esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### QIM Development
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute** & **Parameter** & **Description** & \\ \hline \multirow{8}{*}{**Appearance**} & \multirow{8}{*}{**Skin**} & Very bright and iridesent; Side brownish (\textgreater{}50\% covered) & 0 \\ \cline{3-4}  & & Bright; side brownish (\textless{}50\% covered) & 1 \\ \cline{3-4}  & & Pale and dull; Side greyish; mouth or operculum reddish-pink & 2 \\ \cline{3-4}  & & Marked; Superficial mucus; Blood on operculum and mouth & 3 \\ \cline{3-4}  & & Clean and closed & 0 \\ \cline{3-4}  & \multirow{2}{*}{**Anus**} & Abundant yellow feces & 1 \\ \cline{3-4}  & & Dry light brown feces and open & 2 \\ \cline{3-4}  & & Sea, seaweed & 0 \\ \cline{3-4}  & & Neutral & 1 \\ \cline{3-4}  & & Rancid; Metallic & 2 \\ \cline{3-4}  & & Putrid & 3 \\ \hline \multirow{2}{*}{**Texture**} & \multirow{2}{*}{**Firmness**} & In rigor mortis & 0 \\ \cline{3-4}  & & Firm, recovers shape fast (\(\leq\)2 sec) & 1 \\ \cline{3-4}  & & Softness; recovers shape slow (\textgreater{}2 sec) & 2 \\ \cline{3-4}  & & Clearly marked and spongy & 3 \\ \hline \multirow{8}{*}{**Eyes**} & \multirow{8}{*}{**Color**} & Black; shinny & 0 \\ \cline{3-4}  & & Black opaque/slightly milky & 1 \\ \cline{3-4}  & & Greyish/milky & 2 \\ \cline{3-4}  & & White/grey in partial or total eye & 3 \\ \cline{3-4}  & & Convex & 0 \\ \cline{3-4}  & & Flat & 1 \\ \cline{3-4}  & & Concave & 2 \\ \hline \multirow{8}{*}{**Gills**} & \multirow{8}{*}{**Color**} & Dark red & 0 \\ \cline{3-4}  & & Light red & 1 \\ \cline{3-4}  & & Brownish red & 2 \\ \cline{3-4}  & & Brown, grey, or discolored; Bacteria present & 3 \\ \cline{3-4}  & & Absent & 0 \\ \cline{3-4}  & & Present and colorless & 1 \\ \cline{3-4}  & & Whitish/cream & 2 \\ \cline{3-4}  & & Abundant, brown, or yellowish & 3 \\ \cline{3-4}  & & Sea or seaweed & 0 \\ \cline{3-4}  & & Neutral & 1 \\ \cline{3-4}  & & Rancid; metallic & 2 \\ \cline{3-4}  & & Putrid & 3 \\ \hline \multicolumn{2}{c}{**Total Demerit Points**} & & **0-25** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quality index method (QIM) scheme proposed for _S. dumerili_ containing a description for each parameter and the attributed scores (from 0 to 3).
## 6 Thematic Taxonomy of UWSNs
### Security
#### 6.4.3 Data Integrity and Confidentiality  
Additionally, another crucial security requirement is integrity. Each node has to preserve the confidentiality of the data during data transmission. Apart from the data, the packet head also must be encrypted to protect the identity of each node. Moreover, the message must be fresh. The node can ensure that old information from previous transmissions is not transmitted or received by implementing the time-varying technique. If the node has previously sent data, that particular node cannot deny that transmission which was performed by itself. This requirement is known as Nonrepudiation.  
Several studies on identifying existing forms of attack were conducted. Based on the findings, the invasion in UWSNs occurred during data transmission, in the form of denial-of-service (DoS), assaults on physical nodes, replication, and impersonation. According to [97], attacks of DoS often occur in UWSNs due to the high level of effectiveness of DoS attacks, difficulty in detection, and low costs of operation. The analysis of DoS attacks on the physical layer is discussed in detail by [98] and [99]. They tested the results in real test implementation. An attack on data is also a common security issue in UWSNs. Data management using information-centric architecture is one of the methods to protect the data from DoS attacks. However, strikes from smart DoS types are still capable of affecting the data. In consequence, ref. [100] incorporated the concept of machine learning in information-centric architecture to detect various types of mobile attackers.  
Based on previous research findings, security issues in UWSNs are primarily focused on security in localization, routing, data aggregation, trust model, and intrusion detection. Five processes proposed in [101] protect location by integrating the trust model in the evaluation process to further enhance the security features of the localization technique. The application of this trust model was improved by [102] in implementing centralized trust management in UWSNs using a cloud model. The objective of the trust management method is to determine the trustworthiness of each sensor node by applying a mathematical approach to obtaining trust evidence. In [103], the researchers implemented several experiments and found that an efficient encryption algorithm can preserve the confidentiality and integrity of the data. Additionally, the approach can also reduce overhead communication in the application layer. After the node deployment process, key generation based on RSS will allow pair nodes to update the secret keys at any time. A wireless sensor network uses the techniquecommonly, but the method is not appropriate for the harsh and inconsistent environment of UWSNs. Therefore, ref. [104] introduced a crucial multi-channel generation algorithm to produce more efficient secret bits to allow secure acoustic communications in UWSNs.
