esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 3 Status Quo of Key Technologies of Smart Water Conservancy
### Big Data and Artificial Intelligence Technology  
Big data and artificial intelligence are the main technical support for digital twins to realize the functions of cognition, diagnosis, prediction and decision making [39]. Big data has \"5V\" characteristics, that is, Volume, Velocity, Variety, Value and Veracity [40]. The integration of different data, the iteration of native data and lifetime data, will produce greater data representation and higher data value. Artificial intelligence and big data are interrelated fields. AI requires large amounts of data for training and optimization, while big data requires AI algorithms and techniques to process and analyze the data. In the field of water, machine learning has been continuously developed and applied. It leverages big data, advanced algorithms and sensor technology to enable intelligent analytics and predictions.  
Studies have shown that machine learning can monitor water quality in real time [41], predict change trends [42], improve the efficiency of water conservancy systems, and improve flood vulnerability assessment [43], for example, water quality elasticity prediction using multiple algorithms [44], or flood prediction through support vector machines [45]. In addition, the improved neural network structure also improves the accuracy of water quality prediction [46]. Through AI, the water sector can better process large-scale data, uncover hidden patterns and trends, and support data-driven decisions. In the water conservancy digital twin system, the digital twin senses the real-time data from the physical entity and is trained by various artificial intelligence algorithms to complete the tasks of diagnosis, prediction and decision making. Taking the digital twin-enabled water plant as an example, the key parameters of different sections of the water plant are monitored by a digital twin engine, and the parameters of the process equipment are optimized in real time by combining intelligent models.
## 3 Results and Analysis
### Combination of GPS, GLONASS, and BDS for PPP Solutions  
For the GPS based PPP solution, the addition of other satellites in processing will enhance the geometric strength, resulting in improvement of the positioning accuracy potentially. The performances of combined PPP are mainly guaranteed by GPS observations as to our investigation, and it will be shown later. In this section, we consider solutions of ten cases: GPS only, GPS + BDS, GPS + GLONASS, GPS + GLONASS and BeiDou IGSO, MEO, GPS + BDS GEO, GPS + BDS IGSO, GPS + BDS MEO, GPS + BDS GEO and IGSO, GPS + BDS GEO and MEO, and finally GPS + BDS IGSO and MEO. The corresponding abbreviations can be seen in Table 2. The performances of static and kinematic solutions for all sites are analyzed and compared. The analysis is still based on the convergence time and positioning accuracy.
## 9 Security and Privacy Risk Controls
### Review of Security and Privacy Controls  
The aim at this stage is to present a list of reasons which can cause a control to fail. During the review, the following considerations need to be taken into account in order to identify the cause of the failure:  
* The control was not properly implemented according to the implementation guidelines outlined in Appendix B. In that case the developer needs to implement the control again according to the implementation guidelines.
* Appropriate control was not selected for addressing the threats and/or vulnerabilities. If the appropriate control is not available in Appendix B, then analyze external sources such as NIST 800-53, ISO 27005, OWASP and blogs for appropriate control and implementation details.
* The developer did not follow appropriate secure coding practices during implementation.
# Analysis of the Energy Efficiency of a Pneumatic Booster Regulator with Energy Recovery
## 5 Results and Discussion
### Comparison of VBA and VBA-R
#### 5.4.2 Comparison of Energy Efficiency  
To verify that the recovery energy helps improve the energy efficiency, the VBA and VBA-R were compared as shown in Figure 20. It is clear that, as the supply pressure increases, the energy efficiency decreases. However, with the help of the recovery chamber, the efficiency of the VBA-R is always 5-10% higher than that of the VBA. Once again, this proves the performance improvement of the VBA-R.
# Fast and Efficient Lunar Finite Element Gravity Model
## 3 Representations of Lunar Potential Using Orthogonal Finite Elements
### Sampling: Radii and Shells  
To help illustrate a sample FEM grid, consider a spherical grid with radius \(R_{m}\) (i.e., a sphere with the radius of the Moon at the lunar equator). This sphere is then covered by a 2-D mesh from \(0\leq\lambda\leq 360^{\circ}\) and \(-88^{\circ}\leq\phi\leq 88^{\circ}\). It is good to note that the poles are neglected in this routine due to the limitations of the original SH model. As discussed, the poles of astronomical bodies are treated as singularities in SH modeling. However, the FEM model can be used to approximate the surface gravity at the poles using the Cartesian coordinates. This is implemented in later approximation models. The following section allows us to discuss the process of finding the most efficient initial conditions like shell size and radius sample.
