esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
Hence, popularity skewness has a significant role in driving users' data viewing patterns and our proposed caching mechanism efficiently caches contents and improves content access via cache resources. Figure 5 shows performance gain of our proposed caching mechanism under the influence of increasing number users. The efficiency of the proposed caching mechanism is clear in this result because increasing number of users implies more diverse data demands. However, all contents cannot be cached due to the cache constraint; therefore, optimal cache decisions are necessary. For instance, when there are 30 users, our proposed caching mechanism achieves 3.5%, 5.5%, 7.2%, and 12% lower delay than LB&B, LFUCC, HPCC, and RCC, respectively. This shows that our proposed caching mechanism can intelligently decide which contents should be cached based on evolving user data requests and positions. In Figure 6, we analyzed the impact of the cache capacity on the data access delay. It is obvious, when cache capacity is large, then more contents can be cached. This enables more data access through the cache resources. When cache capacity is 60, our proposed caching mechanism achieves 2.6%, 3.3%, 7%, and 8.2% lower delay than LB&B, LFUCC, HPCC, and RCC, respectively. From the obtained results, it is clear that our scheme efficiently utilized cache resources, even in the case of low cache capacity, to accurately predict users' data demands and perform caching accordingly. Figure 3: Impact of number of contents on the network delay when \(N=50\) and \(\gamma=1\). through cache resources. For \(\gamma=2\), our proposed mechanism performs 18%, 47.5%, 90%, and 96% more data offloading through cache resources than LB&B, LFUCC, HPCC, and RCC respectively. Hence, our proposed mechanism has optimized data placement and efficiently utilized limited cache resources. In Figure 8, an improvement in backhaul saving in result of increasing cache capacity is analyzed. It is clear from the obtained results that when cache capacity is large more data requests are fulfilled via cache resource and backhaul resources are conserved. For instance, when cache capacity is 45, our proposed mechanism achieves 6%, 35%, 64%, and 74% high backhaul saving than LB&B, LFUCC, HPCC, and RCC, respectively. These obtained results demonstrate our proposed mechanism can accurately place contents in the cache resource by observing users' data demands.
## 5 Experimental Results
### Experiment Setting  
To verify the practicality of the mathematical model designed in this paper, the optimized NSGA-III, NSGA-III, and NSGA-II were applied to realize the cooperative scheduling of Yangtze River ports and berths. These algorithms were run using MATLAB R2022b on a 64-bit Windows operating system.  
As compared with the other two objectives, this paper pays more attention to the berth deviation cost; the algorithm is set up to take the ship deviation cost as the optimization objective first, and the algorithm convergence process is shown in Figure 3 after 100 iterations. As in the early stage of the algorithm, most of the solutions in the population give a larger gap between the actual berth of the ship and its preferred berth, affected by the constraint; the value of the target one is larger. However, in about 20 generations, the cost of berth preference began to gradually decline, in about 55 generations there was a small jump out of the local optimum, in 80 generations it was stabilized in the range of feasible solutions, and it gradually entered convergence and ultimately stabilized at about 2620, so this paper adopts 100 for the number of iterations. Other experiments were conducted using a population size of 50, \(P_{c_{min}}=0.1\), \(P_{c_{max}}=0.9\), \(P_{m_{min}}=0.01\), and \(P_{m_{max}}=0.1\).
## 2 Point-Spread Functions in Phase Space
### Fresnel Approximation  
Within the Fresnel approximation each spherical wavefront is approximated as a parabola  
\[P_{\text{Fr}}(r):=\frac{1}{i\lambda z}e^{i\varphi_{\text{Fr}}(r)}\quad\text{ with}\quad\varphi_{\text{Fr}}(r):=-\frac{2\pi}{\lambda}\left(z+\frac{r^{2}-z^{2}}{2 z}\right)\,. \tag{6}\]  
Instantaneous phase and instantaneous frequencies are given by  
\[f_{\xi}(r):=\frac{1}{2\pi}\frac{\partial\varphi_{\text{Fr}}(r)}{\partial\xi} =-\frac{(\xi-x)}{\lambda z}\quad\text{and}\quad f_{\eta}(r):=\frac{1}{2\pi} \frac{\partial\varphi_{\text{Fr}}(r)}{\partial\eta}=-\frac{(\eta-y)}{\lambda z }\,. \tag{7}\]  
As a result that a quadratic instantaneous phase yields a linear instantaneous frequency, the latter can grow unbound in the Fresnel approximation whenever \(|\xi-x|\gtrsim z\). In Figure 3a we can observe nicely how the spectrum of a parabolic wavefront extends across the entire Fourier domain for any \(|z|<\infty\).  
