esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Microwave Liquid Crystal Technology
### Software Design Tool for MLC Devices  
A major objective in designing MLC components/devices is to maximize the material's tunability, i.e., to achieve a high tuning effectivity \(\Delta\tau_{\varphi}=\tau_{\varphi}(V_{sat})-\tau_{\varphi}(0)\), where \(\tau_{\varphi}(0)\) is the orientation effectivity of a device in the unbiased state \(V_{b}=0\) and \(\tau_{\varphi}(V_{sat})\) is the orientation effectivity in the fully biased  
Figure 11: Schematic of an LC varactor with a floating top electrode and bottom electrodes, forming the two plates of the capacitor with distance \(h_{LC}\), where later LC is filled in.  
state beyond saturation \(V_{b}>V_{sat}\). Hence, \(\Delta\tau_{\varphi}\) describes quantitatively how much of the material's tunability can be utilized by a proposed device layout, e.g., to optimize the tuning capacitance range of a specific LC varactor or to maximize the differential phase shift of a certain phase shifter topology.  
E.g., for the capacitor in Figure 9a, where only pre-alignment by means of surface anchoring is active, the optimum \(\tau_{\varphi}(0)=0\) would be achieved when all LC molecules are perpendicular to the RF field lines, whereas not all LC molecules can be tuned in parallel to the RF field for fully biasing with \(V_{b}>V_{sat}\), leading to an orientation effectivity \(\tau_{\varphi}(V_{sat})<1\), and thus to a tuning effectivity \(\Delta\tau_{\varphi}<1\). Hence, for the design and investigation generally of arbitrary microwave structures, including LC-based varactors, phase shifters, filters, polarizers, etc., an in-house software tool _\"SimLCug\"_ has been implemented at TU Darmstadt [74; 229; 230], combining  
* the finite difference method for static fields to simulate the director dynamics and
* the finite difference frequency domain method for a full RF wave simulation.  
The flow chart of this software tool is given in Figure 12.  
Figure 12: Flow chart of the in-house software tool “_SimLCug”_, combining the finite difference method for static fields to simulate the director dynamics and the finite difference frequency domain method for a full RF wave simulation [74; 229; 230].  
It allows a physical insight and systematic analysis of MLC-based structures in dependence of the LC's properties, the alignment layers, and their orientation, as well as the design parameters, e.g., in terms of the tuning effectivity \(\Delta\tau_{\varphi}\) and how to improve it, in terms of orientation effectivity \(\tau_{\varphi}(V_{b})\) over time and its response times \(\tau_{\text{on}}\) and \(\tau_{\text{off}}\)[74; 175; 231; 232].
## 4 Presumptions and Network Structuring
### State of the Art
#### 4.3.2 Data Aggregation Using Similarity Function (JACCARD)  
The JACCARD similarity Function supports many other similarity functions. So, it is widely used for various applications. It returns a value [0,1]. The higher the value, the more the similarity. In order to reduce the similar values, the highest cardinal value is treated as a duplicate pair and can be eliminated. The size of the data set is reduced before transmission to the sink node.  
Example:  
Suppose, there is a sensor \(R\).  
At a period \(k\), calculated vector measurements \(N_{i}=[7,\,7.8,\,7.9,\,9.2,\,9.5,\,9.6,\,9.7,\,10,\,10.1]\) Considering \(\Psi=0.4\),  
The similarity function will transform the set of measures into \(N_{i}^{\prime}=\{(7,3),\,\,(9,5),\,(10,2)\}\) where 3, 5, 2 are the weights of 7, 9, 10, respectively.
###### Abstract  
This study introduces a frequency regulation strategy to enable the participation of wind turbines with permanent magnet synchronous generators (PMSGs). The optimal strategy focuses on developing the frequency support capability of PMSGs connected to the power system. Active power control is performed using maximum power point tracking (MPPT) and de-loaded control to supply the required power reserve following a disturbance. A kinetic energy (KE) reserve control is developed to enhance the frequency regulation capability of wind turbines. The coordination with the de-loaded control prevents instability in the PMSG wind system due to excessive KE discharge. A KE optimization method that maximizes the sum of the KE reserves at wind farms is also adopted to determine the de-loaded power reference for each PMSG wind turbine using the particle swarm optimization (PSO) algorithm. To validate the effectiveness of the proposed optimal control and operation strategy, three different case studies are conducted using the PSCAD/EMTDC simulation tool. The results demonstrate that the optimal strategy enhances the frequency support contribution from PMSG wind turbines.  
