esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Illustrative Examples
### Beam Analysis
#### 4.2.1 Concerning the Accuracy and Efficiency
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**ANSYS**} & \multirow{2}{*}{**Proposed Beam 1**} & \multicolumn{2}{c}{**Classic Beam**} & \multicolumn{2}{c}{**Proposed Beam 2**} & \multicolumn{2}{c}{**Proposed Beam 3**} & \multicolumn{2}{c}{**Proposed Beam 4**} \\  & & **Shell** & & & & & & & & & \\ \hline \multirow{2}{*}{**Number**} & **Frequency** & **Frequency** & **Relative** & **Frequency** & **Relative** & **Frequency** & **Relative** & **Frequency** & **Relative** & **Frequency** & **Relative** \\  & **[H]** & **[H]** & **Error** [\%] & **[H]** & **Error** [\%] & **[H]** & **Error** [\%] & **[H]** & **Error** [\%] & **[H]** & **Error** [\%] \\ \hline \multirow{2}{*}{1} & 17.83 & 17.91 & 0.45 & 18.77 & 5.27 & 18.66 & 4.65 & 18.57 & 4.15 & 18.48 & 3.64 \\  & 2 & 29.01 & 29.16 & 0.52 & 30.63 & 5.58 & 30.43 & 4.89 & 30.35 & 4.62 & 30.16 & 3.36 \\  & 43.20 & 42.56 & -1.48 & 77.52 & 79.4 & 44.11 & 2.11 & 44.11 & 2.11 & 43.20 & 0 \\  & 4 & 57.73 & 56.97 & -1.32 & / & / & 57.11 & -1.07 & 57.11 & -1.07 & 57.11 & -1.07 \\  & 5 & 60.56 & 58.86 & -2.81 & / & / & 59.89 & -1.11 & 59.89 & -1.11 & 59.89 & -1.11 \\  & 6 & 65.10 & 63.74 & -2.09 & / & / & / & / & / & / & 64.23 & -1.34 \\  & 7 & 66.62 & 64.69 & -2.90 & / & / & 65.13 & -2.24 & 65.13 & -2.24 & 65.13 & -2.24 \\  & 8 & 72.53 & 72.01 & -0.72 & / & / & 72.30 & -0.32 & 72.30 & -0.32 & 72.30 & -0.32 \\  & 9 & 75.31 & 76.27 & 1.27 & / & / & / & / & 78.34 & 4.02 & 78.20 & 3.84 \\  & 10 & 75.71 & 76.95 & 1.64 & / & / & / & / & 78.84 & 4.13 & 78.84 & 4.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the first 10 natural frequencies of the illustrative thin-walled structure modelled with the proposed theory and ANSYS Shell 181, respectively. Figure 12: The amplitude functions of the identified cross-section deformation modes varying along the beam axis concerning the first four eigenvectors: (**a**) Out-of-plane modes and (**b**) in-plane modes. Moreover, one should bear in mind that the results of the proposed models were obtained with a mesh of 40 1D elements, while those in the comparison were derived with a 3D shell model adopting 2816 2D shell elements, notably having a comparable accuracy. This was a great improvement of the computation efficiency in the analysis of thin-walled structures.
# Research on Data Transmission of Laser Sensors for Reading Ruler
## 3 Research on Coding Ruler Data
### Coder Bar Grid Description  
First, an understanding of the basic composition of the reader is required. Inside the U-shaped reading wharf, a laser transmitter is installed on one side, and a laser receiver is installed on the other side [13, 14, 15]. The laser transmitter emits a steady beam, which travels through a specific path and is finally received by the corresponding laser receiver. When the receiver successfully receives the beam, it converts this signal into a digital signal to read the data.  
When the reader is installed on the ruler track, it begins to efficiently read data. A key element in this process is the light-tight encoder. Different areas on the encoder scale have different effects on the beam. When the beam from the laser transmitter is blocked by one side of the encoder, the laser receiver cannot receive the optical signal, and the digital signal \"0\" is output.  
With the relative displacement of the code reader on the ruler track, the correlative laser sensor continuously passes through the holes on the ruler. The beam can pass normally when the sensor passes through these holes. At this time, the laser receiver receives the optical signal and outputs the digital signal \"1\".  