# Aerodynamics Analysis of Speed Skating Helmets: Investigation by CFD Simulations
## 4 Numerical Simulations
### Domain Discretization  
The discretization was performed with the _Ansys Fluent_ software with the meshing mode option activated. A scheme (.scm) code was generated in order to automatize the volume domain meshing process. This code was fed with two separate files: a.stl file containing the external geometries of the refinement regions or bodies of influence (BOI) and a.msh file containing surface mesh of the domain exterior and skater geometry. A thorough grid sensitivity study was performed as part of the verification assessment for the bare-head case. Four grid densities (18, 21, 34 and 51 million elements) were tested monitoring total drag of the skater, mesh quality in terms of skewness and mean flow velocity profiles at several x and y direction rakes around the skater. All the previously mentioned parameters suffered minimal variations when the mesh was refined to 34 million elements. In particular, the 34 million element count simulation grid ensured a mesh average skewness quality [24] indicator of 0.21, which lies in the range (0, 0.25), which is indicative of excellent mesh quality [25]. A simulation with a larger number of elements (51 million element mesh) resulted in a variation of the skater's C\({}_{D}\) of 0.45%, thus allowing one to conclude that mesh independence has been reached with 34 million elements. A total number of five bodies of influence or refinement volumes where strategically placed around the skater so as to reduce the number of meshing elements, as seen in Figure 4a. An unstructured discretization of tetrahedral elements was established with a global maximum, minimum element size and growth rates of 500 mm, 0.75 mm and 1.2 were defined respectively. The element sizes of the five bodies of influence were 250 mm, 140 mm, 70 mm, 20 mm and 10 mm, counting from outermost to closest to the skater respectively (Figure 5 top-left). A skater/helmet surface maximum, minimum element size and growth rates of 3 mm, 0.75 mm and 1.2, respectively, were selected. An extra refinement was applied to the skater/helmet near wall region, ensuring a correct application of the turbulence model [26]. This requirement was essential to resolve the viscous sublayer and buffer layer, correctly reproducing the nature of the boundary layer. Then, a prismatic inflation layer mesh of 15 layers with a growth rate of 1.4 [27] was implemented. The first node height was set at 0.02 mm. This configuration ensures a \(y^{+}<1.23\) along the whole surface of the skater/helmet. Figure 5 illustrates the previously described mesh in the median-plane of the fluid domain.  
Figure 4: Schematic of the simulation fluid domain: (**a**) isometric view showing domain zones and bodies of influence; and (**b**) domain size.
# Numerical Simulation on Corneal Surface Behavior Applying Luminous Beam Levels
## 5 Conclusions  
In this research, a numerical simulation characterizes the structural deformations of the corneal surface by the irradiance of different luminous levels. The proposed hypothesis states that the light exerts mechanical stress (compressive stress) on the corneal surface, taking into consideration the power and wavelengths of luminous beams and the mechanical properties of the human eyeball; those loads can generate partial deformations due to overexposure in specific periods. By using open-source code programs that apply the FEM, it was possible to develop a whole numerical scenario based on our own pseudo coding, considering mechanical properties, mathematical models, and the study case to analyze the behavior of the corneal surface under different loads. This method and the numerical simulation based on the C program language can be used for different structural static analyses of biological tissues from the human body. Outcomes show the structural displacements under the irradiation of light depending on the brightness, the distance, and the wavelength; common devices such as screens of smartphones show maximum displacements up to 9.07 \(\upmu\)m when the blue light of the screen is at 200 mm. On the other hand, it is important to consider the concentration of laser beams on surfaces; optical tweezers produce radiation of pressure which in nanometric and micrometric scales produces forces of light moving the particles of the irradiated surface. The cornea is the transparent structure through which the human organ filters and refracts the light. Therefore, a slight change in the cornea geometry significantly affects the optical tissue. In a viscoelastic material, these deformations totally disappear; however, because the cornea tends to thin with age, these effects grow exponentially, leading to pathogenesis or diseases in older people.  
Conceptualization, F.G.-L. and C.R.T.-S.; methodology, C.R.T.-S.; software, F.G.-L.; validation, J.A.F.-C., J.L.T.-A. and E.A.A.-A.; formal analysis, M.A.G.-C. and C.R.T.-S.; investigation, C.R.T.-S.; resources, M.A.G.-C.; data curation, F.G.-L. and J.L.T.-A.; writing--original draft preparation, F.G.-L. and J.A.F.-C.; writing--review and editing, C.R.T.-S.; visualization, M.A.G.-C.; supervision; project administration, E.A.A.-A.; funding acquisition, C.R.T.-S. All authors have read and agreed to the published version of the manuscript.  
