esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of the Encapsulated SBO
#### 2.3.5 Bioaccessibility  
The in vitro bioaccessibility of \(\upbeta\)-carotene was assessed by the simulation of gastrointestinal digestion using the harmonized static approach previously described by [PERSON] et al. [28] with some modifications. Firstly, 500 mg of SBO was mixed with 2 mL of ultrapure water and then combined with SGF in 1:1 ratio (\(v/v\)), as suggested by the original procedure [25]. The samples were incubated for 2 h at 37\({}^{\circ}\)C in a water bath (Buchi B-490, Flawil, Switzerland), maintaining constant shaking at 110 rpm. Following the incubation, SIF was added to the mixture, where the gastric chime-SIF ratio was set to 1:1 as suggested by the original method [25]. The simulation of intestinal digestion was conducted by shaking the samples for subsequent 2 h, at 37 \({}^{\circ}\)C in the shaking water bath (110 rpm). To eliminate the crude components from the sample, the mixture was placed on ice for 10 min and then centrifuged for 20 min at 4 \({}^{\circ}\)C at 4100\(\times\)\(g\) rpm. Enzyme activity in collected supernatants containing bioavailable fraction was halted by subjecting the sample to a heat shock at 100 \({}^{\circ}\)C for 5 min using a Thermomixer R (Eppendorf, Hamburg, Germany). Afterwards, the samples were cooled in an ice bath for another 10 min before being centrifuged, again under identical conditions. The resulting supernatant, which represented the bioavailable fraction of the samples, was stored at \(-\)80 \({}^{\circ}\)C until further analysis. A blank sample containing only the digestion solutions was also prepared to ensure that there were no interferences from the reagents used. The \(\upbeta\)-carotene concentrations were quantified following the official AOAC method [29]. The \(\upbeta\)-carotene bioaccessibility after the simulation of gastrointestinal digestion was determined by calculating the percentage (%) according to Equation (2):  
\[\text{\% Bioaccessibility}=(\text{amount in bioaccessible fraction/amount in undigested extract})\times 100 \tag{2}\]
## 3 Background
### Deep Belief Networks
To overcome the difficulty of calculating expectations, the contrastive divergence (CD) algorithm [44] for estimating log-likelihood gradient was developed by [PERSON] in 2002. CD-k algorithm approximates the expectation in Equation (23) by limited \(k\) (often \(k=1\)) iterations of Gibbs sampling to update the network parameters \(\theta=(W,b,c)\). The improved algorithm of the CD algorithm is persistent contrastive divergence (PCD) algorithm [45], which makes the training process more efficient. The update process of parameter \(\theta\) is as follows:  
Figure 1:2  
Figure 1:3  
Figure 1:4  
After an RBM has been trained, another RBM can be stacked on top of the first one. The hidden layer output of the first RBM is used as the visible layer input of the second RBM. Therefore, several layers of RBMs can be stacked to extract different features automatically, which represent gradually more complex structure in the data. In practice, we stack RBMs and train them with the greedy layer-wise unsupervised learning algorithm. During the training phase, each added hidden layer is trained as an RBM. When the DBNs have been trained, the network parameters \(\theta=(W,b,c)\) are used to initialize the weights of a multi-layer feed-forward neural network. The back propagation algorithm is used to fine-tune the network parameters \(\theta=(W,b,c)\) to improve the detect performance of the neural network. The DBNs structural model is shown in Figure 1:5  
Each BernoulliRBM (Bernoulli Restricted Boltzmann Machine) algorithm in DBNs is described in Algorithm 2. ```