We follow certain rules in order to accurately depict the encoder. In encoding, \"1\" corresponds to the hollow part and \"0\" to the solid part on the ruler. In this way, the solid and hollow parts on the encoder ruler constitute a unique encoding sequence, as shown in Table 6. In this example, the code \"11100100101011\" corresponds to a series of solid and hollow parts on the encoder scale.  
It is worth noting that the data reading method of this laser sensor has many advantages. First, it has very high reading accuracy and can meet the needs of most application scenarios. Second, because it uses a laser as the signal transmission medium, it has a strong anti-interference ability and can operate stably in a harsh industrial environment.
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 2 Materials and Methods
### Chemical Hydrolysis of Undisrupted Cells  
Chemical hydrolysis of undisrupted _M. salina_ cells was investigated using a combination of acidic and enzymatic hydrolysis. A pretreatment with proteases was performed before the hydrolysis with polysaccharide-degrading enzymes to determine if this would increase the accessibility of carbohydrates for the latter enzymes. The two different protease mixtures tested for this purpose were Protame(r) (Novozymes, Bagsvaerd, Denmark) with maximum activity at pH 7.0 and Protease from _Aspergillus saitoi_ (Sigma-Aldrich, Taufkirchen, Germany) with maximum activity at pH 3.5. Thus, the use of the latter protease was also considered as acidic hydrolysis.  
Chemical hydrolysis was performed using _M. salina_ biomass with a CDW concentration of 163 g L\({}^{-1}\) and the commercial enzyme mixes listed in Table 3. The enzyme dosing is expressed in g enzyme per g CDW of the biomass suspension and was adjusted to 1.0% protease, 5.9% cellulase mixture, and 0.12% mannanase. Since the optimal pH and temperature conditions to achieve maximum activity differed for each enzyme, these parameters were adjusted differently for the proteolysis and the following polysaccharide hydrolysis, as listed in Table 4. The exact procedure applied was as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline
**Nr.** & **Material** & **Diameter, mm** & **Density, g mL\({}^{-1}\)** \\ \hline
1 & Zirconium oxide & 1 & 6.1 \\
2 & Zirconium oxide & 0.5 & 6.9 \\
3 & Agate & 1 & 6.0 \\
4 & Glass & 3 & 2.9 \\
5 & Stainless steel & 5 & 7.7 \\
6 & Stainless steel & 2 & 7.7 \\
7 & Tungsten carbide & 3 & 14.9 \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of the various beads used for cell disruption of _M. salina_ by bead milling.  
* Adjustment of the pH to the optimum value depending on the protease used as specified in Table 4 by addition of concentrated acetic acid solution.
* Proteolysis using the specified protease mix for 18 h.
* Autoclaving at 121 \({}^{\circ}\)C for 20 min (for sterilization and protease inactivation).
* Adjustment to pH 5.0 by addition of concentrated potassium hydroxide solution.
* Addition of cellulase and mannanase mixtures and further hydrolysis for 72 h.
## 2 Materials and Methods
### Computational Fluid Dynamics (CFD) Model  
In this section, the computational fluid dynamics governing equations are discussed with complete information for the fluid flow system. Commercially available finite volume software ANSYS Fluent was used in the present study for the CFD simulation. The simulation is subdivided into many mechanism volumes to be related to finite volume method. The conservation equation applicable is given [31] as in Equation (1).  
\[\int\limits_{CV}\frac{\partial(\rho\varphi)}{\partial(t)}dV+\int\limits_{CV} \
abla\cdot(\rho\varphi u)dV=\int\limits_{CV}\
abla\cdot(\Gamma\
abla\varphi) dV+\int\limits_{CV}S_{\varphi}dV \tag{1}\]  
where, \(\rho\) is density, \(\varphi\) is a variable fluid property, \(CV\) is the control volume, \(\Gamma\) is the diffusion coefficient, and \(S_{\varphi}\) is the rate of increase of the property \(\varphi\) due to sources.  