# Advances in the Study of Bioactive Compounds and
###### Abstract  
This review examines the nutritional composition, bioactive compounds, and potential health benefits of goji berries (_Lycium barbarum_ L.). Goji berries contain significant amounts of carbohydrates (46-87% dry weight), proteins (5.3-14.3% dry weight), and dietary fiber (3.63-16 g/100 g fresh weight). They are rich in micronutrients, including vitamin C (2.39-48.94 mg/100 g fresh weight) and potassium (434-1460 mg/100 g fresh weight). The berries' unique polysaccharides, particularly _Lycium barbarum_ polysaccharides (LBPs), exhibit molecular weights ranging from 10 to 2300 kDa. Flavonoids, such as quercetin and rutin (1.0-1.3 mg/g dry weight), and carotenoids, especially zeaxanthin (0.5-1.2 mg/g dry weight), contribute to the berries' antioxidant properties. In vitro and animal studies have demonstrated various health benefits, including antioxidant, anti-inflammatory, and immunomodulatory effects. However, more human clinical trials are needed to confirm these findings. The review also highlights the impact of geographical origin, cultivation practices, and processing methods on nutrient composition, emphasizing the need for standardization in research and commercial applications. Future research should focus on the bioavailability, metabolism, and potential synergistic effects of goji berry compounds.  
Keywords: polysaccharides; antioxidants; phytochemicals; nutraceuticals; functional food +
Footnote †: journal: Applied Sciences
## 5 Main Results
### Verification of Robustness  
As shown in Figures 3 and 4, to evaluate the robustness of the models, we conduct experiments over 30 times for each model and visualize the results using a violin plot. Exper  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Agenda**} \\ \cline{2-5}  & **Turns** & **Match** & **Inform** & **Success** \\ \hline GP-MBCM [37] & 2.99 & 44.29 & 19.04 & 28.9 \\ ACER [38] & 10.49 & 62.83 & 77.98 & 50.8 \\ PPO [33] & 9.83 & 69.09 & 83.34 & 59.1 \\ ALDM [39] & 12.47 & 62.60 & 81.20 & 61.2 \\ GDPL [23] & 7.64 & 83.90 & **94.97** & 86.5 \\ \hline
**VRB (Ours)** & **7.59** & **90.87** & 90.97 & **90.4** \\ \hline _Human_ & 7.37 & 95.29 & 66.89 & 75.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results on agenda-based user simulators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**VHUS**} \\ \cline{2-5}  & **Turns** & **Match** & **Inform** & **Success** \\ \hline GP-MBCM [37] & - & - & - & - \\ ACER [38] & 22.35 & 33.08 & 55.13 & 18.6 \\ PPO [33] & **19.23** & 33.08 & 56.31 & 18.3 \\ ALDM [39] & 26.90 & 24.15 & 54.37 & 16.4 \\ GDPL [23] & 22.43 & 36.21 & 52.58 & 19.7 \\ \hline
**VRB (Ours)** & 20.96 & **44.93** & **56.93** & **20.1** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results on VHUS-based user simulators.  
imental results show that our proposed method outperforms PPO in every metric, despite some negative outliers, and has a much lower standard deviation than PPO. An example of a dialogue session comparison between VRB and PPO is available in Table 4.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**VRB** & **PPO** \\ \hline U: \{‘attraction-inform-area-1’: ‘south’\} & U: \{‘attraction-inform-choice-1’: ‘8’, ‘attraction-request-type-7’: ‘?\} \\ U: \{‘attraction-request-post-?’: ‘?’, ‘attraction-request-phone-?’: ‘?’, ‘attraction-request-addr-?’: ‘?’, ‘attraction-request-type-?’: ‘?’, ‘attraction-name-1’: ‘the junction’, ‘attraction-recommended-name-1’: ‘cliftion way’\} \\ U: \{‘attraction-request-post-?’: ‘?’, ‘attraction-request-phone-?’: ‘?’, ‘attraction-request-fee-?’: ‘?’, ‘attraction-request-type-?’: ‘?’, ‘attraction-inform-free-1’: ‘?’, ‘attraction-inform-phone-1’: ‘0122351151’, ‘attraction-inform-port-1’: ‘cb17 gx’\} \\ U: \{‘attraction-request-type-?’: ‘?’\} \\ S: \{‘attraction-inform-type-1’: ‘museum’\} \\ U: \{‘hotel-inform-price-1’: ‘cheap’, ‘hotel-inform-area-1’: ‘centre’\} \\ S: \{‘hotel-inform-name-1’: ‘alexander bed and breakfast’\} \\ U: \{‘hotel-request-post-?’: ‘?’, ‘hotel-request-phone-?’: ‘?’\} \\ S: \{‘general-reymore-none-none’: ‘none’, ‘hotel-inform-phone-1’: ‘01223525725’, ‘hotel-inform-post-1’: ‘cb12 de’\} \\ U: \{‘hotel-inform-stays’\}: ‘don care’, ‘hotel-inform-day-1’: ‘don care’, ‘hotel-inform-people-1’: ‘dont care’\} \\ S: \{‘booking-book-ref-1’: none’\} \\ U: \{‘general-bye-none-none’: ‘none’\} \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison between VRB and PPO with respect to the dialogue act.