de-loaded control; frequency regulation; kinetic energy reserve control; maximum power point tracking; particle swarm optimization; permanent magnet synchronous generator
## 5 Wood-Based Panel Emissions
### Wood Panels Bonded without Adhesive
#### 5.2.1 VOC Emissions from Heat-Treated Softwoods
For untreated Scot's pine, the compounds emitted in the greatest quantities were \(\alpha\)-pinene (410-890 \(\upmu\)g m\({}^{-2}\) h\({}^{-1}\)), \(\Delta\)-3-carene (340-680 \(\upmu\)g m\({}^{-2}\) h\({}^{-1}\)), and hexanal (22-70 \(\upmu\)g m\({}^{-2}\) h\({}^{-1}\)), while for treated Scot's pine were acetic acid (4-86 \(\upmu\)g m\({}^{-2}\) h\({}^{-1}\)), furufural (11-26 \(\upmu\)g m\({}^{-2}\) h\({}^{-1}\)), and hexanal (10-17 \(\upmu\)g m\({}^{-2}\) h\({}^{-1}\)). [PERSON] et al. [148] studied Thermoholz-treated wood (spruce, map, beech, and ash) at 180 and 200 \({}^{\circ}\)C and compared it with its untreated peers. Emissions from untreated spruce were mainly terpen (mono-, sesqui-, and diterpenes), accounting for around 95% of total VOC emissions. After heat treatment, carboxylic acid and aldehyde emissions increased (representing 25% and 10% of total emissions, respectively), while terpene emissions decreased. However, terpene emissions remained in the majority after treatment, accounting for around 65% of total emissions. [PERSON] et al. [148] identified over 50 terpen emitted by untreated spruce: \(\alpha\)- and \(\beta\)-pinene, sylvestrene, carophyllene, and \(\gamma\)- and \(\delta\)-cadinene were the main compounds. As for aldehydes, hexanal accounted for 70% of aldehydes emitted by untreated spruce. After treatment, hexanal accounted for only 1.4% of aldehyde emissions, while furufural and 5-methylfurfural accounted for a very high 96% of the aldehyde fraction. All these studies concluded that the compounds emitted by untreated softwood were mainly terpene compounds, in particular pinenes, limonene, or camphene, as well as a few aldehydes in smaller quantities (hexanal). On the other hand, after heat treatment, the total quantity of VOCs emitted was lower than before heat treatment, with aldehyde compounds predominating, notably acetic acid and furufural.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 3 SWE-Based Acquisition Chain
### Sensor  
Sensors are the component gathering data and making it available through a communication interface, usually serial port or Ethernet. Three different kinds of sensors are taken into account within this work:  
* **COTS Sensors**: Commercial off-the-shelf (COTS) sensors are commercially available devices without any particular enhancement in terms of interoperability.
* **OGC PUCK-enabled Sensors**: Sensors which implement the OGC PUCK protocol.  
Figure 5: Proposed SWE-based acquisition chain.  
Virtual Instruments: Software components that merge or process data from different sources, generating new data sets accessible through a communication interface.  
Each sensor should have its own associated SDF, a SensorML description containing both sensor metadata and deployment information (see Section 4) regardless of their nature. This file is a key piece of the architecture, as it contains all metadata required to describe, operate and register a sensor into Sensor Web services. OGC PUCK-enabled sensors may have their own SDF embedded within its internal payload memory, providing automatic detection and self-description capabilities.  
Ideally physical sensors should be OGC PUCK-enabled to provide end to end plug and play capabilities. However, the majority of commercial sensors do not implement this protocol and do not provide auto-detection and self-description procedures. Therefore the operators have to manually match the sensor with SDF stored locally on the platform in order to provide compatibility with the proposed standardized architecture.
## 3 Three-Dimensional Numerical Simulation  
### Model Description  
At the meantime, Flow-3D was used to build a three-dimensional hydrodynamic and temperature numerical model with the same size of the experimental test model (Figure 3a), for the further research of TCC. The model was divided into 145 longitudinal segments, 45 vertical layers, and 20 lateral segments. Based upon the phenomenon of the experimental test, TCC appeared arc-shaped because of the thrust force by flow and its flexibility, and the fields around the curtain were the most concerned. Thus, for the test zone, 45 segments were set in the upstream forebay zone and 25 segments were set in the curtain zone to give a better show of the arc-shaped curtain for obtaining more details (Figure 3b).  