The volume integral of the convective and diffusive terms (Gauss's divergence) is rewritten as,  
\[\int\limits_{CV}\
abla\cdot(a)dV=\int\limits_{A}n\cdot adA \tag{2}\]  
where, \(n\cdot a\) is the component of vector \"a\" in the direction of the vector \"\(n\)\" normal to surface element \"\(dA\)\". So, the Equation (1) can be written as,  
\[\frac{\partial}{\partial t}\left(\int\limits_{CV}\rho\varphi dV\right)+\int \limits_{A}n\cdot(\rho\varphi u)dA=\int\limits_{A}n\cdot(\Gamma\
abla\varphi) dA+\int\limits_{CV}S_{\varphi}dV \tag{3}\]  
Here, the terms \(\frac{\partial}{\partial t}\left(\int\limits_{CV}\rho\varphi dV\right)\) represents the rate of change of the total amount of fluid property. \(\int\limits_{A}n\cdot(\rho\varphi u)\) expresses the net rate of decrease of fluid property \(\varphi\) of the fluid element due to convection. \(\int\limits_{A}n\cdot(\Gamma\
abla\varphi)\) is the net rate of increase of fluid property \(\varphi\) of the fluid elements due to diffusion and \(\int\limits_{CV}S_{\varphi}dV\) is the rate of increase of property \(\varphi\) as a result of sources inside the fluid element.  
Figure 4: Fluid structure Interaction domains and boundary interfaces.
# Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
## 5 Experiments
### Performance Evaluation
#### 5.2.2 Influence of Number of Services  
On  
As shown in Figure 7, the increase in the number of services results in a reduction in the probability of configuring the corresponding services in adjacent servers. This means that services need to be transmitted from a more distant server, and the increase in the transmission distance leads to an increase in the delay in user requests. When the service types are 10 and 40, the edge network can configure that number of services, so there is little difference in the delay between the three algorithms. When the number of services is 70, the probability that the neighboring servers are configured with the related services further decreases; therefore, the increase in delay becomes larger. When the number of services is 100, not all services can be fully configured, due to the resource constraints of the edge servers. When the service requested by the task is not configured in the edge network, the task cannot be processed, i.e., an off-target situation occurs, leading to a spike in delays. However, benefiting from our strategy, we are able to significantly reduce the delay when collaborating among edge servers, so the delay in the performance of user requests is still better than that of the other two algorithms.  
As shown in Figure 8, when the number of services is 10 and 40, the performance of the three algorithms in terms of the energy consumption of IoT devices is similar to that of the delay performance. However, with the increase in the number of services, because the decision of the DCOS algorithm pays more attention to the reduction in delay, the increase in the energy consumption of IoT devices is more significant than the increase in user request delays. Our strategy can efficiently reduce the delay, but the energy consumption of offloading tasks and the migration of services to intermediate nodes is inevitable. In terms of energy consumption, our strategy is not significantly better than that of DDPG and ESM.  
Limited by the resources of the edge servers, the edge network can only host a certain number of services; if the number of services exceeds the threshold, this will lead to off-target situations, which cause delays in user requests and the energy consumption of IoT devices to become larger. Through Figure 9, we can observe that the DCOS algorithm still outperforms DDPG and ESM in terms of the total cost, because our algorithm is more concerned with reducing textcolorblue delays in user requests, and can make flexible decisions that allow for better collaboration between edge servers, reducing the total cost. This demonstrates the effectiveness and flexibility of the DCOS algorithm.
## 3 Case Analysis
### Data Preprocessing  
Due to factors such as sensor anomalies, network failures, and other noise interferences, the data collected and stored by the platform contains missing values and outliers. In this study, the quartile method is used to identify and remove outliers. The remaining data, along with the originally missing data, are then completed using cubic spline interpolation. Furthermore, to eliminate the impact of differences in the magnitudes of different variables on model accuracy, the data are normalized using the min-max normalization method, which is formulated as follows:  
\[x^{\prime}(i)=\frac{x(i)-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{10}\]  
where \(x(i)\) and \(x^{\prime}(i)\) represent the values before and after normalization, respectively, and \(x_{\text{max}}\) and \(x_{\text{min}}\) are the maximum and minimum values of the variable sequence, respectively.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Subblock Selection Rationality Experiments  
In this paper, experiments were conducted on different subblock selection methods, and the total watermark experiments were conducted according to the subblocks selected by different methods. Figure 5 shows the extracted watermark BER (bit error rate) values for each group of experiments under the requantization attack, and the BER is calculated as in Equation (12). The combination of NNZ and Ef is 1 as the screening subblock condition in this paper, 2 is using only NNZ as the screening subblock condition, and 3 is using only Ef as the screening subblock condition. As can be seen from Figure 5, the BER values of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Type** & **Parameter Value** \\ \hline Frame rate & 30 fps \\ GOP structure & IPPP \\ Coding grade & Baseline \\ Entropy coding type & CACVL \\ Quantitative parameters & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameter configuration.  