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 6 Discussion  
We did a set of evaluations in this work and demonstrated the effectiveness of RDFID to detect human motion of different moving patterns using WiFi signals. However, there are still some limitations in RDFID. In this section, we will give a discussion about the limitations and potentials of RDFID.  
Although the approach can achieve a high intrusion detection accuracy, it may be influenced by several factors.  
First, the relative location of the intruder and transceivers can affect the detection accuracy. There exists a relationship between the impact of the intruder to the signal transmission and the distance of the intruder to the transceivers. When the intruder moves far away from the transceivers or the first Fresnel zone, it becomes more difficult to extract effective features from the collected CSI of the ambient wireless signal. As a result, the detection accuracy suffers a degradation when the distance of the intruder to the transceivers.  
In addition, in real scenarios there may exist more than one intruder. Nevertheless, the movement of multiple intruders will break the periodicity of the received CSI. In consequence, the detection performance will be affected directly.  
Despite these limitations, WiFi signal-based intrusion detection systems have much potential in a smart home. In our future work, we will explore more effective features that less affected as much by the distance of the intruder to the transceivers and the number of the intruders in the environment to make the approach more robust in smart home applications.
# P System with Fractional Reduction
## 5 Experimental Results
### Simulation Experiments  
Various P System simulation tools are listed in the literature [24]. In this paper, we make use of the P System simulation software UPSimulator (UPS for short), which is used to read the P System model described by UPL and to perform simulation experiments on the model. Therefore, this section first introduces the relevant knowledge of UPLanguage, then introduces the UPSimulator simulation software, and finally provides an example to demonstrate the simulation process.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IIoT Architecture** & **Layers** & **Objectives** & **Peculiar Vulnerabilities** \\ \hline \multirow{8}{*}{**Baselines**} & \multirow{2}{*}{Layer 1} & It houses sensors, actuators, transmitters, and embedded devices. It is the lowest (physical) layer of OT and deals with physical industrial processing. & Reverse engineering, eavesdropping, brute force, and malware \\ \cline{2-4}  & & \multirow{2}{*}{Layer 2} & This layer communicates with the physical layer. The communication devices in this layer include a distributed control system, PLC, and gateways. & Replay attacks, MITM attacks, brute force, and sniffing \\ \cline{2-4}  & & \multirow{2}{*}{Layer 3} & It is the topmost layer of OT that collects and shares the incoming data from the preceding OT layers. The devices for this task include SCADA, HMI, control rooms, and operation stations & IP spoofing, malware, data sniffing, and data manipulation \\ \hline \multirow{8}{*}{**Baselines**} & \multirow{2}{*}{Layer 4} & It is the bottom IT layer that collects incoming data from the topmost OT layer for storage at the remote data centers. Layer 4, thus, supports office applications, intranet, mail, and web services. & Phishing, SQL injection, malware, DNS poisoning, and brute force \\ \cline{2-4}  & & \multirow{2}{*}{Layer 5} & It is the top IT layer for strategic business planning using cloud computing, data analytics, the Internet, mobile devices, and smart devices. & DoS attack, malware, password, side channel attack, and MITM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IT/OT layers with their peculiar attacks. We surveyed the security challenges of IIoT and discovered that they could be broadly divided into two categories: architectural design and multifaceted connectivity. 2. We reviewed how learning techniques have been deployed in the IIoT ecosystem to predict, identify, and mitigate the launch of attacks against a secure IIoT environment. 3. Also, as we are in a post-quantum era, we conducted an in-depth analysis of how quantum mechanisms have advanced IIoTsec and examined the limitations of quantum deployment in the IIoT ecosystem. 4. Based on the inherent features of learning algorithms and quantum principles, we highlighted a few prospects for hybridizing the two techniques to realize a secure IIoT ecosystem. 5. In conclusion, we advocated that IIoT-centric datasets should be used for validation as long as the model is tailored toward IIoTsec and presented IIoT-centric datasets with their distinguished features. The remaining part of the work is structured as follows: Section 2 elaborates on the review approach of the study, where the research questions are explicitly spelled out. Our key findings are presented in Section 3. In Section 4, we highlight the challenges, limitations, and prospects of the study. Finally, the concluding remark is made in Section 5.
# Real-Time Motion Adaptation with Spatial Perception for an Augmented Reality Character
## 3 System Overview and Approach  
Figure 2 illustrates an example of the adaptive motion control in a puddle in the real situation. The movements made by the adapted AR character depend on the spatial configuration. We should also consider the identification of real-world objects to estimate 3D positions in AR [27], assuming that the character would be positioned in various real environments. After transformations such as the position and rotation of the character  
are decided, the character will adapt its motion depending on the character's position registered in the AR environment. In this case, to represent natural movement, the AR character's motion will be established in an adaptive form in real-world situations based on contexts of real space, and the 3D position of the AR character will be estimated.  