The grid-independence tests were taken before the numerical simulation. For working condition A1, the RWT with different grid sizes were compared, as shown in Table 2. It can be concluded that variation in RWT basically did not with the grid size increase and decrease, both vertical and longitudinal; however, the water temperature of the outflow changes by up to 2%. Therefore, the mesh generation method described in this paper was determined by considering the calculation precision and calculation amount comprehensively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & _CP_ **(m)** & _Pr (\%)_ & _OSD_ **(cm)** & _RQ_ **(m3/h)** &
\begin{tabular}{c} **Vertical Temperature** \\ **Distribution** \\ \end{tabular} & **RWT (\({}^{\circ}\)C)** \\ \hline A0 & — & — & 40 & 6.4 & S1 & 27.5 \\ A1 & 3.5 & 85 & 40 & 6.4 & S1 & 35.3 \\ A2 & 2.5 & 85 & 40 & 6.4 & S1 & 35.4 \\ A3 & 1.0 & 8  
Since the mechanical characteristics were not concerned, the curtain was simplified as a rigid arc-shaped structure without regard to flexibility.  
The boundary conditions and scenarios were set the same as the experimental tests. After repeated adjustment, the bottom Manning coefficient was set as 0.01, the coefficient of bottom heat exchange was set as 0.3 W/(m\({}^{2}\)\({}^{\circ}\)C), and dynamic viscosity and thermal conductivity of fluid were set according to water properties at different temperatures.  
In order to cover the shortage of experimental tests, the numerical model's scenarios were as a supplement and improvement to experimental tests. Thus, each variable set multiple scenarios; taking \(Pr\) as an example, the numerical model scenarios were set the same as model tests and with 95%, 92%, 80%, 62%, 50%, and 48% added at the same time. Through a lot of numerical simulation, the accuracy of the physical model was verified and the principle of TCC was summarized as well.
###### Abstract  
This communication aims to address the recent surge of interest in reconfigurable intelligent surfaces (RISs) among both academic and industrial communities, which has largely neglected the historical developments of two other underpinning technologies, i.e., liquid crystal on silicon (LCOS) and liquid crystal reflectarray antenna (LCRA). Specifically, this communication focuses on the state of the art of LC-RIS, highlighting the unique features of this newly raised enabling technology for post-5G (6G) networks and comparing it to LCOS, which operates at different frequencies and is suited to different use cases. Drawing on insights from existing knowledge of LCOS and LCRA, opportunities and challenges are explored for LC-RIS's technical advancements in enhancing the coverage, capacity, and energy efficiency of wireless networks. In particular, the development status and roadmap of LC-RIS in China is reviewed.  
electromagnetics; intelligent reflecting surface; liquid crystal; liquid crystal on silicon; phased array; reconfigurable intelligent surface; RIS; reflectarray; 5G; 6
## 5 Power Optimization
### The Optimization Results  
The F-test is a test under the null hypothesis (H0) where the statistical value follows the F-distribution, which is usually used to test the significance of the equation and the degree of influence of each factor on the test index. In this paper, Design-Expert software is used to process the test data in Table 4, test the fitting degree of the regression equation, verify the significance of each factor on the regression equation, and obtain the ANOVA results of cutting power consumption, as shown in Table 5. As can be seen from Table 5, model P (0.0052) is less than 0.01, indicating that the regression model is extremely significant. The misfitting term P (0.1961) was greater than 0.05, that is, the misfitting was not significant. And the goodness-of-fit of the regression model R\({}^{2}\) is 0.90, indicating that the quadratic regression equation fitted by the model is consistent with the reality, which can correctly reflect the relationship between cutting power consumption and X\({}_{1}\) (forward speed), X\({}_{2}\) (cutter roll speed), and X\({}_{3}\) (bending angle). The regression model can be used to predict the results of optimization tests. The regression equation of cutting power consumption was obtained after removing these insignificant factors:  
\[\mathrm{Y}=-2.96+0.5026\mathrm{X}_{1}+0.3616\mathrm{X}_{3}-0.4078\mathrm{X}_{2 }X_{3}-0.7193\mathrm{X}_{2}^{2}+0.6130\mathrm{X}_{3}^{2} \tag{6}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **X\({}_{1}\)** & **X\({}_{2}\)** & **X\({}_{3}\)** & **Cutting Power Consumption Y** \\ \hline
1 & \(-1.000\) & \(-1.000\) & 0.000 & 1.640 \\
2 & 1.000 & \(-1.000\) & 0.000 & 2.212 \\
3 & \(-1.000\) & 1.000 & 0.000 & 2.391 \\
4 & 1.000 & 1.000 & 0.000 & 3.286 \\
5 & \(-1.000\) & 0.000 & \(-1.000\) & 2.347 \\
