esa-sceva/satcom-chunk-collection huggingface dataset:
# Phytochemical and Functional Properties of Fruit and Vegetable Processing By-Products
## 3 Results  
### Nutritional Composition  
The nutritional composition in terms of the ash, protein, lipid, total dietary fiber (TDF), and soluble dietary fiber (SDF) contents of different fruit and vegetable by-products is shown in Table 1.  
The ash content statistically differed between samples, showing the highest mean value for cucumber peel (9.6 \(\pm\) 0.1 g 100 g\({}^{-1}\) dw), followed by orange carrot peel (8.7 \(\pm\) 0.3 g 100 g\({}^{-1}\) dw), while the lowest mean value was for apple pomace (1.5 \(\pm\) 0.1 g 100 g\({}^{-1}\) dw). Similar results were found by [PERSON] and [PERSON] [34], [PERSON] et al. [35], [PERSON] et al. [36], and [PERSON] [37]. Almost all the samples showed similar values of protein content, from the 4.8 \(\pm\) 0.1 g 100 g\({}^{-1}\) dw of apple pomace to the 8.1 \(\pm\) 0.3 g 100 g\({}^{-1}\) dw of the peels of parasnip and peach. The exceptions were the apple peel and cucumber peel samples, with protein values equal to 0.7 \(\pm\) 0.1 g 100 g\({}^{-1}\) dw and 20.5 \(\pm\) 0.2 g 100 g\({}^{-1}\) dw, respectively. Similar characteristics were noted by [PERSON] and [PERSON] [34] and [PERSON] et al. [38]. The lipid content of all samples is quite low, ranging between the 0.5 \(\pm\) 0.1 g 100 g\({}^{-1}\) dw of peach pomace and the 5.5 \(\pm\) 0.1 g 100 g\({}^{-1}\) dw of apple pomace, similar to the results of [PERSON] et al. [39] and [PERSON] [37]. Fruits and vegetables can be considered as an important source of fiber. Our study confirmed this: apple peel and pomace, black and orange carrot peel, cucumber peel, kumquat peel and pomace, and peach peel and pomace showed a fiber value greater than 50.0 g 100 g\({}^{-1}\) dw. On the contrary, mango had a low fiber content (4.3 \(\pm\) 0.5 g 100 g\({}^{-1}\) dw for pomace and 15.1 \(\pm\) 0.8 g 100 g\({}^{-1}\) dw for peel), in accordance with [PERSON] et al. [40] and [PERSON] et al. [41]. TDF can be classified into two types of complementary fibers: soluble dietary fiber (SDF) and insoluble dietary fiber (IDF). The quantity of SDF in peels was higher than in pomaces for apple, orange carrot, and mango samples. An opposite behavior was observed for peach and black plum samples.
###### Abstract  
Resonance enhanced multiphoton ionization (REMPI) is a powerful method for the sensitive determination of polycyclic aromatic hydrocarbons (PAHs) in gaseous mixtures via mass spectrometry (MS). In REMPI, ions are produced by the absorption of at least two photons including defined electronic intermediate states. As a result--unlike other laser-based ionization techniques--spectroscopic selectivity is involved into the ionization process. Nevertheless, these wavelength-dependent ionization rates impede the quantification using REMPI. For this purpose, relative photoionization cross sections (relPICS) give an easy-to-use approach to quantify REMPI-MS measurements. Hereby, the ionization behavior of a single compound was compared to that of a reference substance of a given concentration. In this study, relPICS of selected single-core aromatics and PAHs at wavelengths of 266 nm and 248 nm were determined using two different time-of-flight mass spectrometric systems (TOFMS). For PAHs, relPICS were obtained which showed a strong dependence on the applied laser intensity. In contrast, for single-core aromatics, constant values of relPICS were determined. Deviations of relPICS between both TOFMS systems were found for small aromatics (e.g., benzene), which can be assigned to the differences in UV generation in the particular system. However, the relPICS of this study were found to be in good agreement with previous results and can be used for system-independent quantification.  