Note, how the Fresnel approximation is hard to grasp for PSFs from either their spatial (Figure 1a) or frequency profiles (Figure 3a) alone. When both domains are jointly regarded in Figure 3c, the nature of the approximation is immediately revealed. Its instantaneous frequency is a 1 st order (linear) Taylor expansion to the rigorous PSF around the point \(f_{\xi}=0\) and gives rise to quadratic wavefronts. It also becomes immediately evident under which conditions the Fresnel approximation holds. Loosely speaking, it holds when the maximal frequency in the hologram is smaller than \(1/\lambda\) and that \(\max\left(\xi-x\right)/z\) and \(\max\left(\eta-y\right)/z\) are small. This is at the heart of the requirement \(N_{F}\leq 1\).
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 2 Background
### Measurement Factors Influencing a PPG Signal  
This measurement of a PPG signal is a process influenced by five factors (Figure 1):the environment: the external conditions in which the measurement is taken (e.g., temperature, pressure, ambient light, occurrence of a perturbation from the environment hiding the portion of skin in case of iPPG);
* the material: the portion of skin subjected to the measurement;
* the operator: the person performing the measurement;
* the equipment: the device used for the measurement (e.g., a pulse oximeter/smartwatch for PPG or a camera for iPPG);
* the methods: the physics behind the measurement and the algorithm dedicated to the extraction of the desired measurand and the protocol of measurement.  
From each factor, perturbations can affect the quality of a PPG measurement (Table 1).
# Multiband and Lossless Compression of Hyperspectral Images
## 4 Experimental Results
### CCSDS Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images/Configurations** & \multicolumn{2}{c|}{\(\boldsymbol{N=8\,,B=1}\)} & \multicolumn{2}{c|}{\(\boldsymbol{N=8\,,B=2}\)} & \multicolumn{2}{c}{\(\boldsymbol{N=16\,,B=2}\)} \\ \hline \multicolumn{7}{c}{Calibrated (16-bit)} \\ \hline  & AC & PAQ8 & AC & PAQ8 & AC & PAQ8 \\ \hline Yellowstone 0 & 6.7889 & 6.6034 & 6.4564 & 6.4012 & 6.4085 & 6.3411 \\ Yellowstone 3 & 6.6854 & 6.4961 & 6.3553 & 6.2757 & 6.3106 & 6.2183 \\ Yellowstone 10 & 6.0635 & 5.8620 & 5.8397 & 5.7096 & 5.7754 & 5.6309 \\ Yellowstone 11 & 6.3023 & 6.1701 & 6.0889 & 6.0418 & 6.0329 & 5.9723 \\ Yellowstone 18 & 6.7909 & 6.5840 & 6.4235 & 6.3504 & 6.3715 & 6.2865 \\ \hline Average & 6.5262 & 6.3431 & 6.2328 & 6.1557 & 6.1798 & 6.0898 \\ \hline \multicolumn{7}{c}{Calibrated (12-bit)} \\ \hline Hawaii & 2.9533 & 2.9041 & 2.8748 & 2.8434 & 2.7965 & 2.7616 \\ Maine & 3.0746 & 2.9835 & 2.9528 & 2.9030 & 2.9005 & 2.8413 \\ \hline Average & 3.0140 & 2.9438 & 2.9138 & 2.8732 & 2.8485 & 2.8015 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Achieved results for the uncalibrated images of the CCSDS dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods/Scenes (Yellowstone Calibrated)** & **0** & **3** & **10** & **11** & **18** & **Average** \\ \hline \(3D\)-\(MBLP+PAQ8\) (\(N=16\), \(B=2\)) & 3.92 & 3.80 & 3.26 & 3.58 & 3.84 & _3.68_ \\ \(3D\)-\(MBLP+PAQ8\) (\(N=8\), \(B=2\)) & 3.99 & 3.87 & 3.35 & 3.66 & 3.92 & _3.76_ \\ \(3D\)-\(MBLP+PAQ8\) (\(N=8\), \(B=1\)) & 4.09 & 3.97 & 3.40 & 3.71 & 4.04 & _3.84_ \\ \(3D\)-\(MBLP+AC\) (\(N=8\), \(B=1\)) & 3.98 & 3.88 & 3.39 & 3.63 & 3.91 & _3.76_ \\ \(3D\)-\(MBLP+AC\) (\(N=16\), \(B=2\)) & 4.04 & 3.95 & 3.47 & 3.70 & 3.98 & _3.83_ \\ \(3D\)-\(MBLP+AC\) (\(N=8\), \(B=2\)) & 4.18 & 4.08 & 3.55 & 3.79 & 4.15 & _3.95_ \\ \(3D\)-\(MBLP+AC\) (\(N=8\), \(B=1\)) & 4.08 & 3.83 & 3.40 & 3.63 & 3.94 & _3.75_ \\ FL & 3.96 & 3.83 & 3.40 & 3.63 & 3.94 & _3.75_ \\ FL\# & 3.91 & 3.79 & 3.37 & 3.59 & 3.90 & _3.71_ \\ LUT\# & 4.82 & 4.62 & 3.96 & 4.34 & 4.84 & _4.52_ \\ LAIS-LUT\# & 4.48 & 4.31 & 3.71 & 4.02 & 4.48 & _4.20_ \\ TSP-W1 & 3.94 & 3.81 & 3.37 & 3.60 & 3.92 & _3.73_ \\ TSP-W2 & 3.99 & 3.86 & 3.42 & 3.67 & 3.97 & _3.78_ \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison with other lossless compression methods (calibrated images). The results are reported in bits-per-sample (BPS).