6 & 1.000 & 0.000 & \(-1.000\) & 3.970 \\
7 & \(-1.000\) & 0.000 & 1.000 & 3.805 \\
8 & 1.000 & 0.000 & 1.000 & 4.736 \\
9 & 0.000 & \(-1.000\) & \(-1.000\) & 2.270 \\
10 & 0.000 & 1.000 & \(-1.000\) & 3.111 \\
11 & 0.000 & \(-1.000\) & 1.000 & 3.420 \\
12 & 0.000 & 1.000 & 1.000 & 2.630 \\
13 & 0.000 & 0.000 & 0.000 & 2.500 \\
14 & 0.000 & 0.000 & 0.000 & 2.955 \\
15 & 0.000 & 0.000 & 0.000 & 3.410 \\
16 & 0.000 & 0.000 & 0.000 & 2.854 \\
17 & 0.000 & 0.000 & 0.000 & 3.101 \\ \hline \hline \end{tabular} Note: \(p\leq 0.01\) means extremely significant, marked with \({}^{***}\); \(p\leq 0.05\) means significant, marked with \({}^{***}\); \(p<0.05\) means insignificant, marked with \({}^{***}\); \(p\leq 0.05\) means insignificant, marked with \({}^{***}\).  
\end{table} 
Table 4: Design and results of cutting simulation experiment.
## 3 Data Analysis
### Label Correlation  
We calculated the correlation between each emotion over the sentences in our dataset, that is, emotions that occur in the same sentence (since sentences can express multiple emotions given a multi-label formulation), and present the results in Figure 4. The highest and lowest label pairs are summarized in Table 6. The results are mostly common sense, given the definitions, and are extremely similar to those in GoEmotions, with the top pairs being the same where possible (e.g., annoyance and anger, joy and excitement, nervousness, and fear). Very low negative correlation between emotions is likely less common in literature compared to social media due to the purposeful use of contrast. For example, it is common to praise one character while in the same sentence showing contempt for others (\"I liked your spirit; I hate these tame, perfectly conventional girls; they bore me to death\"). Or show hope in the middle of despair (\"I feel at times very much deprest indeed, but have no doubt but that all things will work for good\").  
Figure 4: Emotion correlation calculated over the sentences in our dataset. Mostly common sense, except for smaller values of negative correlation. The results are in line with other datasets.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**No of Emotions** & **Examples (\%)** \\ \hline
0 & 14.3 \\
1 & 27.4 \\
2 & 20.8 \\
3 & 14.4 \\
4 & 10.0 \\
5 & 6.6 \\
6 & 4.3 \\
7 & 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of emotions per example (%). The majority of sentences have two or fewer emotion labels.
## 4 Experiments and Results
### Evaluation Metrics  
Quantitative and qualitative assessments were conducted using established evaluation metrics, including the Dice Similarity Coefficient (Dice) score and the Hausdorff distance (HD) [51; 52]. The Dice score serves as a metric for the similarity between two sets. In the context of image segmentation, it quantifies the resemblance between the segmentation outcomes predicted by the network and the manual annotations, and is expressed as follows:  
\[Dice=\frac{2 TP}{2 TP+FP+FN} \tag{5}\]  
where \(TP\), \(FP\), and \(FN\) denote true-positive cases, false-positive cases, and false-negative cases, respectively. The Hausdorff distance (HD) signifies the maximum distance between the boundary of the predicted segmentation and the actual region boundary. A lower HD value indicates a smaller error in the predicted boundary segmentation, which corresponds to higher quality. The HD can be mathematically represented as follows:  
\[D(P,T)=\max\{sup_{t\in T}\ inf_{p\in P}d(t,p),\ sup_{p\in P}\ inf_{t\in T}d(t,p)\} \tag{6}\]  
where \(t\) and \(p\) represent the real region boundary and predicted segmentation region boundary, respectively. \(d(\cdot)\) represents the distance between \(t\) and \(p\). The sup denotes the supremum and the \(\inf\) denotes the infimum.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 7 Conclusions  
This paper analyzes the security risks of data asset sharing circulation in uncontrolled environment, and focuses on the security requirements of data assets sharing circulation in four key scenarios: outsourcing storage, multi-party sharing, trusted operation and cross-domain return. Taking cryptography technology as the core, a security protection model of data asset life cycle is established, a security technical framework covering the whole life cycle of data asset sharing and circulation is designed, and a design scheme of data asset security sharing circulation system architecture based on cryptography service technology is proposed. The research work of this paper provides a novel cryptography-based architecture and solution for systematically solving the security problem of data asset sharing and circulation in uncontrolled environment, which can effectively improve the security efficiency of data asset sharing and circulation and ensure the availability, control and credibility of data assets.  