0: Training dataset \(S=\left\{x^{(1)},x^{(2)},\cdots,x^{(N)}\right\}\), \(RBM(v_{1},\cdots,v_{m};h_{1},\cdots,h_{n})\), \(m\) is the number of visible units, \(n\) is the number of hidden units; learning rate \(\lambda\). 0: the RBM weight matrix \(W\); the bias vector \(b=(b_{1},b_{2},\cdots,b_{i},\cdots,b_{m})\) of the RBM visible layer, the bias vector \(c=(c_{1},c_{2},\cdots,c_{j},\cdots,c_{n})\) of the RBM hidden layer. 1: init: \(W_{ij}=b_{i}=c_{j}=\Delta W_{ij}=\Delta b_{i}=\Delta c_{j}=0,for\quad i=1, \cdots,m,j=1,\cdots,n\). 2:for\(iter=1,2,\cdots,T\)do
3:for all\(x^{(l)}\in S\)do
4:\(v^{(0)}=x^{(l)}\);
5:for\(t=0,1,\cdots,k-1\)do
6: for all hidden units \(j=1,2,\cdots,n\), do sample \(h_{j}{}^{(t)}\sim P(h_{j}|v^{(t)})\);
7: for all visible units \(i=1,2,\cdots,m\), do sample \(v_{l}{}^{(t+1)}\sim P(v_{l}|h^{(t)})\);
8:endfor
9:for\(i=1,2,\cdots,m,j=1,2,\cdots,n\)do
10:\(\Delta W_{ij}=\Delta W_{ij}+\lambda\cdot\left(P(h_{j}=1|v^{(0)})\cdot v_{i}^{ (0)}-P(h_{j}=1|v^{(k)})\cdot v_{i}^{(k)}\right)\);
11:endfor
12:for\(i=1,2,\cdots,m\)do
13:\(\Delta b_{i}=\Delta b_{i}+\lambda\cdot\left(v_{i}^{(0)}-v_{i}^{(k)}\right)\);
14:endfor
15:for\(j=1,2,\cdots,n\)do
16:\(\Delta c_{j}=\Delta c_{j}+\lambda\cdot\left(P(h_{j}=1|v^{(0)})-P(h_{j}=1|v^{( k)})\right)\)
17:endfor
18:endfor
19:\(W=W+\Delta W\)
20:\(b=b+\Delta b\)
21:\(c=c+\Delta c\)
22:endfor
23:return network parameters \(\theta=(W,b,c)\). ```  
**Algorithm 2** BernoulliRBM (Bernoulli Restricted Boltzmann Machine)  
Figure 1:6 DBNs structural model.
## 3 Case Study
### Ship Motions and Accelerations  
The influence of ship motions on a vessel's forces is significant and necessitates their inclusion in calculations [29]. Neglecting to account for these motions can lead to severe consequences, affecting the stability, structural integrity, and material responses of the ship. The transverse acceleration due to sway and the vertical acceleration due to heave, in m/s2, are to be taken from Bureau Veritas rules [30]. The summary of accelerations influencing the design of the mast is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Young’s Modulus (GPa)** & **Poisson’s Ratio** & **Density (kg/m3)** \\ \hline
200 & 0.3 & 7850 \\ \hline \end{tabular}
\end{table}
Table 2: Material properties of the steel.  
Figure 3: Bridge and deckhouse structure. (**a**) Front view, (**b**) side view, (**c**) top view.  
The previous formulation from the rule [30] enables fast calculations and skips advanced seakeeping calculations, i.e., with strip theories or panel methods. Nevertheless, these calculations might be developed in further stages of the ship design spiral in order to improve the previous calculations.
# A Novel 4 \(\times\) 1 MISO-VLC System with FBMC-OQAM Downlink Signals
## 4 Conclusions  
We have designed a novel VLC system with a 4 \(\times\) 1 MISO channel, which uses FBMC-OQAM to generate downlink signals. The results show that the FBMC-OQAM signal has better reception performance than the OFDM signal and greatly reduces out-of-band leakage. This system not only multiplies downlink bandwidth capacity but also the used FBMC-OQAM signals offer better reception quality compared with traditional OFDM signals. It has the potential advantages in future indoor VLC system applications.  
Conceptualization, Y.S. and Y.L.; methodology, Y.S., Y.L. and A.W.; validation, Y.Z., C.L. and P.C.; writing--original draft preparation, Y.L.; writing--review and editing, Y.S.; supervision and project administration, R.Z., J.Y. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61107064), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201901201), the Zhejiang Key Research and Development Project (2017C01043), the Chongqing Science and Technology Commission Foundation (cstc2018 jcyjAX0038 and cstc2016 jcyjA0246), the Chongqing Three Gorges Reservoir Area Geological Environment Monitoring and Disaster Warning Key Laboratory Open Fund Major Project (ZD2020A0104), and the postgraduate scientific research innovation project of Chongqing Three Gorges University (YJSKY22014).  
