esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Experiments Results
#### 4.1.6 IGD Value Convergence Experiment  
In order to demonstrate the convergence curves of the SA-MPCMOEA algorithm compared to other algorithms in terms of the IGD value, we conducted a convergence analysis in CF1, CF2, LIRCMOP8, LIRCMOP10, LIRCMOP11, and LIRCMOP14, respectively. For each test problem, we selected 6 values from 1000 function evaluations to show the convergence variation of the algorithm. As can be seen in Figure 6, SA-MPCMOEA converges faster than other algorithms when in search mode 1, which demonstrates the effectiveness of the angle-based convergence model. This is mainly because, given limited computational resources, there are fewer individuals available to explore the feasible region. If the algorithm cannot converge quickly to reduce the search range, the population will spend some computational resources on ineffective exploration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problem** & **M** & **D** & **SA-MPCMOEA/MP** & **SA-MPCMOEA/FS** & **SA-MPCMOEA/DA** & **SA-MPCMOEA** \\ \hline CF1 & 2 & 10 & \(2.1823\times 10^{-1}\) (\(3.92\times 10^{-2}\)) & \(-\) & \(2.1162\times 10^{-1}\) (\(5.74\times 10^{-2}\)) & \(-\) & \(1.7174\times 10^{-1}\) (\(1.22\times 10^{-2}\)) & \(-\) & \(1.4121\times 10^{-1}\) (\(3.12\times 10^{-2}\)) \\ CF2 & 2 & 10 & \(4.9262\times 10^{-1}\) (\(5.46\times 10^{-2}\)) & \(-\) & \(2.9244\times 10^{-1}\) (\(7.91\times 10^{-2}\)) & \(2.5221\times 10^{-1}\) (\(5.89\times 10^{-2}\)) & \(8.5247\times 10^{-2}\) (\(2.42\times 10^{-3}\)) \\ CF3 & 2 & 10 & \(4.0022\times 10^{-10}\) (\(10.46\times 10^{-10}\)) & \(-\) & \(3.4833\times 10^{-2}\) (\(2.29\times 10^{-1}\)) & \(2.2313\times 10^{-4}\) (\(4.66\times 10^{-1}\)) & \(1.3271\times 10^{-2}\) (\(2.03\times 10^{-3}\)) \\ CF4 & 2 & 10 & \(9.9443\times 10^{-1}\) (\(9.35\times 10^{-1}\)) & \(-\) & \(1.3150\times 10^{-1}\) (\(1.21\times 10^{-6}\)) & \(7.3013\times 10^{-2}\) (\(2.07\times 10^{-1}\)) & \(-\) & \(2.2093\times 10^{-1}\) (\(1.14\times 10^{-1}\)) \\ CF5 & 2 & 10 & \(7.6384\times 10^{-1}\) (\(1.81\times 10^{-1}\)) & \(6.1379\times 10^{-3}\) (\(8.87\times 10^{-6}\)) & \(3.6262\times 10^{-10}\) (\(9.75\times 10^{-1}\)) & \(1.3844\times 10^{-10}\) (\(9.26\times 10^{-1}\)) \\ CF6 & 2 & 10 & \(2.0121\times 10^{-1}\) (\(1.11\times 10^{-1}\)) & \(-\) & \(1.3078\times 10^{-1}\) (\(1.40\times 10^{-1}\)) & \(5.4343\times 10^{-1}\) (\(1.50\times 10^{-1}\)) & \(1.0460\times 10^{-10}\) (\(5
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Swarm Exchange Particle Swarm Optimization Algorithm  
The swarm exchange particle swarm optimization algorithm splits the particle swarm into several subpopulations, usually divided into two to three, with each subpopulation corresponding to different search strategies in the solution space for iterative search [27]. One of the subpopulations is selected for the main search group, and the rest of the subpopulations can be used as an auxiliary contraction group. In order to prevent the main search group from falling into a premature state due to the lack of diversity in the iterative process [28], the particles in the auxiliary search group will be exchanged with the particles in the main search group according to the rules to ensure the global search performance of the algorithm.  
The swarm exchange particle swarm optimization algorithm usually has the following exchange methods: random exchange: randomly select particles in the auxiliary search group to exchange with the main group; local exchange: group the particles in the main and auxiliary groups according to the spatial location or sort them to form a ring according to a certain rule, and then exchange them in the group or ring segments.