1 are mostly lower than those of 2 and 3, so the combination of NNZ and Ef for selecting subblocks results in better anti-requantization performance.  
\[BER=\frac{1}{P\times Q}(\sum_{i=0}^{p-1}\sum_{j=0}^{Q-1}w(i,j)\oplus w^{\prime}(i, j)) \tag{12}\]  
where \(w\) is the original watermark, \(w^{\prime}\) is the extracted watermark, \(P\) and \(Q\) are the size of the watermark, \(\oplus\) is XOR operation.
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 4 Energy and Its Digital Transformation
### Digitalization in Building Energy Management
Digital twins can also help reduce maintenance costs by predicting equipment failures, scheduling maintenance activities accordingly, and helping improve safety by simulating hazardous scenarios and testing contingency plans [43]. Digital twin technology can model buildings and analyze their energy status to determine factors that affect energy efficiency. Then, take corresponding measures to reduce energy consumption and improve the sustainability of the building. For example, digital twin technology can optimize a building's lighting and air conditioning systems to reduce electricity consumption and carbon emissions. In addition, it can optimize the materials and design of building facades and windows, thereby improving the building's thermal insulation and ventilation, reducing energy consumption and negative environmental impact. Overall, digital twins provide building energy with data and analytics that support decision-making and implementation to improve energy efficiency and enable more energy-efficient operations.
## 2 Materials and Methods
### Development of Procedures for Verifying Surface Roughness Parameters

Table 3: Established measurement parameters for the Alicona InfiniteFocusG4. Figure 7: Orientation of acetabulum area of models manufactured for the surgery of patient no.1. light into the optics and focusing it through the lens onto the sample using a translucent mirror. The sample reflects the light, and the resulting image is projected onto a digital sensor through precision optics. The image produced is similar to that of conventional light microscopy, displaying a small depth of field. The distance between the sample and the lens is then altered while maintaining constant image registration. Subsequently, the reflected focus is calculated for each object position. Finally, the depth is determined by varying the focus value. This method provides a detailed 3D view of the surface [50, 51, 52]. Data processing was carried out in SPIP software 6.4.2 to determine the surface roughness parameters of the anatomical structure. First, the analysis area was cropped to 5 mm \(\times\) 5.5 mm due to numerous artifacts at the edges of the scans. Then, the process of form removal was carried out. Then, due to the visible measurement noise, the number of pixels was halved, and the noise-reduction process was carried out using a 15 \(\times\) 15 median filter. A Gaussian filter \(\lambda\)c = 0.8 mm was applied to separate the long-wave components, marking the transition from roughness to waviness. The result was a 3D and 2D visualization of the surface roughness and statistical parameters. During the research, three 3D parameters were analyzed:  
* Sa--the arithmetical mean height of the surface (6): \[Sa=\frac{1}{A}\iint\limits_{A}|z(x,y)|dxdy\] (6)
* Sq--root mean square height of the surface (7): \[Sq=\sqrt{\frac{1}{A}\iint\limits_{A}z^{2}(x,y)dxdy}\] (7)
* Spk + Sk + Svk--the sum of reduced peak height (Spk), core height (Sk), and reduced dale depth (Svk). Spk, Sk, and Svk are parameters related to the material ratio curve (Figure 8)
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method
### Adaptive-DecayRank Algorithm with Bayesian Updating  
Next, we describe our Adaptive-DecayRank algorithm, which incorporates a dynamic scoring function with _adaptive decay factors_ for responsive updates based on graph changes, along with a Bayesian updating mechanism for further refinement. The Adaptive-DecayRank algorithm extends our previous DecayRank approach. This innovation enables the algorithm to dynamically adapt to real-time changes in graph structures, ensuring robustness and accurate anomaly detection in dynamic graph settings.
# A Robust Wind Turbine Component Health Status Indicator
## 2 Materials and Methods
### Signal Smoothing  
Lastly, the generated failure index from component selected variables is smoothed. The cubic spline function has been considered for this issue. A spline function is a curve constructed from piecewise polynomial functions, ensuring continuity at their junction points. Spline functions have numerous applications in signal smoothing, noise reduction, and trend extraction in time series analysis.  
