esa-sceva/satcom-chunk-collection huggingface dataset:
# A Network Analysis-Based Approach for As-Built BIM Generation and Inspection
## 2 Literature Review
### Research Gaps and Research Objectives  
The major limitations of existing studies and the corresponding objectives are recognised as follows:  
(1) Prior domain knowledge related to civil engineering is required for traditional manual or vision-based methods, resulting in the difficulty and inconvenience of the identification of specialised features and parameters during BIM inspection and comparison.  
The proposed approach aims to simplify the identification process by utilising network science, making it accessible to users without extensive B&C backgrounds. This will greatly facilitate the adoption of advanced and interdisciplinary technologies.  
(2) The performance of machine learning-related approaches is highly dependent on appropriate parameter settings, and wrong parameter settings result in very poor algorithm performance. This procedure is complicated and redundant for multiple implementations for different scenarios.  
The adoption of network-related properties in the new method significantly reduces the sensitivity to parameter tuning and simplifies implementation across diverse scenarios, making the process more robust and less dependent on precise parameter adjustments.  
(3) Previous studies mainly focused on element-to-element comparisons without global feature consideration, adversely affecting analysis efficiency and operation time. Neighbours of critical, abnormal components are more prone to generate misidentifications.  
Our approach considers the correlations and relationships among building elements by conducting topological analyses, improving comparison efficiency and reducing computational resource consumption.
## 2 Data and Methods
### Data and Pre-Processing
#### 2.2.2 MODIS LAI Product  
As an essential parameter to characterize the biophysical changes and canopy structure of vegetation, LAI directly impacts the transpiration efficiency, photosynthesis, and energy  
Figure 1: (a) Location of the study area with DEM, (b) land cover type (2020).  
balance of vegetation [21]. We obtained the 8-day MODIS LAI product (MOD15A2) with a spatial resolution of 500 m for the 2000-2020 period from the Land Processes Distributed Active Archive Centre (LP DAAC) at [[https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac](https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac)]([https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac](https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac)) (accessed on 2 June 2021). According to the seasonal characteristics of vegetation growth in the study area, plants enter dormancy from November to March of the following year [31; 32]. Thus, we used LAI from April to October of each year. Based on the interface provided by ArcGIS 2.7 and the Python platform, the 8d LAI was synthesized into monthly data using the average method.
# Random Routing Algorithm for Enhancing the Cybersecurity of LEO Satellite Networks
###### Abstract  
The recent expansion of networks of low-earth orbit (LEO) satellites such as Starlink, OneWeb, and Telesat and the evolution of communication systems toward B5G and 6G with densely interconnected devices could generate opportunities for various cyber attacks. As the satellite network offers many crucial services to the public and governmental organizations, cyberattacks pose severe risks to the communication infrastructure. In this study, we propose a random routing algorithm to prevent distributed denial-of-service (DDoS) attacks on an LEO satellite constellation network. The routing algorithm utilizes the classical algorithms, i.e., k-DG, k-DS, k-SP, and k-LO, by introducing randomness and selecting one with weighted probability distribution to increase the uncertainty in the algorithm. The study shows that the proposed random routing algorithm improves the average and median cost of the attacker against DDoS attacks while maintaining the functionality of the network. The algorithm is optimized by formulating a Bayesian optimization problem. In addition to providing an additional level of uncertainty in the routing, there is an improvement of 1.71% in the average cost and 2.05% in the median cost in a typical scenario. The algorithm causes the network to be robust to cyber attacks against LEO Satellite Networks (LSNs), however, similar to any other defensive measures, it reduces the network's goodput.  
Keywords:cybersecurity; distributed-denial-of-service attacks; LEO satellite network; routing algorithm +
Footnote †: journal: Electronics Electronics
_Sustainability_**2014**, \(6\), 6250-6266; doi:10.3390/su6096250  
_Article_  
Model Estimation of Water Use Efficiency for Soil Conservation in the Lower Heihe River Basin, Northwest China during 2000-2008  
[PERSON] 1,\({}^{\dagger}\), [PERSON] 1,\({}^{\star}\), [PERSON] 2,\({}^{\dagger}\) and [PERSON] 3  
1 State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China; E-Mail: [EMAIL_ADDRESS]
2 College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China; E-Mail: [EMAIL_ADDRESS]
3 Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-6483-3471; Fax: +86-10-6485-6533.  