## 4 Discussion
### Satiety Index Test  
We expected to find an increase in satiety with the added FI, but our results did not reach statistical significance. Food products that produce high satiety can help with energy intake regulation and consequently the management of weight. This is due to imparting a satiation sensation, which reduces the amount of energy consumed at the following meals. There are a number of studies on the impact of FI, including DFs, on the SI of food products.  
It is suggested that the addition of DF can modify and improve gut hormone excretion and induce satiety. Previous researchers studied the impact of some DF on satiety response. Adding 2.5% DF for semi-solid and solid foods improved satiety response [17]. In a previous review on the relationship of DFs and appetite, it was stated that high-level addition of both GG and IN improved satiety response and decreased intake of energy [18].  
The impact of DF on satiety levels have been linked to a number of mechanisms, including that DF are less energy-dense, take longer time to chew and digest [19], make a physical barrier and imparts a satiety sensation, they are fermented and produce short-chain fatty acids such propionate, butyrate and acetate, which modify gastrointestinal mobility and induce secretion of satiety hormones, including cholecystkinin glucagon-like peptide-1 and ghrelin [19; 20]. However, in this case, the number of fibers and time might not be enough to increase satiety significantly.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 5 Reward Design
### Relative Rewards  
It is common to reward the difference to the previous value [29; 122; 198; 232; 278], i.e., the temporal difference (TD). Comparison can also be made with other solutions such as initial [178; 179; 181; 195] or best [29; 188; 244; 259; 263; 264; 265; 266] or from other baselines [211; 212]. This relative fitness can be either the ratio of both values [190; 233; 269] or the absolute difference [49; 208; 274].  
The makespan is often used as a temporal difference measurement [49; 84; 142; 188; 278], but other measures such as profit [98; 208; 266] or resource utilization [109; 178] are sometimes used.  
Relative rewards shift the focus to improvement over time instead of immediate absolute improvements. They often have some type of normalization built in, which makes states across different problems more comparable. However, if wrong reference points are used or if noise is too impactful, the agent can converge into suboptimal solutions.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 4 Analysis of Performance Results
### Simulation Results  
The PER is the metric used to evaluate the performance of a receiver in terms of communication reliability. The scenario considered to perform the simulations to obtain data from the PER is described in Table 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **802.11p/bd** & **LTE-V2X/NR-V2X** \\ \hline Packet duration & [304; 152; 104; 72]\(\mathrm{\SIUnitSymbolMicro s}\) \\ \hline Waveform & OFDM \\ \hline Channel equalization method & MMSE \\ \hline Receiver antenna gain (Grx) & 3 dBm \\ \hline Transmitter antenna gain & 3 dBm \\ (Gtx) & \\ \hline Maximum Doppler shift (fm) & 100 Hz and 2000 Hz \\ \hline Coherence time & 4.23 and 0.21 \\ \hline Fading distribution & Rayleigh \\ \hline \hline \end{tabular}
\end{table}
Table 8: PER calculation configuration parameters.  
Figure 3: Comparison of data rate (**A**) and transmission latency (**B**) for LTE-V2X and NR-V2X protocols.
## 5 Conclusions  
Influence of the initial conditions on the wind parameters at a bridge middle span in a U-shaped valley by CFD and AHP was investigated in this study. The main conclusions are as follow:  
1. The numerical results showed that the spatial distribution of wind parameters at the mid-span of a bridge in a U-shaped valley is complex and is significantly affected by the initial conditions.
2. In order to connect CFD and AHP, the wind parameter factor and factor sensitivity coefficient were proposed. A new quantification model was established to describe the functional relationship between the factor sensitivity coefficient and the scale value, and the logarithmic function reliability was verified. An evaluation system for the influence of initial conditions on wind parameters was formed.
3. The evaluation results showed that the influence of oncoming wind direction and wind speed profile on wind speed was equivalent, followed by surface roughness and inlet wind speed. The sensitivity of the wind attack angle to the oncoming wind direction was much higher than that of surface roughness, inlet wind speed, and wind speed profile. The oncoming wind direction had the greatest influence on the wind azimuth angle, which was the sum of wind speed profile, surface roughness, and inlet wind speed.
4. As for the wind-resistant of bridges in the U-shaped valley, oncoming wind direction was the first initial condition and needs the comprehensive consideration. The second was the wind speed profile. The surface roughness and inlet wind speed were the least important factors.