REMPI-TOFMS; quantification; cross section; polycyclic aromatic hydrocarbons +
Footnote †: journal: Applied Physics Letters
## 6 Results and Discussions
### Scenario 1: Rayleigh-Generalized Gamma Fading  
The outage probability vs. SNR \((\tilde{\gamma})\) for scenario 1, Rayleigh-Generalized Gamma fading, for threshold SNR \((\gamma_{th})\) = 5 dB, 10 dB, and 20 dB is shown in Figure 2. The fading parameters were set at \(v\) = 1, \(m\) = 1, 2 in Figure 2a and \(v\) = 1, 2, \(m\) = 1 in Figure 2b. It can be observed in Figure 2a that \(m\) = 2 outperformed \(m\) = 1 in all cases, which was expected because of the increase in the fading parameter \(m\) at a constant value of \(v\) and \((\tilde{\gamma})\), leading to a reduction in fading severity and hence reducing the possibility of an outage. A similar trend can be observed in Figure 2b for \(v\). It can be noted that increasing the threshold SNR while keeping other factors \(m,v,(\tilde{\gamma})\) constant increased the outage probability of the system. This is because increasing \((\gamma_{th})\) means that more instantaneous SNR \((\tilde{\gamma})\) values will fall short of the threshold value, resulting in a higher probability of an outage. It can also be observed in Figure 2 that the curves of the outage performances for the same \(m\) and \(v\) but different \((\gamma_{th})\) parameters followed the same trends for a large range of SNR values. Furthermore, it can be observed that for a constant value of \((\gamma_{th})\) and channel parameters \(m\) & \(v\), the outage performance improved with an increase in the average SNR value. For instance, at \((\gamma_{th})\) = 4 dB, \(v\) = 1, \(m\) = 1, the outage probability was 0.7958, 0.3949, and 0.05343 for average SNR values of 5 dB, 10 dB, and 20 dB, respectively.
## 3 Results
### Study Selection  
The initial search identified 4361 records and 159 were retrieved in the updated search in May 2022 as shown in Figure 1. After the removal of duplicates, a total of4053 records were screened. Considering the eligibility criteria, 3969 records were excluded and 84 studies were selected for full-text reading. Four reports were not retrieved in full text [16; 17; 18; 19] after unsuccessful contacts with authors or library sites. Considering 80 eligible studies, 66 were excluded for the following reasons: cells only (\(n\) = 7), no baseline (\(n\) = 6), no cold corneal storage media (\(n\) = 5), no Optisol-GS (\(n\) = 13), parts of cornea (\(n\) = 18), wrong outcomes (\(n\) = 12), wrong study design (\(n\) = 5), and data not retrieved for analysis (\(n\) = 1). In this manner, fourteen records originating from the main databases were selected. Regarding the records identified via grey literature, the first two hundred matches from the 3250 results in Google Scholar were assessed for the study. One duplicate record was manually removed from these sources, another ninety-seven were identified as duplicates by the Rayyan(r) Web software, and one was removed by duplicate records from Google. Furthermore, 92 records were excluded by title/abstract reading: no use of Optisol-GS (\(n\) = 22), wrong outcomes (\(n\) = 48), wrong study design (\(n\) = 21), and data not retrieved for analysis (\(n\) = 1). No remaining records were assessed for eligibility from the other resources since no articles were found on OpenGrey. Therefore, 14 studies were included in this review.  
Most articles included were published in English, except for one in Japanese [20], which was translated with Google Translate. Experts identified in the Expertscape database by the \"list experts\" were contacted about unpublished results or material in progress: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] From those, Dr. [PERSON], Dr. [PERSON], and Dr. [PERSON] answered the email contact reporting not having any new data of interest to this systematic review. Dr. [PERSON] shared a recent article [21]; there was unsuccessful contact with the other authors from December 2021 to January 2022.
## 3 Experiment Results and Analysis
### Experimental Environment and Parameter Setting  
The experimental environment based on a personal computer featured the following: Intel I5-8250U, dominant frequency 4 \(\times\) 1.60 GHz with 8G RAM, Windows 10 operating system, and the algorithm routines were coded in MATLAB 2018b.The taxi GPS data sets contained subsets Aracaju, Beijing, Chongqing, Rome and San Francisco were used as experimental data sets. In each algorithm routine, clustering performance was evaluated using SC, SP, and SSE indicators, which paid particular attention to SC index. The above algorithm routines were trained 20 times independently in the experiment. The clustering numbers to GAK and K-means++ algorithms were initialized to 100. Then the parameters were adjusted respectively within \(\sqrt{n}\) according to the amount of data sets to acquire the best experimental results. The initial parameter setting of PSO and fuzzy system are shown in Table 4.  