_Received: 29 April 2014; in revised form: 3 August 2014 / Accepted: 4 September 2014 / Published: 12 September 2014_  
There has been very limited research on water use efficiency for soil conservation (WUE-SC) in typical water scarce regions such as the lower Heihe River Basin, where there is serious wind erosion and the soil conservation service plays a key role in guaranteeing the ecological safety of Northern China. The soil conservation service, which was represented by the soil conservation amount (SC), was first estimated with an experiment-based model in this study. Then, the WUE-SC (_i.e._, SC/ET) was calculated on the basis of evapotranspiration (ET) data, and management implications were finally discussed. The results indicated the WUE-SC ranged between 0-98.69 t mm\({}^{-1}\), and it first decreased and then increased on the whole during 2000-2008. Besides, the inter-annual variation of WUE-SC was mainly due to change in the potential soil loss. In addition, the WUE-SC showed significant spatial heterogeneity, and the average WUE-SC of the whole study area was very low due to spatiotemporal inconsistency between the potential soil loss and the vegetation coverage rate. Although there are some uncertainties, these results still can provide local managers with valuable information for water resource utilization and ecosystem management to improve water use efficiency.  
**Keywords:** wind erosion; soil conservation; water use efficiency; ET; ecosystem services; water resources management; lower Heihe River Basin
###### Abstract  
The valorization of food industry by-products is still a major challenge. Here, we report the production of acid whey fermented beverages stabilized with flaxseed gum (derived from oil industry by-product). Four variants of drinks were prepared: (1) fermented whey (W), (2) fermented whey with milk powder added (5% \(w/v\)) (WMP), (3) fermented whey with flaxseed gum added (0.5% \(w/v\)) (WFG1) and (4) fermented whey with flaxseed gum added (1.0 % \(w/v\)) (WFG2). The beverages were kept in refrigerated conditions (5 \(\pm\) 1 \({}^{\circ}\)C) for 28 days. Alterations in lactic acid bacteria population, pH, titradable acidity, water activity, syneresis, viscosity, acetaldehyde content, color, consumer acceptance, bio-active compounds and antioxidant activity were identified. The findings revealed that flaxseed gum addition significantly enhanced bacteria survivability and improved the viscosity of acid whey at a level comparable with milk powder, meeting consumer acceptance criteria. The beverages were characterized by normative physicochemical properties and showed high antioxidant activity and free amino acids level. The use of valuable by-products from the dairy and oil industries opens up a promising route for the production of innovative beverages, which is in accordance with the principles of circular economy and the idea of zero waste.  
by-products; flaxseed; whey; natural thickeners; fermentation; zero waste +
Footnote †: journal: Applied Sciences
## 3 Core Technologies and Algorithms  
### Measurement of the Direction of the Dual PSD-Based Unit's Incident Beam  
Dual PSD-based units were previously proposed in [15]. In the dual PSD-based unit proposed here, the custom beam splitter (BS) consisted of two BSs, both with coplanar reflecting surfaces. The incident light reflected by the custom BS traversed a refraction offset piece as thick as the first BS and then entered PSD I. The portion of the incident light that was transmitted through the custom BS was partly reflected by a CC and received by PSD II.  
In Figure 3, it is seen that the CC joint was located at the origin of the rectangular coordinate system. The cube's center line (i.e., the Z-axis) and the custom BS's reflecting surfaces were fixed at an angle of 45\({}^{\circ}\). \(L_{1}\) is the distance between the CC joint and the center point of the custom BS (i.e., the point of intersection between the Z-axis and the custom BS's reflecting surfaces). \(L_{2}\) and \(L_{3}\) are the distances between the Z-axis and PSD I and PSD II, respectively. \(L_{4}\) and \(L_{5}\) represent the distances between the CC joint and the center points of PSD I and II, respectively. The custom BS's reflecting surfaces and PSDs were all perpendicular to the X-Z plane, assuming that \((y_{1},z_{1})\) and \((y_{2},z_{2})\) were the coordinate values of the beam spots on PSD I and II, respectively. It is important to note that \((y_{1},z_{1})\) and \((y_{2},z_{2})\) are absolute coordinate values, whereas a PSD only measures its own values along its own Y- and Z-axes. Thus, \(z_{1}\) and \(z_{2}\) should actually be \(z_{1P}+L_{4}\) and \(z_{2P}+L_{5}\) in the rectangular coordinate  
Figure 2: Proposed point spread function (PSF) measurement setup and light routes (red/purple arrows).  