5. This paper did not fully consider the coupling effect of various initial conditions.  
Conceptualization, J.W. and J.L.; Methodology, J.W. and J.L.; Software, J.W.; Validation, J.W., Y.L. and X.Z.; Formal analysis, J.W. and X.Z.; Investigation, J.W. and J.L.; Resources J.W.; Data curation, J.W.; Writing--original draft preparation, J.W.; Writing--review and editing, F.W. and J.L.; Visualization, F.W. and J.L.; Supervision J.L.; Project administration, J.L. and F.W.; Funding acquisition, J.L and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (51978077) and the Natural Science Basic Research Program of Shaanxi Province (2022)Q-507); the authors would also like to express their gratitude to the sponsors.  
The study did not involve humans or animals.  
The study did not involve humans.  
The authors declare no conflict of interest.
## 1 Introduction
### Background to the Maritime Industry  
Historically, the maritime domain has been the linchpin of global commerce. Today, it continues its legacy with over 90% of global trade transported by sea. Renowned for its cost-effectiveness and efficiency, maritime transport boasts the unparalleled capability to handle vast cargo loads across expansive distances. This efficiency makes it indispensable in connecting global economies and anchoring international trade [1].  
The maritime industry is multi-faceted, encompassing shipping lines, port operations, naval defense, offshore exploration, fishing, and maritime tourism, each with its unique characteristics and challenges. In recent years, the industry has seen remarkable changesbrought about by technological advancements, evolving regulations, environmental concerns, and the pressing need for operational efficiencies [2; 3].  
Shipping, the most prominent component of the industry, has evolved significantly from the days of sail ships to the era of fully automated vessels. Ports, once considered mere points of transfer, have morphed into complex logistics hubs offering a range of services. Furthermore, emerging sectors such as offshore wind energy and marine biotechnology are paving the way for a more sustainable and diversified maritime industry [4].  
However, these advances also bring about new challenges and complexities. The industry grapples with issues such as environmental pollution, maritime security, overcapacity, infrastructure constraints, and supply chain inefficiencies. These challenges underline the need for technological solutions that can optimize operations, increase efficiency, enhance safety, and reduce environmental impact [5; 6].  
As the maritime industry sails into the digital era, Internet of Things (IoT) technologies have emerged as potential game-changers. IoT's ability to interconnect devices, systems, and services to gather, store, and analyze data could significantly transform the industry's operations and business models, marking the onset of a new era of'smart shipping' and'smart ports' [2; 7].  
The wave of digital transformation has swept across numerous industries over the past few decades, reshaping operational dynamics and opening up avenues for innovation and efficiency. Industries ranging from finance and healthcare to agriculture have embraced the digital tide, harnessing the power of emerging technologies to redefine their landscapes. However, the maritime industry, with its deep-seated traditions and vast operational complexities, has been somewhat gradual in this adaptation. It is only in recent years that we have begun to see a significant digital shift in maritime operations. This relatively slow-paced adoption stresses the immediacy and pertinence of understanding the role of technologies such as IoT and data analysis in the maritime domain. As the maritime industry navigates this digital era, there is a compelling need to understand its trajectory, ensuring it aligns with global digital trends while preserving the industry's unique identity [1; 2; 3; 4; 5; 6; 7].