# Research on a Time Series Data Prediction Model Based on Causal Feature Weight Adjustment
## 3 LWA-LSTM Method
### LiNGAM Algorithm and Causal Weight Adjustment
Under the assumption of a cycle-free graph, there exists a permutation matrix \(P\in R^{m\times m}\) such that \(B^{\prime}=PBP^{T}\) is a strictly lower triangular matrix with all diagonal elements equal to 0. This solving method is proposed based on the Independent Component Analysis (ICA) algorithm. The algorithm first obtains the limit matrix \(W\) in the connection matrix \(Y=WV\) from the observation data through ICA, where \(Y\) is the vector containing independent components. As you can see, W has a derivation relationship with \((I-B)\). Then, combined with the characteristics of \(B^{\prime}\) as a strictly lower triangular matrix, the causal order can be obtained from \(W\) by using methods such as row and column permutation. Finally, after a pruning algorithm, we obtain the final cause-and-effect diagram. The basic flow is shown in Figure 2.  
In our algorithm, we first use the daily data of the features and the closing price of the next day as variables. We apply the LiNGAM algorithm to determine the causal ordering of the variables, obtain the causal matrix, and draw a causal graph. Then, we observe the  
Figure 2: LiNGAM algorithm flowchart. factors in the causal graph that have a causal relationship with the next day's closing price. We select these feature factors and add them to the original feature set by duplicating a column. This increases the weight of causal feature factors in the feature set.
## 1 Introduction  
In the last few decades, miniaturized and low reagent-consuming microfluidic devices have drawn great interest in biomedical and chemical engineering domains [1; 2; 3]. In microfluidic systems, the reagent flowing velocity inside a channel determines the reaction duration or the liquid mixing performance, therefore the flowing velocity or volume flow rate measurement is fundamental for microfluidic operation. Moreover, the flowmetry sensor in microfluidic chips is also of direct interest in biomedical and medical applications, e.g., assessment of burn depth, skin cancer diagnostics and extracorporeal blood flow monitoring [4; 5; 6]. Hence, numerous velocimetry technologies have been proposed in a microfluidic context [7; 8; 9; 10].  
Thanks to its various outstanding advantages such as low-cost, non-invasive, and self-alignment, optical feedback interferometry (OFI) offers a novel alternative for determining flow rate or velocity within flow-channels. Like traditional laser Doppler velocimetry (LDV) [11; 12; 13], this sensor measures the Doppler shift in the frequency spectrum of laser output power when it is subject to optical feedback from flowing particles. The spectrum is then an image of the velocity of the scatterers in the solution. A number of OFI-based flowmetry sensing research works have been reported [14; 15; 16; 17; 18]. However, these works were not evaluated as a systematic function of the particle characteristics and channel structure, which are essential in microfluidic sensing. Due to the lack of theoretical modeling, thus far, OFI-based microfluidic flowmeter sensor study remains challenging, especially with flowing channels of dimension smaller than 100 \(\upmu\)m.  
Involving different fundamental parameters, a synthetic numerical modeling approach using multiple simulation methods is demonstrated in this paper to predict the sensor signal behavior in the presence of optical feedback modulation in microfluidic sensing measurements. In order to validate our modeling method, a microfluidic flowmetry measurement setup was established, and a series of numerical simulations and flow rate measurements were performed based on this system. Comparisons of the simulation results and the measurement were investigated and discussed, then several important system factors influencing the OFI signal spectrum were highlighted; particle size, particle concentration, channel surface reflectivity and channel dimension.