system of Figure 3, respectively, where \(z_{1P}\) and \(z_{2P}\) are, respectively, the values measured by PSD I and II. In addition, the centers of PSD I and II can deviate from the X-Z plane; here, they are expressed as \(L_{6}\) and \(L_{7}\), respectively. Thus, the values of \(y_{1}\) and \(y_{2}\) should be \(y_{1P}+L_{6}\) and \(y_{2P}+L_{7}\) in the rectangular coordinate system of Figure 3, respectively, where \(y_{1P}\) and \(y_{2P}\) are the values measured by PSD I and II.  
In the rectangular coordinate system of Figure 3, the components of the incident beam unit vector \(<\)\(m\), \(n\), \(q\)\(>\) that arrive at the dual PSD-based unit are:  
\[\begin{array}{l}m=\frac{M_{2}-F}{\Delta}\\ n=-\frac{M_{1}-Q}{\Delta}\\ q=-\frac{E}{\Delta},\end{array} \tag{1}\]  
where \(2L_{1}+L_{3}-L_{2}=E\), \(L_{6}+L_{7}=Q\), \(2L_{1}-L_{4}-L_{5}=F\), \(y_{1P}+y_{2P}=M_{1}\), \(z_{1P}+z_{2P}=M_{2}\), and \(\Delta=\sqrt{E^{2}+\left(M_{1}-Q\right)^{2}+\left(M_{2}-F\right)^{2}}\). \(E\), \(Q\), and \(F\) are parameters of the dual PSD-based unit, and \(M_{1}\) and \(M_{2}\) are the values obtained by PSDs I and II, respectively. The parameters \(E\), \(Q\), and \(F\) can be calibrated by the auto-reflection alignment method [17]. In addition, it is clear that the translation motion of the incident beam can be measured by PSD I and II in the dual PSD-based unit.
## 4 Experiments and Results
### Disclosure Risk  
To measure the disclosure risk, we compare the support distribution difference between the patterns obtained without a privacy mechanism, using the classical _PrefixSpan_ algorithm, compared to the patterns obtained by the privacy mechanisms described above. Therefore, this metric quantifies the certainty of the obtained patterns.  
Thus, if the patterns are known with high certainty, it can be assumed that the adversary's knowledge is more accurate to perform an inference attack. To assess the disclosure risk, we used the Jensen-Shannon divergence, which is computed between the support distributions of the patterns obtained by the _PrefixSpan_, _DP-FSM_, and _graph-based_ algorithms. Intuitively, when the Jensen-Shannon divergence is close to zero, the difference between the patterns with and without noise is minimal, and the disclosure risk is maximized. Conversely, the greater the Jensen-Shannon divergence, the greater the difference between the patterns with and without noise is, minimizing the disclosure risk.  
Figure 3 depicts the results of the disclosure risk assessment. In this case, we considered all the extracted patterns to calculate the Jensen-Shannon divergence. Figure 3A shows that the DR is larger for the _DP-FSM_ than for the _graph-based_ algorithm, for \(\varepsilon\) values from \(\varepsilon=0.05\), while it is smaller only for \(\varepsilon=0.01\) for the German and NYC datasets. This illustrates that our mechanism improves the modulation of the disclosure risk.  
Figure 2: Information loss measured as the relative error. Where (**A**) _DP-FSM_ Bank, (**B**) _Graph-based_ Bank, (**C**) _DP-FSM_ German, (**D**) _Graph-based_ German, (**E**) _DP-FSM_ NYC, and (**F**) _Graph-based_ NYC.
## 3 Methods
### HVSR Inversion  
We considered a one-dimensional two-layer soil system with a dominant and sharp acoustic impedance contrast below a shallow surface layer at depth \(d\). Let the frequency \(f_{0}\) be the main peak in the HVSR curve, i.e., the lowest dominant peak frequency. The \(f_{0}\) can be associated to the fundamental seismic resonance frequency \(f_{reson}\) related to the interference of transmitted and reflected from the interface of impedance contrast waves. The seismic resonance frequency of the \(n\)-th mode is related to the shear velocity \(v_{\rm s}\) and thickness of the overlying layer \(d\) as follows [21]:  
\[f_{reson}=(2n+1)\frac{v_{\rm s}}{4d} \tag{2}\]  
where frequency is given in Hz, shear waves velocity in m/s, and layer thickness in m.  