Not applicable.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest.
## 6 Future Research Directions
### Receiver Complexity in SIC Implementation  
In NOMA, the users are multiplexed at the transmitter side using pairing techniques and decoded at the receiver using the SIC receiver. As the number of users increases, the allocation of transmission powers to pair the users is very complex. At the same time, decoding the strong user to the next strong user and up to the weak user is a very difficult task at the receiver end. This process increases the latency and interferencewith the increased number of multiplexed users. Therefore, reducing the latency and interference and providing an efficient and dynamic SIC receiver for the NOMA system is a future challenge.
## 2 Preliminaries and Problem Description  
### Graph Theory  
In this work, a weighted directed graph is introduced to describe the connection relationship between agents: the agents are labeled by nodes, and the interactions by edges. Let \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathbf{\mathcal{A}})\) stand for a weighted directed graph with \(N\) nodes, where \(\mathcal{V}=\{v_{1},\cdots,v_{n}\}\) denotes the set of vertices and \(\mathcal{E}=\{(i,j),\ i,j\in\mathcal{V},\ \text{and}\ i\
eq j\}\) denotes the set of edges, respectively. The adjacency weight matrix \(\mathbf{\mathcal{A}}=[a_{ij}]\in R^{N\times N}\) is used to represent the communication topology among the agents. If the agent \(i\) can obtain information from the agent \(j\), then \(a_{ij}>0\); otherwise, \(a_{ij}=0\). The set of neighbors for agent \(i\) is denoted by \(\mathcal{J}_{i}=\{v_{j}:\ (v_{j},\ v_{i})\in\mathcal{E}\}\). In addition, the Laplace matrix of \(\mathcal{G}\) is denoted by \(\mathbf{\mathcal{L}}=\mathbf{\mathcal{D}}-\mathbf{\mathcal{A}}\), where \(\mathbf{\mathcal{D}}=\text{diag}\{d_{1},\cdots,d_{N}\}\) and \(d_{i}=\sum_{j=1}^{\mathcal{J}_{i}}a_{ij}\). The graph \(\mathcal{G}\) is connected if there is a path between any two vertices.  
The communication topology for one leader agent and \(N\) follower agents is defined as an extended directed graph \(\overline{\mathcal{G}}=(\overline{\mathcal{V}},\ \overline{\mathcal{E}})\) with \(\overline{\mathcal{V}}=\{v_{0},v_{1},\cdots,v_{n}\}\) and \(\overline{\mathcal{E}}\subseteq\overline{\mathcal{V}}\times\overline{\mathcal{ V}}\), where the index \(0\) represents the leader agent. Let the leader adjacency matrix be \(\mathbf{\mathcal{B}}=\text{diag}\{b_{1},\cdots,b_{N}\}\), where \(b_{i}>0\) means that the follower agent \(i\) can obtain the information of the leader agent, and otherwise, \(b_{i}=0\). It is supposed that at least one follower agent can obtain the information from the leader agent.  
**Assumption 1** ([39]).: _The directed graph \(\overline{\mathcal{G}}\) contains a spanning tree, and the leader node is the root node._
# Ultrasound-Based Smart Corrosion Monitoring System for Offshore Wind Turbines
## 1 Introduction  
Wind energy plays a significant role as a renewable energy source, urging the need for cost-effective renewable energy sources. Compared to onshore wind energy, offshore wind turbines benefit from two main advantages: higher mean wind speeds (modest increases in wind speed can result in doubling the generated power) and steadier wind supply, which makes power generation more reliable. These two factors combined have a dramatic effect on the Return on Investment (RoI) of the farm [1].  
In the last decade, we have witnessed a steady increase in the total installed capacity of offshore wind farms. By the end of 2019, 6.1 GW capacity has been newly installed. The total capacity installed by 2019, was 29.1 GW, which is a 10% increase with respect to 2018 [2]. Europe (UK, Germany, Denmark, Belgium) is leading by contributing 75% of total global offshore wind installation, as of the end of 2019, and Asian offshore wind energy production keeps growing significantly, lead by China and followed by Taiwan, Vietnam, Japan, and South Korea. Due to COVID-19, the overall expected wind capacity in 2020 was not met because of the challenges in project construction, execution activities, and investments. However, the Global Wind Energy Council (GWEC) has predicted that offshore wind energy will have a 20% of contribution to the global wind installation by the year 2025.  