## 5 Analysis of the Transmission Delay
### One-Hop Delay/Distance for Intra-/Inter-Lane Scenario
#### 5.2.1 One-Hop Timer Delay for the Intra-Lane Scenario  
In this scenario, the link lifetime is no longer a critical problem when all of the vehicles tend to drive with identical speed. Therefore, we have the one-hop timer delay as:  
\[T_{j}^{(1,1)}=t_{\min}+t_{\Delta}(1-\epsilon)\left(1-\frac{D_{j}^{(1,1)}}{R} \right). \tag{11}\]  
Since the vehicle spatial distribution follows a one-dimensional Poisson process, the vehicle position, conditional on \(N=n\), has a uniform probability distribution in the range \([0,R]\); hence, the random variables \(D_{j}^{(1,1)}\) are independent on one another and uniform over \([0,R]\). Then, we have:  
\[\begin{split}\Pr\{T^{(1,1)}\leq t|N=n\}&=1-\Pr\left\{ T^{(1,1)}>t|N=n\right\}\\ &=1-\Pr\left\{T_{1}^{(1,1)}>t|N=n\right\}\cdots\Pr\left\{T_{n}^{(1,1)}>t|N=n\right\},\end{split} \tag{12}\]  
where \(T_{k}^{(1,1)}\sim U(t_{\min},t_{\min}+t_{\Delta}(1-\epsilon))\). The Complementary Cumulative Density Function (CCDF) of the timer level of each receiver vehicle can be represented as:  
\[\Pr\left\{T_{k}^{(1,1)}>t|N=n\right\}=1-\left(\alpha_{1}t+\beta_{1}\right), \tag{13}\]  
for \(k=1,\ldots,n\), and for all \(t\) belonging to the interval \([t_{\min},t_{\min}+t_{\Delta}(1-\epsilon)]=[-\beta_{1}/\alpha_{1},(1-\beta_{1} )/\alpha_{1}]\), where \(\alpha_{1}=1/t_{\Delta}(1-\epsilon)\) and \(\beta_{1}=-t_{\min}/t_{\Delta}(1-\epsilon)\). Therefore,  
\[\Pr\left\{T^{(1,1)}>t|N=n\right\}=\left[1-\left(\alpha_{1}t+\beta_{1}\right) \right]^{n}. \tag{14}\]Since the number of vehicles follows the Poisson distribution with parameter \(\lambda_{1}\), the Cumulative Distribution Function (CDF) of \(T^{(1,1)}\), conditional on there being at least one vehicle in the transmission range of the initiating vehicle \(A\), can be represented as:  
\[\begin{split}\Pr\{T^{(1,1)}\leq t|N>0\}&=1-\Pr\{T^{ (1,1)}>t|N>0\}\\ &=1-\sum_{n=1}^{\infty}\Pr\left\{T^{(1,1)}>t|N=n\right\}\Pr\{N=n|N >0\}\\ &=\frac{1-\exp\left(-\lambda_{1}\left(\alpha_{1}t+\beta_{1} \right)\right)}{1-\exp\left(-\lambda_{1}\right)}.\end{split} \tag{15}\]  
holding for \(t\in I_{1}\triangleq[t_{\text{min}},t_{\text{min}}+t_{\Delta}(1-\epsilon)]\).  
Based on the results of conditional CDF, the conditional pdfcan be represented as:  
\[f_{T^{(1,1)}|N>0}(t)=\frac{\lambda_{1}\alpha_{1}\exp\left(-\lambda_{1}\left( \alpha_{1}t+\beta_{1}\right)\right)}{1-\exp\left(-\lambda_{1}\right)}. \tag{16}\]  
for \(t\in I_{1}\triangleq[t_{\text{min}},t_{\text{min}}+t_{\Delta}(1-\epsilon)]\) and zero otherwise.  
Let \(t^{(1,1)}\) denote the expectation of \(T^{(1,1)}\), and we have:  
\[\begin{split} t^{(1,1)}=&\int_{t\in I_{1}}t\,f_{T^{ (1,1)}|N>0}(t)\,dt\\ =&\frac{1}{\lambda_{1}\alpha_{1}}+\frac{t_{1,1}^{-} \exp(-\lambda_{1})t_{1,1}^{+}}{1-\exp(-\lambda_{1})}\\ =& t_{\text{min}}+t_{\Delta}(1-\epsilon)\left( \frac{1}{\lambda_{1}}-\frac{1}{\exp(\lambda_{1})-1}\right).\end{split} \tag{17}\]
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 3 Materials and Methods
### Analysis of Volatiles by GC-MS  
7890A gas chromatograph with 5975C mass spectrometer selective detector (Agilent Technologies Inc., Santa Clara, CA, USA) was used, and a DB-5 capillary column (30 m \(\times\)0.25 mm \(\times\) 0.25 \(\upmu\)m) was applied for GC-MS. The extraction was injected into the inlet of GC-MS and desorbed at 250 \({}^{\circ}\)C for 3 min. The injection port was operated in splitless mode, helium (99.999%) was used as carrier gas at the flow rate of 1.2 mL/min. The initial oven temperature was 40 \({}^{\circ}\)C (2 min), ramped at 3 \({}^{\circ}\)C min\({}^{-1}\) to 170 \({}^{\circ}\)C (5 min), and then ramped at 10 \({}^{\circ}\)C min\({}^{-1}\) to 260 \({}^{\circ}\)C (5 min). Mass detector conditions were performed by EI (electronic impact) mode at 70 eV, source temperature at 230 \({}^{\circ}\)C, mass spectra acquisition range of 45-500 amu, scanning rate of 3.18 amu/s. The transfer line temperature was 280 \({}^{\circ}\)C. The volatile compounds were identified by comparing the mass spectra with mass-spectral library (NIST 2011), retention index (RI), aroma description, and matching against the published data [34]. Each extract was analyzed in triplicate. Mean data and relative standard deviation (mean \(\pm\) SD) of volatiles were reported.