If we know the value of the shear wave velocity from the results of the field work or from reference materials, and the value of the resonant frequency from the HVSR curves, then we can easily estimate the depth of the contrast boundary using the Formula (2). The value of shear wave velocity, density of the surface layer soil and the underlying half-space, as well as the thickness of the surface layer estimated using Formula (2), can be used to construct the subsurface soil model, which in turn, along with HVSR curves, can serve as initial data for optimization by inversion methods.  
In this paper, HVSR inversion was conducted by OpenHVSR software [38]. The inversion strategy is based on the Monte Carlo method, where at every iteration a randomly perturbed version of the best-fitting model is produced and used to compute a set of simulated curves to be compared with the experimental HVSR curves. The forward simulation of the theoretical transfer function curves is based on the approach of [39]. A criterion to decide which simulated curve best reproduces the experimental data is based on calculating the value of an objective function, which is a positive, real-value function of the subsurface parameters, and the misfit between the data and the simulated curves, and seeking its global minimum.
## 2 Materials and Methods
### Datasets Used in the Study
#### 2.1.1 Global Land Mapping XCO\({}_{2}\) Data Derived from GOSAT Retrievals
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Regions** & **Long Name** & **Latitude** & **Longitude** & **Description** & **PCC with NDVI** & **PCC with LST** \\ \hline FnC & Forest in Northern Canada & 50\({}^{\circ}\)–57\({}^{\circ}\)N & 86\({}^{\circ}\)–99\({}^{\circ}\)W & F: 63\% & –0.61 & - \\ \hline FeR & Forest in Eastern Russia & 52\({}^{\circ}\)–59\({}^{\circ}\)N & 117\({}^{\circ}\)–130\({}^{\circ}\)E & F: 86\% & –0.68 & - \\ \hline CwR & Cropland in Western Russia & 50\({}^{\circ}\)–57\({}^{\circ}\)N & 33\({}^{\circ}\)–46\({}^{\circ}\)E & C: 70\% & –0.50 & - \\ \hline CnC & Cropland in Northern China & 35\({}^{\circ}\)–42\({}^{\circ}\)N & 107\({}^{\circ}\)–120\({}^{\circ}\)E & C: 58\% & –0.80 & - \\ \hline CnI & Cropland in Northern India & 19\({}^{\circ}\)–26\({}^{\circ}\)N & 74\({}^{\circ}\)–87\({}^{\circ}\)E & C: 89\% & –0.79 & 0.78 \\ \hline GeA & Grassland in Central Africa & 5\({}^{\circ}\)–12\({}^{\circ}\)N & 15\({}^{\circ}\)–28\({}^{\circ}\)E & C: 36\% & -0.80 & 0.79 \\ \hline CeB & Cropland in Eastern Brazil & 8\({}^{\circ}\)–15\({}^{\circ}\)S & 40\({}^{\circ}\)–53\({}^{\circ}\)W & C: 43\% & \multirow{2}{*}{-} & 0.49 \\ \hline FsA & Mixed forest in Southern Africa & 10\({}^{\circ}\)–17\({}^{\circ}\)S & 20\({}^{\circ}\)–33\({}^{\circ}\)E & F: 64\% & - & 0.59 \\ \hline SnA & Shrub-land in Northern Australia & 11\({}^{\circ}\)–18\({}^{\circ}\)S & 124\({}^{\circ}\)–137\({}^{\circ}\)E & G: 60\% & - & 0.47 \\ \hline \end{tabular}
\end{table} 
Table 2: Specifications of the selected regions of interest including the location, major vegetation types and its area proportion, and averaged Pearson Correlation Coefficients (PCCs) of seasonal variation between GM-XCO\({}_{2}\) and NDVI or LST over each region of interest. Several regions of interests are selected for NDVI and LST, respectively. larger than 2.0 ppm are not used in this study. Accuracy assessment of this mapping data is presented in Section 4.1. With high resolution in space and time, this unique global land mapping dataset of XCO\({}_{2}\) make it possible for both spatial and temporal comparison analysis at a fine scale with other satellite-observed parameters. As described in [PERSON] et al. [19], the mapping result and its data uncertainty from this data-driven gap-filling method rely heavily on the original XCO\({}_{2}\) retrievals. Therefore, any possible retrieval artifacts due to retrieval errors will be propagated into the mapping data. More description on the mapping data uncertainty will be discussed in Section 4.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