For the PSO algorithm: the inertia weight \(\omega\) keeps the particle moving with inertia, giving it the tendency to expand the search space and the ability to explore new regions. The acceleration constants \(c_{1}\) and \(c_{2}\) represent the weights of the statistical acceleration terms that push each particle toward the \(P_{best}\) and \(G_{best}\) positions. A low value allows the particle to hover outside the target region before being pulled back, while a high value causes the particle to make a sudden dash toward or over the target region. The higher the number of populations (NP), the higher the accuracy will be obtained, but at the same time, it will prolong the computing time. The velocity range (VRP) determines the resolution (or accuracy) in the region between the current position and the best position. If velocity maximum is too high, the particle may fly past the good solution, and if velocity minimum is too small, the particle cannot perform sufficient exploration, leading to becoming stuck in local optima.  
For the GA algorithm: the adaptive cross coefficient range (ACCR) is too large, it is easy to destroy the existing favorable mode, increase the randomness and easily miss the optimal individual; it is too small to renew the population effectively. The adaptive variation coefficient range (AVCR)is too small and the diversity of the population declines too fast, which easily leads to the rapid loss of effective genes and is not easy to repair; it is is too large, the diversity of the population can be guaranteed, but the probability of higher-order mode destruction also increases.  
For the fuzzy system: the fuzzy range of \(\omega\) in the PSO (FRW) control the variation range in the defuzzification process. The fuzzy range of \(c_{i}\) in the PSO (FRC) control the variation range in the defuzzification process.
## 3 Methods
### Signal Preprocessing  
#### 3.2.1 Noise Reduction and Normalization  
Noise affects recognition rate by distorting the original signal. While recording heart sounds, noises can occur such as external sounds, sweeping sounds due to body movements, breathing sounds, thermal noise and so on. Noise due to skin rubbing occurs at the beginning and end in most cases when recording actual heartbeats. To remove this unwanted sound, 15% of the audio file was first eliminated. Cardiac sounds have specific frequency ranges. S1 has a frequency of at least 100 to 200 Hz, S2 has a frequency of 50-250 Hz, and sick cardiac sound has a maximum frequency of 400 Hz. Table 1 shows the frequency range and diagnostic value according to the cardiac sound. The frequency characteristics of these components can be used as important parameters for identification. After converting to the frequency domain, the band-pass filter eliminates frequencies other than 40-400 Hz, excluding S1, S2 and the heart noise frequency ranges.  
Each sound in the dataset has a different amplitude range, and the quality of the sound may vary depending on the environment where the heartbeat is recorded. Therefore, it is necessary to uniform the dataset for accurate comparison. The signals must be equalized to the same amplitude range and have the same quality. To change the data to a common scale without distorting the difference in range, the signals were scaled between 0 and 1 through min-max normalization. As a result, time-domain noise in the audio files was reduced.
## 1 Introduction  
With the development of science and technology, as well as the community's increased awareness of the human impact on the environment, the effect of noise pollution has become more prominent. Noise data statistics, analysis and control strategy in different fields have attracted interest in different countries and regions [1; 2; 3; 4; 5; 6]. According to the report issued by the Ministry of the ecological environment in China, the contribution to the overall environmental pollution by noise exceeded 30% in 2018, coming in second place only to air pollution [1]. The negative impacts of noise on human health have attracted widespread attention [3; 7; 8; 9]. The report issued by the World Health Organization (WHO) in 2018 revealed that the burden of disease from environmental noise was the second highest [3], which also reflects the seriousness of noise pollution.  
As major energy conversion devices, the centrifugal pumps have been gradually applied in the fields of industry, agriculture, transportation and architecture and so forth. Due to the complexity of flow inside pumps, severe flow-induced noise is generated. For instance, the noise generated by a 22 kW single-stage single-suction centrifugal pump in air and water medium is up to 94 dB and 180 dB at rated operating conditions, respectively, both of which are much higher than the international standard [10]. The unexpected high-level noise could affect the operating stability, living and working environment and human health. Therefore, the study of flow-induced noise generated by centrifugal pumps is extremely significant from the perspective of engineering, environmental protection and human health.  