## 2 Methods
### Package Assessment and Feature Comparison
#### 2.2.1 Data Quality Framework  
We followed a framework for harmonized DQA in observational studies [6] to assess the scope of possible DQA. The detailed structure of the framework and definitions are provided in Appendix B. The framework has been designed based on a literature review of DQ concepts, an empirical assessment of an existing DQ framework [7], and experiences conducting partially automated DQ assessments in cohort studies. Its hierarchical taxonomy consists of three levels: dimensions, domains, and DQ indicators (Table 1). The dimension level distinguishes qualitatively different aspects of DQ: integrity (\"the degree to which the data conforms to structural and technical requirements\"), completeness (\"the degree to which expected data values are present\"), consistency (\"the degree to which data values are free of breaks in conventions or contradictions\"), and accuracy (\"the degree of agreement between observed and expected distributions and associations\") [6]. Each dimension is divided into different DQ domains. These differ mainly in terms of the methodology used to evaluate DQ. For example, the consistency dimension distinguishes range and value violations from contradictions. The range and value violations domain compares single data values against metadata (e.g., range limits). In contrast, the contradictions domain jointly evaluates two or more different data values against a given rule that describes inadmissible combinations. Currently, a total of 10 domains are distinguished across the four dimensions [6]. The different DQ _indicators_ are defined within domains. DQ indicators quantify detected or potential DQ issues (e.g., the number or proportion of data fields with range violations). With few exceptions, the computation of indicators requires the provision of metadata, which represent requirements on the data. However, based on a manual inspection, users can also use many statistics and graphs to infer DQ issues. For example, users can identify range violations with a histogram. Therefore, in addition to indicators, the DQ framework also defines _descriptors_, which provide insights on relevant data properties without quantitatively generating a measure of a deviation from a particular requirement. Having only a descriptor implies a manual processing step to conclude whether a DQ issue is present. Hence, having an indicator is generally preferable to a descriptor. During the package evaluation, we assessed which DQ dimensions and domains were covered by DQ indicators or descriptors. We display our findings at the level of dimensions and domains for a concise overview; in Appendix C, we provide a full summary of indicators and descriptors.
## 3 Results and Discussion
#### 3.3.2 Crystalline Structure  
The crystalline structure of the films was measured using X-ray diffraction (XRD) analysis, and these results are showed in Figure 5.  
Figure 4: Scanning electron micrographs of the surface and cross-section of the gac pulp-based films.  
XRD patterns of the control GP film and the GPP film were presented in Figure 5A, with both exhibiting the diffraction with a single peak. However, GP film showed a shaper peak as compared with that of GPP film, especially at 21.6\({}^{\circ}\) (29). The results indicate that the GP edible film had a semi-crystalline structure, whereas the GPP had an amorphous form. These findings can be explained by the diffraction of the films due to pectin, which was predominately amorphous in nature [63].  
The diffraction of the films incorporated with the essential oils and plant extracts are showed in Figure 5B,C respectively. Overall, all the films had similar patterns of diffraction with a diffraction peak at around 21\({}^{\circ}\) (20). The results (Figure 5B) suggest that the films incorporated with the essential oils had more discernible peaks than the control gac pulp  
Figure 5: XRD patterns of control GP film and GPP film (**A**); of control GP film and films incorporated with plant oils (**B**); of control GP film and films incorporated with plant extracts (**C**).  
film, indicating that addition of essential oils improve the crystalline structure of the films. These can be explained by the molecular interactions between the film components. The result was in line with previous studies [64; 65].  
The results (Figure 5C) also indicated that the films incorporated with the plant extracts had a relatively flat single peak (except the GMC film). Among them, GLE film showed a lower and less discernible peak at 21.11\({}^{\circ}\) (20) than the control gac pulp film, as well as GBA and GMC films. The results are supported by a study of [PERSON] and [PERSON] [17]. Besides, the GMC film had another diffraction peak at 14.9\({}^{\circ}\), revealing that incorporation of macadamia could affect the degree of crystalline in structure of edible film, while it was not in case of blueberry ash and lemon myrtle extracts.