The choice of cubic splines provides a balance between flexibility and simplicity, ensuring effective smoothing without the risk of overfitting associated with higher-order splines. Specifically, the smooth.spline function in R, with an adjusted spar parameter to control the smoothness of the fit, was used. This approach has proven to be robust and practical for the dataset used. As a consequence, the generated failure index series is a smoothed [56] which provides a clean summary to the decision-maker.  
The resulting series is an easy-to-interpret signal with application in failure detection in components where a multivariable environment is considered. In addition, this same index has successful applications in the early detection of component failures, as demonstrated in the paper [57].
# On the Use of the Multi-Agent Environment for Mobility Applications
## 2 Environment as a Spatio-Temporal Reference  
The environment as a spatial reference is the most straightforward use of the multi-agent environment for mobility applications. A spatial environment provides a reference to the agents on which they can be located, on which they can move, and provides them  
Figure 1: Layers of multi-agent environment.  
with metrics to compute a distance between them. This use of the environment is the primary component provided by traffic simulation platforms such as SUMO [7], and general-purpose simulation platforms such as Repast Simphony [8]. The environment provides information about the present situation in the system, such as the positions of all the agents, the states of the entities (e.g., connected or non-connected vehicles), and the current constraints for the agents' actions (e.g., traffic light signals states).  
The environment as a spatial reference for the agents reflects the system's present situation. Agents perceive the state of the environment before acting on it. However, cognitive agents usually have some planning activities and have a representation of the time, additionally to the space dimension. This section presents an approach where the multi-agent environment has two linked dimensions: space and time. This representation can either be used by the agents for planning, to reserve certain parts of the network at specific periods, for instance, or to synchronise agents' actions and movements and also as a mental model for the agents, i.e., an internal knowledge representation of the dynamics of the networks through time.
# Honey as an Adjuvant in the Treatment of COVID-19 Infection: A Review
## 4 Honey as a Co-Adjuvant of SARS-CoV-2 Infection Treatment
### Direct Virucidal Properties  
The antiviral activity of honey may occur by different mechanisms. According to different authors, honey can inhibit viral infection by altering the structure of the surface protein, binding to target receptors on the virus, or interrupting the membrane proteins crucial for viral attachment and entry into the host cells [103; 104]. Moreover, due to the presence of several compounds, such as MGO, copper, ascorbic acid, flavonoids, nitric oxide, hydrogen peroxide, and its derivatives, honey can suppress viral growth by inhibiting viral replication and/or virucidal activity [126]. Recently, a review focused on in silico, in vitro and clinical studies of honey and propolis on COVID-19, and highlighted the positive effect of flavonoids, such as, rutin, naringin, and quercetin, on treatment against SARS-CoV-2. The trace element copper is a well-known inactivator of viruses, while phenolic compounds, ascorbic acid and hydrogen peroxide inhibit viral growth by disrupting viral transcription, translation, and replication [126]. Regarding hydrogen peroxide, routine daily honey intake might provide protection against SARS-CoV-2 due to the biocidal effect of this reactive oxygen species, helping to clean the throat from virus particles. Moreover, the physicochemical properties of honey, namely pH (that ranges between 3.5 and 4.5), osmolarity, viscosity, and thickness, can also contribute to the antimicrobial effects reported [127].  
Figure 3: Possible mechanisms responsible for honey bioactivity against SARS-CoV-2: (1) altering the viral structure: interaction of honey and its major components with structural and/or non-structural proteins in the virus; (2) binding to target receptors on the virus; (3) interrupting membrane proteins (crucial for the viral attachment and entry into the host cells). Illustrations were made with BioRender.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 6 Scheme Analysis
### Security Analysis
#### 6.1.2 User Anonymity  
All users operate with pseudo-identities and use ZKP technology for identity verification, which can ensure the correctness and validity of user identities without exposing any real private information.The security of the ZKP depends on the specific algorithm implementation. In our scheme, the generation and verification of proof are based on the Groth16 algorithm-implemented zk-SNARKs, which satisfies the completeness and soundness properties of a ZKP [27]. Therefore, user anonymity can be guaranteed during the interaction process.