A large amount of studies have been conducted on this subject and many have been discovered in the past decades. For example, according to Web of Science, more than 130 articles, as shown in Figure 1, analyzed the subject directly in terms of the generation mechanism and propagation route, the distribution characteristics and the optimization design of flow-induced noise which are based on theoretical analysis, numerical simulation and experimental measurement, let alone the number of articles that are indirectly related to the subject. The purpose of this paper is to summarize the study progress and corresponding valuable conclusions systematically. Then, the areas that may need to be paid attention to are emphasized, which could lay a foundation for further study.
# Performance Analysis in Heterogeneous Networks with Spatiotemporal Traffic and Scheduling
## 3 Mean Packet Throughput Analysis
### Mean Packet Throughput of the Heterogeneous Network with UFR
 \tag{12}\]  
_where \(\hat{\lambda_{k}}\) is the density of the \(k\)th tier normalized by the density of the \(i\)th tier, i.e., \(\hat{\lambda_{k}}=\frac{\lambda_{k}}{\hat{\lambda_{i}}}\), \(\hat{b_{k}}\) is the bias factor of the \(k\)-th tier normalized by the bias factor of the \(i\)-th tier, i.e., \(\hat{b_{k}}=\frac{b_{k}}{b_{i}}\),_  
\[Z(\theta,\alpha,\hat{b_{k}})=\frac{2\theta b_{k}^{\frac{2}{2}-1}}{\alpha-2} \cdot{}_{2}F_{1}\left[1,1-\frac{2}{\alpha};2-\frac{2}{\alpha};-\frac{\theta}{ \hat{b_{k}}}\right]. \tag{13}\]  
_where \({}_{2}F_{1}\left[\cdot,\cdot;\cdot;\cdot\right]\) is the Gauss hypergeometric function._Proof. : The transmission success probability of the \(i\)th tier is derived as:  
\[ \begin{split}&\mu_{\Phi_{i}}=\mathbb{P}\big{[}\gamma_{i,t}>\theta \big{]}\\ &\quad=\mathbb{E}\big{[}\exp\big{(}-\theta\|x_{i}\|^{a}\big{(} \sum\limits_{y_{k}\in\Phi\backslash x_{i}}\frac{\hat{P}_{ik}\zeta_{xy}y_{k}y_{ k}}{\|y_{k}\|^{a}}\big{)}\big{)}\big{]}\\ &\quad=\mathbb{E}\big{[}\prod\limits_{y_{k}\in\Phi\backslash x_{ i}}\exp\big{(}-\frac{\theta\hat{P}_{ik}\zeta_{yx}y_{k}y_{k}\|x_{i}\|^{a}}{\|y_{k}\|^{a}} \big{)}\big{]}\\ &\quad\stackrel{{(a)}}{{=}}J_{z_{k}}^{\infty}\sum \limits_{k=1}^{K}\exp\big{[}-p_{ak}\lambda_{k}Z(\theta,\alpha,\hat{b_{k}}) \hat{P}_{ik}^{\frac{2}{2}}\pi r^{2}\big{]}f_{R}(r)dr\\ &\quad=\frac{1}{1+a_{i}\prod\limits_{k=1}^{K}\big{[}p_{ak}\cdot \hat{\lambda}_{k}Z(\theta,\alpha,\hat{b_{k}})\hat{P}_{ik}^{\frac{2}{2}}\big{]} },\end{split}  \tag{14}\]  
where \((a)\) follows from the probability generating function (PGFL) and conditional PGFL of PPP [14], \(f_{R}(r)=2\pi\frac{\lambda_{i}}{a_{i}}r\exp\big{(}-\pi\frac{\lambda_{i}}{a_{i} }r^{2}\big{)}\) is the probability density function (PDF) of the serving distance. \(z_{k}=(\hat{P}_{ik}\hat{b_{k}})^{\frac{1}{2}}r\) is the minimum distance between the typical user and its closest BS from the \(k\)th tier based on the bias association policy. Therefore, \(\mu_{\Phi_{1}},\mu_{\Phi_{2}},\cdots,\mu_{\Phi_{K}}\) satisfy the coupling equations in Equation (12). Please note that the success transmission probability of each tier is associated with that of all other tiers. By deconditioning Equation (3) on \(N\), the mean packet throughput of the \(i\)th tier with RS policy is obtained as:  
\[\tau_{RSi}=\sum\limits_{k=1}^{\lfloor\frac{\tau_{\Phi_{i}}}{\xi}\rfloor}\frac{ \mathbb{P}(N_{i}=k)(\mu_{\phi_{i}}/k-\xi)}{1-\xi}, \tag{15}\]  
By deconditioning Equation (5) on \(N\), the mean packet throughput of the \(i\)th tier with RR policy is derived as:  
\[\tau_{RRi}=\sum\limits_{k=1}^{\lfloor\frac{\tau_{\Phi_{i}}}{\xi}\rfloor}\frac{ \mathbb{P}(N_{i}=k)(\mu_{\phi_{i}}/k-\xi)}{1-\frac{k-1}{2}\cdot\mu_{\phi_{i}}/ k-\xi}, \tag{16}\]  
Then, the mean packet throughput can be obtained based on the total probability law as:  