## 4 Qualitative Discussion
### Development of New LBAs and Their Influence on Workability
[PERSON], [PERSON], and collaborators developed LBAs from different technical lignins [28; 74; 75]. [PERSON] et al. [74] observed that Kraft lignins modified with different poly(ethylene glycol) derivatives promoted better workability in cement pastes than a commercial lignocellulosic. In this study, softwood lignins showed better efficiency than hardwood lignins. The admixtures containing smaller poly(ethylene glycol) chains showed better dispersion properties, although the authors did not fully explain the phenomenon. In the following studies, [PERSON] et al. [28; 75] investigated unmodified Soda lignins and Soda lignins modified by reactions with poly(ethylene glycol) derivatives. The unmodified lignins (with low molecular weight and high phenolic hydroxyl group content) showed plasticizing ability better than a commercial lignosulfonate [28]. The poly(ethylene glycol)-modified lignins presented a performance superior to the unmodified lignins [75]. The authors related this performance to the polyethylene glycol content and the length of the poly(ethylene glycol) chains, which possibly contributed to a higher steric hindrance and a higher phenolic hydroxyl group content in the admixtures produced [75]. Other lignin modification strategies were identified in the literature. [PERSON] et al. [17] adopted different chemical modification strategies to improve the performance of purified lignocellulosicate as a plasticizer for concrete mixtures. These authors verified that the most promising products were poly(propylene oxide)-modified lignosulfonate, which improved the dispersing properties of the purified lignocellulosicate by approximately 19%. [PERSON] et al. [80] produced LBAs from the copolymerization and blending of sulfanilic acid-phenol-formaldehyde condensates with a commercial sodium lignocellulosicate. As with the poly(propylene oxide)-modified lignocellulosicate, the mechanism of action of the admixtures produced by copolymerization and blending possibly involved steric hindrance and electrostatic repulsion [17; 80]. In another study, [PERSON] et al. [25] developed LBAs through a blending of a commercial polycarbonate-based superplasticizer and a sulfate pulping black liquor, followed by modification reactions involving ferrous sulfate, hydrogen peroxide, and sodium sulfite. These LBAs incorporations reduced the mixing water by up to 30.3% compared to a reference mortar, while the commercial polycarboxylate-based superplasticizer promoted a mixing water reduction by up to 30.8%, indicating a similar performance.
# Dropped Object Impact Analysis Considering Frequency and Consequence for LNG-FPSO Topside Module
## 5 Numerical Simulation and Test Evaluation
### Drop Impact Simulation of a Suction Drum Bundle  
In this study, a drop impact simulation was performed using the drop impact analysis method to quantitatively analyze impact energy differences according to the drop angles of the suction drum bundle derived from the QRA results provided in Section 4. Figure 23 shows the FE model of the suction drum bundle of the cylinder type and the drop zone PD03. It is composed of 102,686 finite elements and 205,998 nodes.  
To conduct the drop impact simulation, the free fall condition (gravity acceleration of 9.81 m/s\({}^{2}\)) of the suction drum bundle (1.3 MT) was applied as a loading condition and fixed support conditions were applied as a boundary condition on the side areas of the drop zone. To compare the difference in the impact energy according to the drop angle, a simulation was performed in three cases as follows; the simulation results are shown in Figures 24-26.  
Figure 23: FE model for drop impact simulation.  
* Case 1: Vertical drop of suction drum bundle, 0\({}^{\circ}\),
* Case 2: Drop at a certain angle, 30\({}^{\circ}\),
* Case 3: Horizontal drop of suction drum bundle, 90\({}^{\circ}\)  
As shown in Figures 24-26, the maximum impact energy values exceed the potential impact energy (51 kJ) of the suction drum bundle shown in Table 3 in all cases. Particularly, the maximum impact energy value (240.3 kJ) in case 3 is 4.7 times the potential impact energy value. Meanwhile, the difference in the impact energy between case 1 and case 2 is \(-\)43.5% and that between case 1 and case 3 is +75.9% based on the impact energy value (136.6 kJ) in case 1. These results imply that the impact energy value generated by the suction drum bundle shows a large difference according to the impact orientation.  
Figure 24: Simulation results (case 1, 0\({}^{\circ}\)).  
Figure 25: Simulation results (case 2, 30\({}^{\circ}\)).  
