esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 2 Background  
### PPG Signal and Its Application  
A PPG signal reflects the variation with time of the blood volume in the tissue of a patient [5]. The obtained signal is dependent on the mode (transmittance or reflectance) and on the wavelength of the light source used. Indeed, the light penetration depth in the tissue is different according to the wavelength used. Green light can only reach capillaries and upper arterioles under the epidermis, whereas red and infrared light are able to penetrate further into the dermis and reach deeper and larger arterioles [5]. This penetration depth has an influence on the quality of the signal under perturbation. [PERSON] et al. [12] and [PERSON] et al. [13] showed that red- and infrared-based PPG are more subject to motion artifacts than green-based PPG. The PPG signal is composed of a semi-periodic component (AC component) reflecting the modification of blood flow due to cardiac activity and a baseline (DC component) varying with low-frequency physiological changes (e.g., respiration or variations of the sympathetic and parasympathetic system of the patient). The measurement of a PPG signal allows several applications [14]:  
* Extraction of physiological parameters, which can be illustrated by the extraction or estimation of physiological variables such as pulse rate, pulse rate variability, blood oxygen saturation [15], blood pressure [16], jugular venous pulse, respiration rate, cardiac output, arterial stiffness, left ventricular ejection time [10].
* Monitoring of patients by the screening PPG signals to follow their cardiovascular state. It allows the rise of alarms in case of the detection of abnormal situations such as fibrillation in intensive care units.
* Diagnosis: for example, by the detection of cardiovascular or peripheral vascular diseases [17] or arrhythmia, such as atrial fibrillation [18] or ventricular tachycardia [19].
## 3 Results and Discussion
### Viscosity  
In addition to GPC, also the viscosities of the resins were closely examined. For coatings applications, the viscosity is an important parameter, as depending on the applications, the resins have to exhibit a certain viscosity to be applicable. If the viscosity is too high, the use of reactive diluents might be necessary. In general, the viscosity is proportional to the molecular weight. However, in the case of polyester amides, the hydrogen bonding induced by the amide groups should also lead to an increase in viscosity. For the PEAs synthesized in this study, the effect can only be seen at amide contents of more than 5%. As a result, a significant increase in viscosity can only be observed for  
Figure 3: GPC traces of all PEAs synthesized in the course of this study. (a) PEA-001-005; (b) PEA-101-103.  
PEAs exhibit viscosities of around 6.7-8.2 Pa.s for series one and 4.9-5.9 Pa.s for series two. However, the viscosity of the PEAs drops significantly with increasing temperature, with only slight differences of all PEAs at 75 \({}^{\circ}\)C.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 6 Summary  
Kriging provided the best accuracy in the group-delay scenario. The OSR was reduced by 17%, 26%, and 78% compared with Margolis, Lagrange, and Akima. Kriging was the least noise sensitive method. The presented approach to set the variogram parameters according to the desired filter response was justified by the simulation results.  
Wireless sensors send their data in intervals of seconds or minutes. Some transmissions might be lost or delayed. The slow interval allows for more demanding signal processing. However, delays caused by algorithms, which must collect several samples before a prediction for the current point of time can be calculated, are not acceptable. This leads to the demand for low-latency or single-delay resampling. The gap between the current and the previous sample should be reconstructed as soon as a new measurement arrives. Objectives include low OSR, low prediction error, and low latency. Not all of them can be achieved at the same time. In general, a higher RMSE or lower SNR must be accepted.  
With Kriging and Margolis, two methods are available for resampling in the single-delay scenario. The restriction to past samples comes at a price. The OSR must be almost doubled to achieve the same SNR (Table 2). For the case of noise-free jitter input signals, Kriging was also the best method with an OSR reduction of 7%, 39%, and 65% compared with Margolis, Lagrange, and Akima. In case of missing samples Margolis was slightly better with a 4% lower OSR. Noisy jitter signals with \(f_{L}>\) 0.18 Hz were the second case, in which Margolis was better than Kriging. In all other cases, especially for noisy signals with missing samples, Kriging provided the more accurate resampling.  
The simulations for Kriging and Margolis showed that single-delay resampling is feasible. Data from wireless sensors can be integrated with subsequent processing, e.g., by digital twins [4], even if the sensor timing is distorted by jitter or missing samples.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a16040203/s1](https://www.mdpi.com/article/10.3390/a16040203/s1)]([https://www.mdpi.com/article/10.3390/a16040203/s1](https://www.mdpi.com/article/10.3390/a16040203/s1)).  