Figure 3 describes our automatic motion adaptation process for the AR character in real environments. To adjust the motion to match the real situation, first, we needed to predict the objects in the real world using a detection process. At this time, if the score of the detected object was higher than the pre-defined threshold (T) related to object recognition, we decided that adaptive motion should be applied to the AR environment instead of the character's original motion. Here, the threshold value, which is related to the degree of performance of object recognition, required to determine the character's motion will be set depending on environmental conditions such as lighting conditions. Then, we applied this to the corresponding character motion by considering the location of the actual detected object and its characteristics. We then estimated the 3D position from 2D images (or moving pictures) to determine the location where the virtual character should be superimposed. For example, the AR character with original movements (e.g., normal walking movement) will generate suitable motion sequences (e.g., careful walking movement) at the location of the recognized object. As shown in Figure 3, we recognized real objects, such as puddles, from a large number of real images and selected the character's motion expressed to match the real-world context from several motion candidates. Detailed information is provided in the Section 4.  
Figure 3: Automatic motion adaptation process for the AR character.  
Figure 2: Comparison between original motion and our adaptive control using a real puddle: an AR character walking without acknowledging the context of the real environment (**left**) and the character using adaptive motion to cross the puddle (**right**).
# Acoustic Impact of Hybrid-Electric DEP Aircraft Configuration at Airport Level
## 4 Analysis of the Results
### Aerodynamic Results
#### 4.1.2 Motorization 2  
Figure 15 illustrates the interactional process between the propellers, their wakes, and the airframe. In particular, the pressure distributions on the wing highlight a peak expansion at the leading edge throughout the wing, produced by the interactions with the propellers and their wakes. In particular, the top view highlights that the interactional effect of the DEP propellers is higher in the proximity to the fuselage and between the first two innermost DEP propellers, where the expansion extends to more than half the local chord length.  
An example of the convergence history of the solution for climbing flight at full thrust is represented in Figure 16, in terms of the thrust and induced torque coefficients. The figure shows that the four DEP propellers produce the same average thrust with only a slight difference in amplitude for the DEP propeller closer to the tip propeller, DEP4. The propeller DEP1 (red line) is located near the fuselage and produces a thrust fluctuation that is slightly higher than the others due to its interaction with the fuselage on its side. extremely reduced thrust generated by the tip propeller was also observed. The difference in RPM between the DEP propellers and the tip one is such that four revolutions of the DEP propellers correspond to about three revolutions of the tip propeller. Finally, slight differences in the average values of the induced torque were also observed.  
Figure 17 shows a representation of the loads, CN M\({}^{2}\), acting on the propellers' disk both in climbing, at full thrust, and in descending flight, at 40% ETR. In particular, the higher loads on the upper region of the disks in climb are mainly explained by the positive AoA of the aircraft's attitude. All DEP propellers show a similar behavior, with a localized region of low CN M\({}^{2}\) in the lower region of the disk and in the inner part of the blade span. The tip propeller produces much lower loads on the disk.  
In descending flight at 40% ETR, the loads on the disks are significantly reduced. The load distributions show a wider area of lower values, bounding the higher values toward the blade tips, especially in the lower region of the disks. The negative attitude of the aircraft produces higher loads on the lower region of the tip propeller disk, resulting in a reversed behavior with respect to the climbing flight.  
Figure 15: Motorization 2—Cp distribution and wake development in a 100%-ETR climb, isometric view (**a**) and view from above the aircraft (**b**).
## 8 Performance Comparison  
The proposed technique's performance is evaluated in comparison with the algorithm discussed in [24]. The algorithm presented in [24] is based on the wavelet transform, and its is influenced by the noise in the current signal. Further, this algorithm uses GPS for the synchronization of the sampling frequency at both terminals of the transmission line and communication channel, which involves a high cost and makes the method complex. The algorithm introduced in this manuscript is less affected by noise, and it is less complex and eliminates the requirement of the communication channel and GPS. Fault indices for the LG fault computed using the proposed algorithm and the algorithm reported in [24] are described in Figure 13a,b, respectively. It is observed that the time taken to attain the value of the threshold for the FI associated with Phase A is high in the algorithm reported in [24] compared to the proposed approach. Further, the values of the FI associated with healthy Phases B and C is zero after the fault incidence for proposed algorithm, whereas it is non-zero for the algorithm reported in [24]. Hence, the algorithm reported in [24] might generate a false tripping signal, the probability of which is very low when using the protection scheme proposed in this paper. Hence, the proposed protection scheme is superior compared to the protection scheme reported in [24].