Offshore structures are installed in relatively deep water and exposed to the harsh marine environment. This leads to an increase of the cost of construction and deployment compared to onshore structures [3] and higher operation and Maintenance (O&M) costs of installations compared to land or coastal-based structures due to the limitations in accessibility, manpower, special equipment requirements, etc. [4]. The added costs in the construction and deployment of offshore turbines were mitigated at the beginning as theturbines were installed mainly in coastal sites with shallow waters. In recent years, there has been a constant increase in the water depth and distance to shore for the offshore wind farms [5], as farms are located further away from shore (60 km) and as depth water reaches 200 m [6]. This leads to higher Capital Expenditure (CAPEX) and O&M costs that reduce the benefits of the farm [7]. Several foundations for wind turbines have been developed trying to reduce the construction and installation costs. Another key to the profitability of the wind farm is to improve the useful life of the wind turbines by maintaining the operational conditions of the towers to minimize the breakdowns or downtime of the turbines. Therefore, improving O&M models and tools and, at the same time, reducing costs have become key to maintain the profitability of offshore wind farms. Numerous research works providing different approaches that support directly or indirectly to improve O&M process of offshore wind turbines/farms have been discussed in Reference [8; 9; 10; 11; 12].
## 3 Materials and Methods  
Three analyses were conducted on the FEDEFL version 1.0.8 to test interoperability improvements. The analyses employed the same methods to match EFs or components of these flows as described in previous studies [10; 13]. The analyses used publicly available sources that were available to the authors. The first analysis was conducted to determine how capable the FEDEFL is at mapping to other LCA sources. Two databases and datasets (ecoinvent and USLCI), one software (openLCA), and two LCIA methods (TRACI and ImpactWorld+) with existing mappings to FEDEFL were selected for analysis. The number of unique flowables that were mapped were compared with the total number of unique flowables in each of the sources. In this analysis, unique flowables refers to the name of the flows, excluding any context or unit information.  
The second analysis was performed to determine the current interoperability between other LCA EF lists. The sources of EF lists is limited to either publicly available EF lists or EF lists that were available through previous and ongoing collaborative efforts, such as the critical review [10; 13]. The authors acknowledge that additional databases and datasets, software and LCIA methods exist, but were not included in the comparison. This comparison is not meant to be an exhaustive comparison of all LCA sources, but a representation of general issues between LCA sources. Flows from each list were compared to flows in every other flow list using name-to-name matching. For the purposes of simplicity, context information was not included when matching flows, and only unique flows (i.e., flowables) were used from each list. Ten sources were selected for this analysis: four databases and datasets, three pieces of software and three LCIA methods. Table 3 provides the ten source names and versions that were used for this analysis  
Lastly, an analysis was conducted to use the FEDEFL as an intermediate flow list in mapping LCI databases to LCIA methods. Flow lists from two LCI databases--USLCI and ecoinvent--and lists from three LCIA methods were used in the analysis. The versions of the LCI and LCIA sources used are listed in Table 3. For this analysis, the source lists were both mapped to FEDEFL and then compared to see if there was an improvement in matching capabilities between the original source to source matching and the matching using the FEDEFL as a common EF list.  
\begin{table}
\begin{tabular}{l l l} \hline
**Source Type** & **Source Name** & **Version** \\ \hline Databases and datasets & USEEIO & v1.1 [37] \\ Databases and datasets & USLCI & Q4v2019 [26] \\ Databases and datasets & IDEA & v2.3 [38] \\ Databases and datasets & ecoinvent & v3.6 [39] \\ Software & openLCA & Reference flow list (2020) [28] \\ Software & SimaPro & Used in 2018 GLAD critical review [10] \\ Software & GaBi & Used in 2018 GLAD critical review [10] \\ LCIA methods & TRACI & v2.1 [31] \\ LCIA methods & ReCiPe & 2016 release [32] \\ LCIA methods & ImpactWorld+ & [33] \\ \hline \end{tabular}
\end{table}
Table 3: Flow list analysis sources.