Therefore, it is necessary to analyze the impact energy value and collision phenomenon according to drop angle via quantitative analysis using the drop impact analysis method in the detailed structure design stage of the drop zone following the QRA step. In addition, it is recommended that one examine the structural safety of the drop zone based on quantitative analytical results.
## Material and Methods
### Study Variables
#### 2.4.2 Secondary Outcome Variables  
The same calibrated examiner (PP) recorded probing pocket depth (PPD) values in millimeters (mm) and bleeding on probing (BOP) and plaque index (PI) with dichotomic values (present/absent) using a periodontal probe (PCP-Unc 15, Hu-Friedy(r), Chicago, IL, USA) with a light force (approximately 0.2 N). Calibration was achieved if an agreement of 90% within 1 mm was obtained by duplicate measurements of PPD on 10 random teeth and implants.  
Furthermore, time of functional loading, implant location (central incisor, lateral incisor, canine, first premolar), type of prosthetic rehabilitation (single crown or multiple unit), opposing dentition (natural teeth, fixed prosthodontics on natural teeth, implant-supported fixed prosthodontics), length and diameter were recorded. A patient was defined as a \"periodonitis case\" in accordance with the definition provided by [PERSON] et al. [30] in the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions.  
A trained examiner (PP) realized the esthetic evaluation of the peri-implant mucosa and the implant crown, using the Pink and White Esthetic Score (PES/WES) as described by [PERSON] et al. [31].  
Figure 1: Representative X-rays of an implant of the platform matching group, (**A**). Prosthetic baseline, (**B**). Follow-up.  
Figure 2: Representative X-rays of an implant of the platform switching group, (**A**). Prosthetic baseline, (**B**). Follow-up.  
The examiner evaluated intraoral photographs taken with a digital camera (Nikon D7500, Nikon, Tokyo, Japan) using standard settings (ISO 200, F29, exposure time of 1/160) at the follow-up appointment. Furthermore, impressions were taken, and study casts were realized for each participant to complete the PES/WES assessment. In order to calibrate esthetic evaluation, the examiner evaluated each photograph and study cast in two separate occasions; in case of different scores, the same examiner performed another evaluation.  
Case definitions suggested by [PERSON] et al. [32] for detecting peri-implant diseases in epidemiological studies were adopted.  
Peri-implant health was defined as absence of clinical signs of inflammation, bleeding and/or supuration on gentle probing, without radiographic bone loss.  
Peri-implant mucositis was characterized by presence of bleeding and/or supuration on gentle probing, without radiographic bone loss.  
Peri-implantitis was defined as presence of bleeding and/or supuration on gentle probing, with radiographic bone levels \(\geq\)3 mm apical of the most coronal portion of the intraosseous part of the implant.  
Absence or presence of peri-implant diseases was recorded at the follow-up visit based on clinical and radiographic records.
## 2 Beam Dynamics Design
### Choice of Beam Dynamics Parameters
#### 2.2.4 Input Energy  
Subsequently, we experimented with different input energies of proton beams to optimize both transmission efficiency and cavity length, aiming for a high accelerating gradient. The choice of input energy significantly impacts the design parameters. The lower the input energy of the particles, the stronger the space charge effect between them, which will cause emittance growth. Higher input energies require longer shaper and gentle buncher sections to effectively bunch the beam, consequently leading to an extended cavity length [33]. Transmission efficiency, influenced by numerous factors, exhibits variations across different input energy levels.  
Finally, the optimal balance is achieved when the input energy is set at 30 keV, resulting in a transmission efficiency of 100% and a relatively short cavity length of 2.03 m.
# A Thermopile Device with Subwavelength Structure by CMOS-MEMS Technology
###### Abstract  
Besides the application of the photonic crystal for the photodetector in the visible range, the infrared devices proposed with subwavelength structure are numerically and experimentally investigated thoroughly for infrared radiation sensing in this research. Several complementary metal oxide semiconductor (CMOS) compatible thermopiles with subwavelength structure (SWS) are proposed and simulated by the FDTD method. The proposed thermopiles are fabricated by the 0.35 \(\upmu\)m 2P4M CMOS-MEMS process in TSMC (Taiwan Semiconductor Manufacturing Company). The measurement and simulation results show that the response of these devices with SWS is higher than for those without SWS. The trend of the measurement results is consistent with that of the simulation results. Obviously, the absorption efficiency of the CMOS compatible thermopile can be enhanced when the subwavelength structure exists.  
subwavelength; thermopile; CMOS-MEMS; infrared radiation; infrared sensors Article