\[\tau=\sum\limits_{i\in I_{1}}a_{i}\tau_{RSi}+\sum\limits_{i\in I_{2}}a_{i} \tau_{RRi}. \tag{17}\]
# BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining
## 5 Case Studies
### Global Deformation Pattern of Suspension Bridges  
Users may use the scripts to obtain the 1-h averages of structural displacements over one year, as illustrated in the bottom left panel of Figure 3. Clearly, the yearly variations in various displacements are of different ranges. For example, the mid-span vertical displacements of the main cables (GPS 2-Z and GPS 3-Z) are approximately one order of magnitude larger than the other displacements.  
To examine the trend of GPS5-X, GPS1-X, GPS6-X, GPS7-X, GPS4-X and GPS8-Y, we remove the GPS 2-Z and GPS 3-Z curves from the time-series plot to narrow the vertical axis range to between \(-0.15\) and \(0.15\) m. This adjustment highlights the tower-top horizontal displacements along the bridge axis (GPS 1-X and GPS 4-X). As shown in Figure 5, the GPS 1-X and GPS 4-X curves stand out. Both curves evolve at the same pace yet out of phase, which means the two towers deflect in the opposite directions under normal operational conditions.  
Further, the scatter plots in the top right panel, as shown in Figure 3, are used to explore the correlation between different variables in the time-series plot. Despite the great difference in variation ranges, the mid-span vertical displacements (GPS2-Z and GPS3-Z) exhibit a significant linear correlation with the tower-top horizontal displacements (GPS1-X and GPS4-X). The absolute value of the Pearson correlation coefficient for GPS2(3)-Z versus GPS1(4)-X is quite close to 1.0. Similar to scatter plots, the parallel coordinate plot in the bottom right corner can also be used to investigate the relationships between various variables.  
When a data point in any scatter plot is selected, the corresponding data points at the same time instant in the other plots are highlighted automatically. For example, in Figure 6, the green dots represent a deformation state of the bridge (designated as State A), while  
Figure 5: The 1-h averages of GPS 1-X and GPS 4-X (the tower-top horizontal displacements along the bridge axis) over one year, showing the two towers deflect in the opposite directions under normal operational conditions.  
the red dots represent another state (State B). This feature allows for a clear interpretation of the bridge's transition between different operational states.  
The physical explanation for the observed phenomenon is attributed to the overall deformation compatibility of suspension bridges, as illustrated in Figure 4. The two tower tops moving close to each other (i.e., transitioning from State A to State B) is always accompanied by a decrease in the mid-span elevation of the main girder, and vice versa. This deformation pattern revealed in BOPVis, aligns with the deformation mechanisms of real suspension bridges [28], thereby verifying the reliability of the monitoring datasets.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 6 Comparison
### FPGA Performance Compared to General-Purpose Processors
Especially in the case of energy-constrained embedded devices or in applications where data arrive sequentially and processing in large batches is not possible, FPGAs provide outstanding performance in terms of latency, power consumption, and energy efficiency.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 2 Related Work
### Determining Request/Relay Vehicle  
Some research has discussed how to select a vehicle to request or relay content. In [22], a vehicle that lies in the common communication area of a packet sender and other potential relay vehicles is preferentially selected. In [5], the content requester requests content from vehicles moving in the same lane first and then to vehicles moving in adjacent lanes upon failure.