## 6 Conclusions  
In this study, an improved spectral angle feature based on SAM was proposed. The apple orchards in the study area were classified using GEE, which is computationally powerful and has strong online visualization and computational analysis capabilities. The classification results showed that this feature could effectively improve the identification accuracy of apple orchards in the study area. In summary, the improved spectral angle feature proposed in this study can effectively identify apple orchards in the Zhaotong City area under conditions of complex terrain topography with cloudy and rainy terrain by combining RGB Bands, NDVI, and texture features (GLCM). The specific findings are as follows:  
(1) In this study, using Sentinel-1 and Sentinel-2 images as data sources, the RGB Bands, red-edge features, NDVI, texture features, and improved spectral angle features were used in the GEE platform to identify apple orchards in the study area using the RF algorithm. By comparing the classification accuracy of different classification features, this study observed that the texture features facilitated the identification of apple orchards and could not effectively identify other land classes. The combination of the RGB band and spectral angle features can effectively improve the extraction accuracy and overall accuracy of apple orchards, which are 80.3% and 67.17%, respectively, compared to the single RGB band classification. When the RGB Bands were combined with NDVI, GLCM, and spectral angle features, the apple orchard extraction accuracy and overall accuracy reached their best values of 88.89% and 84.44%, respectively.  
(2) The research method used in this study can provide a reference for recognition tasks in complex terrains and has practical value. However, when constructing the spectral angle features, the VV and VH bands of the 12 months of SAR images have a certain redundancy, and the study area contains many mixed image elements, resulting in the \"pretzel phenomenon\" in the study results, which has a certain impact on the extraction accuracy of apple orchards. Therefore, the VV and VH bands, which have more obvious differences from other land types, can be considered in constructing spectral angle features to reduce redundancy, reduce computational effort, and improve the separability of spectral angle features. Concurrently, in subsequent work on identifying apple orchards in the entire Zhaotong City area, other data products with higher accuracy can be used to complete the selection of samples without manual intervention to avoid misclassification of land types due to visual interpretation.  
Conceptualization, J.L., W.S. and X.Z.; software, X.Z. and D.Z.; validation, Q.W.; resources, W.S. and J.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L. and W.S.; supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Yunnan Province Key Research and Development Program (No. 202202 AD080010).
## 5 Attacks and Countermeasures
### Attacking MANETs  
MANETs are vulnerable to several attacks due to their decentralized and wireless characteristics. In the following, we present selected attacks on MANETs that affect both the routing algorithm and the data transmission. We further discuss _Sybil_ attacks in the context of MANETs.
# Residential Buildings Complex Boundaries Generation Based on Spatial Grid System
## 6 Discussion
The area of interest is a specific type of building boundary generator with a capacity for creating numerous design variations. The uses for these results are numerous in the specific fields of architecture, gaming, generative design, etc. For example, the most common of the real-life complex building boundary has borders with the number of edges that are mostly between the minimal and average results of our algorithm. The analyzed buildings are not average but rather common examples of the real-life set mainly because the basic rectangular and simple shape boundaries are more commonly seen than the complex ones. Although not such common, buildings with complex boundary shapes are the mainstream of the presented approach. The internal function-based approach usually results in basic and simple shapes of boundary layers. On the contrary, contemporary architecture is sometimes strongly influenced by outer factors like adapting to the force of the wind or the increase of daylight insulation or even dominant visual message making the building a recognizable environmental marker. Concerning system sciences, the general specification of any system includes functional and non-functional requirements, where the latter addresses the constraints under which the former have to be applied. The implementation of non-functional requirements is generally much more demanding. The trend of automatic generation of residential buildings applies not just in the field of architecture, but also in the media and video game industries where flexible rule-based rapid model generation is essential. The is that the functionality is not a key component in the digital and video game industry residential building simulations. The emphasis is on the recognizable visual style and game narrative. The form of the building acts as a container for the interior space but it is not solely defined or influenced by it. This was the main reason to ignore the interior functionality in the proposed approach in order to enable the rapid generation of building boundary variations into which the interior functionality may be included afterward.