###### Abstract  
The marine environment's complexity poses considerable difficulties for the stability and reliability of communication links. The restricted coverage of onshore base stations in marine areas makes relay technology a critical solution for extending the communication coverage. Here, connectivity analyses help nodes select the optimal forwarding links, reducing transmission failures and improving the network performance. However, the rapid changes in marine wireless channels and the complexity of hydrological conditions make it challenging to acquire precise channel state information (CSI). In particular, dynamic environmental factors like tides, waves, and wind speed lead to substantial variations in the channel parameters over time. In response to these challenges, this paper puts forward a ship-to-shore communication system using relay ships to extend the coverage of terrestrial base stations. A novel channel modeling method is designed to capture the characteristics of marine wireless channels accurately. Additionally, a machine learning (ML)-based approach is introduced to predict the dual-hop link connection probability at future time points by analyzing historical time-series data on oceanic environmental and ship movement parameters. The proposed model consists of a convolutional-layer-based feature extractor and a bidirectional long short-term memory (BiLSTM) estimator. The CNN module extracts effective high-level features from the input data, while the BiLSTM module further explores the dependencies and dynamic patterns along the temporal dimension. The attention mechanism is introduced to distinguish the importance of the information through a weighted approach. The experimental results show that compared to traditional methods and other deep learning approaches, the proposed CNN-BiLSTM-AM model performs better in terms of its prediction accuracy and fitting ability. The model's mean squared error (MSE) is as low as 0.0126.  
m 202  
networks [1]. To extend the coverage of terrestrial networks, relay technology has been introduced, allowing vessels to access base stations via multi-hop wireless communication. However, marine communication link systems face numerous challenges, such as complex geographical environments, varying climatic conditions, and a sparse user distribution, all of which can lead to frequent link disconnections and consequently affect the quality of communication [2]. In this context, predicting link availability has become an important tool for optimizing the network performance. This assists in providing intelligent routing choices for relay access schemes, reducing redundant transmissions, and improving the communication stability.
## 4 Research Results
### Influence of Rainwater on Measurement Using the UPV Testing Method
#### 4.1.2 The Testing Slab V2 UHPFRC  
Results of ANOVA for the second slab are summarized in Table 5 and Figure 10.  
The ANOVA showed that the hypothesis of the same means was not rejected (\(p\)-value is greater than the significance level 0.05).
## 2 Survey of Commercial Small Platforms
### Passive Microwave
#### 3.1.2 Microwave Sounders (MWS)  
In the last few years, intensive work has been conducted to develop missions to prove the feasibility of using microwave sounders on nano-platforms, such as MicroMas [32], and the Earth Observing Nanosatellite-Microwave (EON-MW) [33]. The measurement with gaps that can be analyzed with this technology is the atmospheric pressure over the sea surface.  
Table A5 presents a survey of the representative current and future missions with microwave sounders capable of measuring the atmospheric pressure over the sea surface. The gaps for this variable are the revisit and the latency times. To fill these gaps, a constellation of microwave sounders based on CubeSats missions could observe fast weather phenomena requiring high revisit time (3 h or less). A good example of CubeSat mission is EON-MW. The payload is a dual-reflector radiometer with a mass of 4 kg, an antenna size of 11 cm, and spatial resolution of 30 km on altitude of 600 km at 54 GHz.
## 2 Methodology and System Architecture
### Communication and Interfacing  
In the course of the implementation within the CPSs context and to ensure hardware compatibility with Industry 4.0, different system extension approaches were investigated analogous to the extension by a microcontroller board that integrates the PLC S7-1200 with the KRC4 through LabVIEW API, enabling communication to-and-from both manufacturing hardware, as well as cloud-devices interactions. Figure 6 illustrates the system interaction.  
Figure 5: IBM Watson menu.  
The challenge behind the network is the ability to communicate with both the PLC and the KUKA, without using their respective accessories, in other words, connecting the PLC using its S7 protocol to the KRC4 through external applications. Currently, there is no standard protocol released by KUKA and Siemens available for mutual usage (i.e., outside their specific environment). Communications between devices are established using a network protocol analyzer _Wireshark_, and then a library is written to read/write PLC data blocks, KRC4 variables, and data structures by using the observed protocol.