This research was supported by the German Research Foundation (DFG) for the project 'In-network data analysis of spatially distributed quantities' under grants Pa2507/1 and Je722/1. For further information, visit www.intelligentcontainer.com.  
Simulation results including Matlab scripts for figure plotting can be downloaded at: [[https://www.mdpi.com/article/10.3390/a16040203/s1](https://www.mdpi.com/article/10.3390/a16040203/s1)]([https://www.mdpi.com/article/10.3390/a16040203/s1](https://www.mdpi.com/article/10.3390/a16040203/s1)). Python source code can be made available on request.  
The authors declare no conflict of interest.
# Hypoxia Modulates Regenerative Potential of Fetal Stem Cells
## 2 Materials and Methods  
### Human Fetal NPC and SDSC Culture  
Human fetal NPCs and SDSCs [10; 11] (ScienCell Research Laboratories, Carlsbad, CA, USA) were plated into T175 plastic flasks at 3000 cells/cm\({}^{2}\) in growth medium (\(\alpha\)-minimum essential medium (\(\alpha\)-MEM) containing 10% fetal bovine serum (FBS), 100 U/mL penicillin, 100 ug/mL streptomycin, and 0.25 ug/mL fungizone) (Thermo Fisher Scientific, Waltham, MA, USA). The cells were cultured in a 5% CO\({}_{2}\) incubator at 37 \({}^{\circ}\)C in either hypoxia (5% O\({}_{2}\), 38 mm Hg) or normoxia (21% O\({}_{2}\), 159 mm Hg). The medium was changed every other day. During expansion, cell number was measured and morphology was documented.  
Two primary designs were utilized in this experiment: (1) Pretreatment culture regimen (Figure 1A): passage 3 human fetal NPCs and SDSCs were grown for 7 days in either hypoxia or normoxia. Afterward, cells were reseeded into T25 plastic flasks for 21 days of either adipogenic or osteogenic induction (LN and NN) or centrifuged to form pellets for chondrogenic induction. The pellets from NPCs or SDSCs that were pretreated under normoxia were allowed to grow in normoxia D0-D21 as a control (N-N-N) and in hypoxia D0-D10 and transferred to normoxia D10-D21 (N-L-N). The pellets that were pretreated under hypoxic conditions were allowed to grow in normoxia D0-D21 (L-N-N) and in hypoxia D0-D21 (L-L-L); and (2) Differentiation treatment regimen (Figure 1B): passage 3 human fetal NPCs and SDSCs were expanded for 7 days in normoxia. Afterward, cells were incubated in T25 plastic flasks for 21-day adipogenic or osteogenic induction in either normoxia (NN) or hypoxia (NL). Pellets were subsequently formed and separated into three treatment groups: normoxia for D0-D21 (N-N-N), hypoxia for D0-D21 (N-L-L), and normoxia from D0-D10 and then transferred to hypoxia from D10-D21 (N-N-L).
## 3 Results
### Sensitivity Analysis  
Since the health risk assessment model involves more than 20 variables and uncertainty is inevitable during the Monte Carlo simulation, sensitivity analysis was used to determine the sensitivity factors that have important influence on health risk assessment. The Crystal Ball Software 11.1 was used for the sensitivity analysis and results were listed in Figure 7.  
Figure 7 shows that the concentration of Cr in MSW was the most sensitive parameter contributed the most to TEHI and TCRI for male and female workers. The other sensitive parameters for TEHI were IngR, Pb, EF, Cd, As and Hg, accounting for (16.3%-16.6%), (15.0%-15.1%), (12.6%-12.7%), (4.5%-4.8%), (1.7%-1.8%), and (0.2%-0.3%) of the total variance, respectively. The other sensitive parameters for TCRI were InhR, EF, and As, accounting for (7.9%-8.1%), (6.1%-6.2%), and 0.2% of the total variance, respectively.
