing protocol are proposed respectively, also a dropping scheme is presented in network layer. Simulation results over various environments with dissimilar conditions revealed the effectiveness of the architecture in improving the lifetime of the network as well as preserving the video quality.  
**Keywords:** Wireless Multimedia Sensor Network, Video Sensor Nodes, Communication Protocol Stack, Energy Efficiency, Video Quality.
## 5 Improvement of ZPC Tracking Performance
### Validate the ZPC's Interference-Free Property.  
An experiment is proposed to demonstrate the ZPC's interference-free property, which improves the tracking accuracy. There are three configurations in the experiments. (1) The received signal is a conventional NR signal containing both reference signals and unknown communication data. The local replica signal has a CP and its integration duration is \(T_{\text{sym}}\). (2) The input signal is the same as (1). Implementing ZPC utilizes a zero-padded local replica signal with a \(T_{u}\) integration duration. (3) The received signal only contains the reference signal without communication data, and the receiver uses ZPC. The tracking accuracy of DLL with different configurations is plotted in Figure 6. Comparative results reveal two critical findings: First, the ZPC configuration (red curve) achieves significantly higher tracking accuracy than conventional CP-based processing with duration \(T_{\text{sym}}\) (blue curve). Because the CP introduces interference to the subcarriers' orthogonality when the integrated duration is \(T_{\text{sym}}\), the subcarrier maintains orthogonality when the ZPC is adopted (the integrated duration is \(T_{u}\)). ZPC significantly eliminates interference caused by the CP and inter-carriers, thereby improving tracking accuracy. Second, configurations (2) and (3) have identical DLL standard deviation (STD) curves, which confirms their same tracking performance. This indicates that the NR communication signal in other carriers has no interference on the DLL when using ZPC. The reason is that ZPC maintains subcarrier orthogonality through optimized integration window selection and zero-padding CP. Overall, ZPC ensures the orthogonality and serves as an interference-free correlation mechanism, effectively eliminating the influence of the CP and inter-carriers while significantly enhancing tracking accuracy.
## 3 Acceptability Measurement of an IVIFPR
### Multiplicative Consistency Index of an IVIFPR
If \(\varphi=1\), only \(HD\left(\widetilde{R}\right)\) is considered, indicating that DM is optimistic; If \(\varphi=0\), only \(CD\left(\widetilde{R}\right)\) is considered, showing that DM is pessimistic; If \(\varphi=0.5\), decision maker pays no attention to the risk, denoting that DM is risk neutral. As parameter \(\varphi\) increases, DM is more optimistic. The parameter \(\varphi\) acts a tradeoff part between \(HD\left(\widetilde{R}\right)\) and \(CD\left(\widetilde{R}\right)\). Therefore, the MCI of an IVIFPR in Definition 8 is more flexible and appropriate for all kinds of DMs with various risk attitudes._The MCI shows the reliability degree of the original preference information given by DMs, that is to say, as the value of \(\ MICI\left(\widetilde{R}\right)\) decreases, the original information in IVIFPR \(\widetilde{R}\) is more reliable and consistent. When \(\ MICI\left(\widetilde{R}\right)=0\), then IVIFPR \(\widetilde{R}\) is absolutely multiplicative consistent. **Definition 9**: _Assume that \(\ MICI_{0}\left(0\leq MCI_{0}\leq 1\right)\) is a predefined consistent threshold. When \(\ MICI\left(\widetilde{R}\right)\leq\ MICI_{0}\), the IVIFPR \(\widetilde{R}\) is an acceptable multiplicative consistent. Otherwise, IVIFPR \(\widetilde{R}\) is an unacceptable multiplicative consistent._  
_In some specified circumstance, the consistency threshold \(\ MCI_{0}\) is predefined by the practical decision-making problems. However, it is hard for a DM to provide an absolutely or acceptably consistent IVIFPR. If the multiplicative consistency degree of an original IVIFPR is inferior, one cannot guarantee that the original IVIFPR is rationality. Therefore, it is necessary to obtain an acceptable multiplicative consistent IVIFPR with satisfying \(\ MCI\left(\widetilde{R}\right)\leq\ MCI_{0}\). In this situation, an optimization model is established to solve this problem._
## 3 Results and Discussion
### Quantitative Evaluation and Comparison  
#### 3.2.1 Comparison of Two Initial Sparse Seed Point Extraction Methods  
First, we compared two initial sparse seed points extraction methods. The sparse seed points for _volleyball_ were extracted using SFM (based on SIFT) and our proposed method based on DAISY and are shown in Figure 7. The first row provides the entire view and the second row gives enlarged details of the corresponding red rectangles in the first row. From the first row, one can see that the errors in seed points when using the proposed method were much fewer. From the second row, one can see that the seed points extracted using proposed method were much denser (the number of sparse seed points extracted by the SFM method was 19,058, compared to 104,688 by the proposed method). In short, the initial sparse seed points produced by the proposed method were much denser, yet with much fewer error seed points. As a result, the reconstructed dense 3D points became more complete, which can easily be seen in Figure 8.  