Conceptualization, D.Y. and Y.W.; methodology, D.Y.; software, Y.W.; validation, D.Y., Y.W. and W.H.; formal analysis, Y.W.; investigation, W.H.; resources, W.H.; data curation, Y.Z.; writing--original draft preparation, D.Y.; writing--review and editing, D.Y.; visualization, W.H.; supervision, Y.Z.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FOUNDATION OF NATIONAL KEY LABORATORY OF SECURITY COMMUNICATION, grant number 6142103042304.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Measurement of Wheel Radius in an Automated Guided Vehicle
## 3 An Overview of Selected Measurement methods  
### Introduction  
Rolling radius defined as the agreed size of the radius of such a rigid wheel, which at section \(L\) performs the same number of rotations \(n_{k}\). This describes Relationship (7):  
\[L=2\pi r_{t}n_{k}. \tag{7}\]  
Thus, the rolling radius of the wheel \(r_{t}\) is determined by the formula:  
\[r_{t}=\frac{L}{2\pi n_{k}}. \tag{8}\]  
Wheel slip of the driven wheel \(s\) is:  
\[s=\frac{v_{s}}{v_{x}}=\frac{\omega r-\omega r_{t}}{\omega r}=1-\frac{r_{t}}{r}. \tag{9}\]  
Wheel slip of the braked wheel \(s\) is:  
\[s=\frac{v_{s}}{v_{x}}=\frac{\omega r_{t}-\omega r}{\omega r_{t}}=1-\frac{r}{r _{t}}. \tag{10}\]  
where:  
\(r_{t}\)--rolling radius;  
\(r\)--free radius;  
\(v_{s}\)--slip velocity;  
\(v_{x}\)--wheel center speed.  
For the driven wheel in the slip range \(s\) from 0 to 1, the rolling radius \(r_{t}\) changes from \(r\) to 0. With the case of the rolling radius \(r_{t}\) = 0 we deal when the vehicle stands \(v\) = 0 and the driven wheels rotate at an angular speed \(\omega>0\). For a nondriven wheel in the slip range \(s\) from 0 to 1, the rolling radius \(r_{t}\) varies from \(r\) to \(\infty\). With the case of a rolling radius, \(r_{t}=\infty\) is when the vehicle moves at a speed \(v>0\) and the braked wheel is blocked \(\omega=0\). In the range of small slip to \(s=0.02\), the relationship between vertical wheel load and circumferential force is linear [22].  
There are many advanced theoretical methods to determine the rolling radius as a function of the type of work and load. The accuracy of these methods depends on the knowledge of specific parameters. Sometimes, and in many cases, this requires additional experimental testing. For this reason, it was decided to determine the rolling radii directly on the basis of experimental research. According to Relationship (8), to determine the rolling radius, it is sufficient to measure the distance traveled by the road \(L\) and read the recorded data on the quantities made by the wheel revolutions \(n_{k}\). In this measurement method, it is very important to maintain the straight-line nature of the movement. In real traffic conditions, there will always be smaller or larger deviations from the theoretical route. These deviations are affected by the control system and incorrect initial position and values of the rolling radii entered into the navigation algorithm.
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 5 Experimental Results
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values** \\ \hline Total number of stations & \{100, 150, 200\} \\ Number of MBSs & 10\% of the stations \\ Normalized radio range of MBSs (\(R_{M}\)) & \{0.3, 0.5\} \\ Normalized radio range of SCs (\(R_{S}\)) & \{0.15, 0.2\} \\ Iterations of SGTC (\(Max\_Itera\)) & 10,000 \\ Initial temperature (\(\alpha_{0}\)) & 1.0 \\ Initial set of links (\(E_{0}\)) & \{Ø, Feasible fully connected, Voronoi\} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameters. Figure 1: Experimental workflow. Lastly, a set of algorithms compute the diameter \(D_{G}\), robustness \(R_{G}\), cost \(C_{G}\), and entropy \(S_{G}\) of network \(G\).