# A Phase Bias Compensation Method for Anti-Interference Antenna Arrays in RTK Positioning
## 2 Materials and Methods
### The Bias Compensation Method for Anti-Interference Algorithm
#### 2.5.1 Bias Compensation Method Based on Array Weighted-Coefficient Conjugation
\tag{24}\]  
The Lagrange derivation is used to obtain the optimal solution, and the Lagrange expression is shown in (25). \[L(w)=\mathbf{w}^{H}\mathbf{R}_{xx}\mathbf{w}+\mathbf{v}^{H}(\mathbf{A} \mathbf{w}^{*}-\mathbf{B}\mathbf{w}-\mathbf{C}) \tag{25}\]  
Here, \(\mathbf{v}\) is an unknown constant vector and partial derivative with respect to \(\mathbf{w}\). The result is (26). \[\mathbf{R}_{xx}^{T}\mathbf{w}^{*}=\mathbf{B}^{H}\mathbf{v} \tag{26}\]  
And (26) can be expressed as (27). \[\left\{ \begin{array}{l}\mathbf{w}^{*}=\mathbf{R}_{xx}^{-1}\mathbf{B}^{H} \mathbf{v}\\ \mathbf{w}=\left(\mathbf{R}_{xx}^{-1}\right)^{*}\mathbf{B}^{T}\mathbf{v}^{*} \end{array} \right. \tag{27}\]  
Substituting (27) into (23), we can obtain (28). \[\left\{ \begin{array}{l}\mathbf{A}(\mathbf{R}_{xx}^{-1})^{*}\mathbf{B}^{T} \mathbf{v}^{*}-\mathbf{B}\mathbf{R}_{xx}^{-1}\mathbf{B}^{H}\mathbf{v}=\mathbf{ C}\\ \mathbf{A}\mathbf{R}_{xx}^{-1}\mathbf{B}^{H}\mathbf{v}-\mathbf{B}(\mathbf{R}_{ xx}^{-1})^{*}\mathbf{B}^{T}\mathbf{v}^{*}=\mathbf{C}\end{array} \right. \tag{28}\]  
Combining (28), we obtain (29). \[\left[\mathbf{A}(\mathbf{R}_{xx}^{-1})^{*}\mathbf{B}^{T}+\mathbf{B}(\mathbf{R} _{xx}^{-1})^{*}\mathbf{B}^{T}\right]\mathbf{v}^{*}=(\mathbf{A}\mathbf{R}_{xx}^ {-1}\mathbf{B}^{T}+\mathbf{B}\mathbf{R}_{xx}^{-1}\mathbf{B}^{T})\mathbf{v} \tag{29}\]  
Simplifying (29), we obtain (30). \[\left\{ \begin{array}{l}\mathbf{B}_{2}\mathbf{v}^{*}\mathbf{=B}_{1}\mathbf{v} \\ \mathbf{B}_{1}=\mathbf{A}\mathbf{R}_{xx}^{-1}\mathbf{B}^{T}\mathbf{+B}\mathbf{ R}_{xx}^{-1}\mathbf{B}^{T}\\ \mathbf{B}_{2}=\mathbf{A}(\mathbf{R}_{xx}^{-1})^{*}\mathbf{B}^{T}\mathbf{+B}( \mathbf{R}_{xx}^{-1})^{*}\mathbf{B}^{T}\end{array} \right. \tag{30}\]Substituting \(\mathbf{v}^{*}\)=\(\mathbf{B}_{2}^{-1}\mathbf{B}_{1}\mathbf{v}\) into (28) and (29), we can obtain (31). \[\left\{ \begin{array}{l}\mathbf{v}\mathbf{=}(\mathbf{B}(\mathbf{R}_{\mathbf{x} }^{-1})^{*}\mathbf{B}^{\mathbf{T}}\mathbf{-}\mathbf{AR}_{\mathbf{x}}^{-1} \mathbf{B}^{\mathbf{H}}\mathbf{B}_{2}^{-1}\mathbf{B}_{1})^{-1}\mathbf{C}\\ \mathbf{w}\mathbf{=}\mathbf{-}\mathbf{R}_{\mathbf{x}}^{-1}\mathbf{A}^{\mathbf{ H}}\mathbf{v}\end{array} \right. \tag{31}\]  
Here, \(\mathbf{w}\) is the weight coefficient.