The quantitative evaluation results for accuracy and completeness of reconstructed dense 3D points of _volleyball_ are listed in Table 3. As suggested by the DTU benchmarks, when evaluating accuracy, for each point \(p\) of reconstructed dense points, the nearest point is found in ground truth and its distance to the point \(p\) is calculated and marked as \(d_{i}\). The average of all \(d_{i}\) is called mean accuracy. When all \(d_{i}\) is sorted, the middle \(d_{i}\) is called median accuracy. When evaluating completeness, for each point \(g\) of ground truth, a nearest point is found in reconstructed dense points and its distance to point \(g\) is calculated and marked as \(D_{i}\). The average of all the \(D_{i}\) is called mean complete. When all \(D_{i}\) is sorted, the middle \(D_{i}\) is called median complete. Since an incomplete ground truth would lead to a biased evaluation, we manually cut out the same volumes from the ground truth and all dense reconstructed points where the ground truth was complete (the regions in the red rectangles in the first row of Figure 8). From Table 3, it can be seen that the mean accuracy/mean complete, 0.28/1.00 mm, of the dense 3D points expanded from sparse seed points extracted by the proposed method were significantly better than the 0.36/1.12 mm achieved by the SFM method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Image Number** & **Image Size** & \(\boldsymbol{\mathrm{\updelta}}\) \\ \hline _dino_ & 48 & 640 \(\times\) 480 & 0.006 \\ _temple_ & 47 & 640 \(\times\) 480 & 0.003 \\ _volleyball_ & 49 & 1600 \(\times\) 1200 & 0.01 \\ _house model_ & 49 & 1600 \(\times\) 1200 & 0.0085 \\ _science hall_ & 65 & 1152 \(\times\) 864 & 0.06 \\ _lion_ & 42 & 1152 \(\times\) 864 & 0.009 \\ _quadrupol_ & 45 & 1152 \(\times\) 864 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of datasets and expansion step \(s\).
## 3 Results and Discussion
### Analysis of the Differences in the Content of VOCs in 'hongjiang' Obtained by Different Factors  
To visualize and accurately analyze the differences between location, variety, and cultivation modes, the differences in VOCs in 'hongjiang' obtained for different factors were analyzed by VIP analysis and heatmap analysis in OPLS-DA. Fifty-one differential VOCs with VIP \(>\) 1 in different factors were obtained in OPLS-DA and were used to make a heatmap in Figure 4 and Table S2. As shown in Figure 4, the content of VOCs in 'hongjiang', obtained under different locations and methods, was analyzed. For example, \(\alpha\)-terpinene (13) was detected only in PRHN. In addition, 1,5-diethenyl-2,3-dimethylCyclohexane (97), Eremophila-1(10),8,11-triene (73), Labdadienediial (90) were detected only in PRNN. Geranylgeranyl acetate (101), and 8-Cedren-13-ol (75) were detected in MRNN only. Cryptone (37) and 7-methyloct-3-yne (19) were detected in PROY only, 2,4-dodecadienal (42) was detected in PQOY only, and octanoic acid (35) was detected in PCON only.  
1-Hexanol (5), octanal (11), 1-nonanal (22), ethyl octanoate (39), 2-methyl-5-(1-methyletenyl) -cyclohexanone (40), decanal (41), \(\beta\)-citronellol (45), geraniol (48), farnesene (72), and \(\delta\)-cadinene (83) were detected in 21 groups of samples. Methyl geranate (54) was absent in PRHN, PRGN, and PRNN (half-net room, little-net room, and big-net room cultivation modes) and present in PROY, PQOY, and PCON (open-air cultivation modes).
## 4 Analysis of the CRB for Short Observation Intervals  
The matrix \(\mathbf{J}^{-1}\left(\mathbf{\Psi}\right)\) derived in the previous section shows that \(\text{CRB}(\mathbf{\Psi})\) in the specular reflection scenario considered will depend on:  
* The scenario geometry: through the propagation path difference between the two signal components \(\Delta\rho\), which will be a function of \(h\) and the satellite elevation angle \(\theta_{el}\) (and its variation) during the observation time.
* The properties of the reflecting surface: through its reflection coefficient, \(\Gamma\), that can be modeled as a function of the signal's incident angle and the electrical properties of the surface and its roughness.
* The GNSS signal considered and the receiver features: the signal modulation and its transmission bandwidth, the C/N\({}_{0}\) of the LOS signal, the antenna's radiation pattern and the receiver front-end's bandwidth (and the filtering scheme considered) will all have an impact on the resulting CRB.  
However, understanding these dependencies only by examining \(\mathbf{J}^{-1}\left(\mathbf{\Psi}\right)\) is not straightforward. The expressions for their diagonal elements can be, in the general case, quite cumbersome with several terms. Nonetheless, we examine the specific case when \(\Delta\rho>\rho_{chip}\), where the initial FIM \(\mathbf{J}(\mathbf{\xi})\) is a diagonal matrix, which greatly simplifies the terms in the final \(\mathbf{J}^{-1}\left(\mathbf{\Psi}\right)\).