## Results  
### Interannual Variation in NPP for Different Forest Types  
The annual average values of NPP for various forest types in the reservoir area from 1981 to 2014 are shown in Figure 3. The simulation results indicated that the interannual variation of NPP was drastic, presenting five consecutive and alternatively fluctuating curves according to the different forest types. The higher and lower values exhibited consistent variation trends. Coniferous forests accounted for the largest area in the reservoir region and were mainly composed of _Pinus massoniana_ forest, cedar forest, and Chinese fir forest, accounting for 35%-40% of the total forest area. The average NPP value was 550.29 g C m\({}^{-2}\) year\({}^{-1}\), ranging between 262.93 and 807.71 g C m\({}^{-2}\) year\({}^{-1}\), with a value difference of 544.78 g C m\({}^{-2}\) year\({}^{-1}\) between the maximum and the minimum values. Mixediferous and broadleaf forests were mainly dominated by _Pinus massoniana_, cedar, Chinese fir, _Quercus variabilis_, and oriental white oak, accounting for 15%-18% of the total forest area. The average NPP value was 504.49 g C m\({}^{-2}\) year\({}^{-1}\), varying between 307.02 and 654.63 g C m\({}^{-2}\) year\({}^{-1}\). The deciduous broadleaf forest in the reservoir region is severely disturbed by human activities and presented a stripped distribution pattern. It accounted for 11% to 14% of the total forest area. The average NPP value was 458.69 g C m\({}^{-2}\) year\({}^{-1}\), ranging between 351.12 and 531.62 g C m\({}^{-2}\) year\({}^{-1}\). The evergreen broadleaf forest represented the zonal vegetation in the reservoir area, but excessive exploitation had led to serious disturbance and destruction of the original vegetation. The area with natural forest was extremely limited and mainly consists of secondary forests, accounting for only about 5% of the total forest area. The average NPP value was 456.40 g C m\({}^{-2}\) year\({}^{-1}\), ranging between 248.69 and 607.67 g C m\({}^{-2}\) year\({}^{-1}\). The shrub forest is dominated by _Cotinus coggygrai_, _Rhus javanica L._, _Coriaria nepalensis_ and _Lespedeza bicolor_, and its area accounted for about 30% of the total forest area [46]. The average NPP value was 274.99 g C m\({}^{-2}\) year\({}^{-1}\), ranging between 160.66 and 484.07 g C m\({}^{-2}\) year\({}^{-1}\).
# Modified 16-Quasi Log Periodic Antenna Array for Microwave Imaging of Breast Cancer Detection
## 2 Antenna Design  
The modified PLPA antenna prototype was designed and fabricated on FR4 substrate with permittivity 4.4, thickness 1.6 mm, and loss tangent 0.02. The prototype was planar and compact with the dimension of 50 \(\times\) 40 mm\({}^{2}\). The antenna structure is shown in Figure 1. The patch and FR4 substrate are represented by the orange and peach color, respectively. The substrate material was preferred to be FR4 as it is inexpensive, easily available, simplicity fabrication, and design resilient. The design patch was fed via 50 \(\Omega\) CPW feedline by using SMA connector. The CPW feedline is best suited for obtaining wider bandwidth, better matching, ease of integration, and lower dispersion. The proposed antenna covers the frequency band from 2-5 GHz. The antenna was made up of a simplified single printed layer fed by CPW, which means that all the dipoles and feedline are printed on a single side of the substrate. The 50 \(\Omega\) feedline was connected to a single strip transmission line, along which the five dipole elements are arrayed on the top layer of the FR 4 substrate.  
The PLPA structural dimensions were optimized as: L = 50 mm, W = 40 mm, w\({}_{\text{f}}\) = 3 mm, w\({}_{\text{g}}\) = 18 mm, l\({}_{\text{g}}\) = 10 mm, l\({}_{\text{l}}\) = 3 mm, l\({}_{\text{l}}\) = 25 mm, l\({}_{\text{l}}\) = 2.5 mm, l\({}_{\text{l}}\) = 2.5 mm, l\({}_{\text{l}}\) = 3.5 mm, l\({}_{\text{l}}\) = 1.5 mm, w\({}_{\text{l}}\) = 9.5 mm, w\({}_{\text{l}}\) = 6.5 mm, w\({}_{\text{g}}\) = 6 mm, and w\({}_{\text{g}}\) = 2.5 mm. The main purpose of this prototype was to act as sensor in an array form in MIS for breast cancer detection. The larger bandwidth, better gain, and stable directional radiation patterns are necessary for the MIS.
## 3 Experimental Results and Theoretical Analysis
### Acoustic Emission Parameters  
#### 3.3.1 Acoustic Emission Counts  
Figure 7 illustrates the variations in the acoustic emission (AE) counts, cumulative acoustic emission (AE) counts and axial stress for sandstone specimens containing en echelon flaws versus time. From Figure 7, in the uniaxial compression tests, the changes in the AE counts, cumulative AE counts  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Point 1**} & \multicolumn{3}{c}{**Point 2**} \\ \cline{2-7}  & **W (J)** & **\(W_{d}\) (J)** & **\(\eta_{d}\) (\%)** & **\(W\) (J)** & **\(W_{d}\) (J)** & **\(\eta_{d}\) (\%)** \\ \hline
**B-30** & 25.79 & 1.96 & 7.60 & 30.34 & 5.43 & 17.90 \\
**B-45** & 23.29 & 1.16 & 4.98 & 26.79 & 3.02 & 11.27 \\
**B-60** & 17.83 & 2.02 & 11.33 & 21.71 & 2.39 & 11.01 \\ \hline \end{tabular}
\end{table}
Table 2: The corresponding total work and dissipated energy of the stress drop points (units: J; %).  
Figure 6: Energy dissipation characteristic curves of specimens containing en echelon flaws: (a) B-30; (b) B-45; (c) B-60.  
and axial stress of selected samples with different flaw inclination angles are seen to exhibit similar trends. It is worth noting that the corresponding AE counts and cumulative AE counts of stress drop point 1 are significantly increased in Figure 7. Therefore, we divide the process of acoustic emission energy release into two stages, and the time at which stress drop point 1 occurred is seen as the turning point in the process of acoustic emission energy release.  
(1) The first stage: According to the photographic monitoring results, no new cracks of the ends of pre-existing fissures were generated at this stage. At the same time, the AE count was smaller, the cumulative AE count increased slowly, and its curve was approximately a straight line.  
(2) The second stage: According to the photographic monitoring results, a new crack appeared at this stage; furthermore, with increasing axial stress, the crack continued to extend until the specimen became unstable. Before the final failure of the specimen, the AE count or cumulative AE count could be seen to increase significantly many times, which was regarded as an early warning signal for the final collapse of the specimen.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method
### Block-Based Watermark Embedding Scheme  
This section details the watermark-embedding process carried out at the block level, integrating the perceptual, structural, and temporal considerations outlined in Section 3.1. The method operates on selected \(8\times 8\) blocks from keyframes, applying a perceptually adaptive quantization strategy to embed watermark bits into specific DCT coefficients in a robust yet imperceptible manner. The watermark-embedding process consists of the following steps:  
1. Each keyframe is divided into a non-overlapping \(8\times 8\) block.
2. Compute the perceptual score map \(P_{i,j}\) and select robust blocks \(B_{sel}\).
3. For each selected block \(B_{i,j}\), compute the DCT coefficient \(C(2,1)\) directly from the spatial domain using Equation (4).
4. Estimate the JND threshold \(J(i,j)\) for the block \(B_{i,j}\).
5. Compute the quantization step as \(\Delta\ =Q\cdot J(i,j)\), where \(Q\) is a fixed scaling factor.
6. Modify \(C(2,1)\) using QIM, i.e., applying Equation (6) with \(u=2,\ v=1\).
7. Replace the original coefficient \(C(2,1)\) with its modified version \(C(2,1)\).  
This embedding process is applied to each bit across multiple keyframes as determined by the temporal redundancy scheme described in Section 3.1.5. In this way, the method achieves robustness not only at the spatial level but also across the temporal dimension, achieving robustness for attacks such as frame loss, swapping, and transcoding.
# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 1 Introduction
Moreover, the entire dehazing framework was enhanced with the utilization of a new activation function in both the generator and discriminator modules. Thorough experimentation was carried out to illustrate the superiority of the proposed model over other image dehazing methods on various image dehazing datasets. * Image enhancement was beneficial to various image processing tasks. One practical application was the detection of objects in degraded images. To the best of our knowledge, our research is the first to propose a new cGAN-based image dehazing method for the enhancement of images for object detection. For this investigation, we created a dataset of road scene videos. Five target objects (pedestrian, bicycle, car, bus and truck) were annotated. Two common weather conditions (rain and fog) were adopted for the synthesis of degraded images. * We adopted the object detector You Only Look Once (YOLO). Experimentation was performed on clear images, images degraded by rain, images degraded by fog and images enhanced using our proposed image dehazing method. The numeric results showed that our proposed image dehazing method can lead to target detection with higher accuracy. The visual results also illustrated that, with the use of images dehazed using our method, the object detector was able to detect objects which may have been missed in images processed using other image dehazing methods. Our paper is organized as follows. The related research on image dehazing and object detection are reviewed in the Section 2. We explain the proposed image dehazing model in detail in Section 3. Section 4 presents the experimental set up of detecting target objects in road scene images. Experimental results of image dehazing and object detection are illustrated in Section 5. We compare our proposed image dehazing model with other methods first on various image dehazing datasets. We then illustrate the performance of object detection using degraded images and images enhanced using our proposed dehazing method. In Section 6, we draw conclusions and present some future work.