The authors are thankful to the Consejo Nacional de Humanidades, Ciencia y Tecnologia (CONAHCyT), and the Instituto Politecnico Nacional for the support received in SIP 20231625 and SIP 20231131, as well as the EDI grant, all from SIP/IPN.  
Not applicable.  
The data presented in this study are available in article.  
The authors are thankful to the Instituto Politecnico Nacional and an EDI grant from SIP/IPN.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Colour Measurements  
Table 4 presents the L*, a*, b*, \(\Delta\)E, and BI for the cookies and L*, a*, b* for wheat flour and mushroom lyophilistates. Color is one of the most important features influencing consumer acceptance of a product. In turn, the type of flour or substitute used has a significant impact on the color of the cookies. The values of all color parameters for raw materials (wheat flour and mushroom powders) were different. The lightness (L*) was the highest for wheat flour, and among the mushroom powders, the powder from freeze-dried oyster mushrooms (L* = 87.25) was much lighter than that from button mushrooms (L* = 82.91). In turn, regarding chromatic values, a higher share of green color was recorded for oyster mushroom powder (a* = -0.86), a higher share of red color (a* = 1.47) for button mushroom powder, and higher values of the b* parameter (from 14.39 to 15.18) for mushroom powders compared with wheat flour (b* = 9.34). Traditional shorthead cookies are characterized by color ranging from yellow through golden to slightly brown. Control cookies were characterized by the highest value of the L* parameter (77.39). With the increase in the addition of mushroom lyophilistate, the value of this parameter was significantly lower, from 75.30 to 72.11 for cookies with _P. ostreatus_ powder and from 66.04 to 60.97 for cookies with the addition of _A. bisporus_. An inverse relationship was noted for the a* parameter of enriched cookies, for which the value was significantly higher (from 5.25 to 6.67), while the value of the a* parameter of control cookies was 3.10. In turn, the highest yellowness (b* parameter) was recorded for cookies
## 4 Results and Discussion  
### Micro-Tests after the Addition of B. pasteurii to CG and FA  
#### 4.1.1 Bacterial Concentration Test  
In Figure 3, the curves of absorbance (ABS) of FA group_1 and CG group_2 are similar to that of Reference_0, as ABS increases according to the stages of the _B. pasteurii_ growth curve. Each ABS vs. time curve shows almost the same three distinct phases: a short lag phase, a very rapid period of cell growth (exponential phase) and a maximum stationary phase, which continued until the 60 th hour (60 h was the time of observation) [43; 44].  
With respect to Reference_0, both FA group_1 and CG group_2 had a higher ABS, while the ABS in CG group_2 was the highest. This indicates that the addition of _B. pasteurii_ to CG and FA does not affect the normal growth of _B. pasteurii_, and a significant increment in biomass was observed in the two groups. Indeed, bacteria in a liquid environment tend to adhere to solid surfaces for growth, which fosters their multiplication and anticipates the beginning of biological processes [45; 46], such as that given in Equation (1). Thus, CG and FA culturing conditions can enhance the biomass of _B. pasteurii_, especially when it is an important factor influencing the mineralization performance.
# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
## 3 Results
### Hyperparameter Study
#### 3.2.2 Influence of Number of Network Layers  
It is widely accepted that the learning ability of the network can be enhanced by increasing the number of network layers. However, given the limitations of time and computing resources, it is desirable to find an optimal network depth during the model design process. Here, we studied five network depths (L = {2, 3, 4, 5, 6}) for the bifurcated blood vessel model with fixed epoch, and other hyperparameters held constant in the training.  
The corresponding convergence history curves are provided in Figure 11. It is apparent that the validation loss decreases significantly as the number of network layers increases. It is particularly interesting to note that the loss is relatively large between the case of two and that of three layers. The results confirm that the deep neural network models, while being more time consuming, have stronger learning ability than shallow neural network models. Meanwhile, it is also known that as the number of network layers increases, it eventually reaches a point of diminishing returns. Therefore, when optimizing the neural network structure, it is necessary to balance the prediction accuracy with computational time cost. In doing so, we determined that six layers were appropriate for the current network structure.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 4 Results and Experimentation  
This section explains the experimental setup and evaluates the recommended model's performance involving parameters shown in Table 3. LEACH, LEACH-C, PSO-NSA, and SEED are compared with our proposed algorithm EE-SS. Cluster lifetime, building time, and energy consumption are all taken into account when evaluating the performance of this clustering order to arrive at an average value, and we ran a total of ten simulations for each possible scenario. Initial nodes are planted in a three-dimensional free space, and their positions and directions are randomly determined. Nodes are assigned a transmission power based on their proximity to other nodes. When the new CHs are chosen, the CM works with the new CHs to plan their moment and communicate with them. Since they must rely on the messages of their constituents, CHs use more energy than CMs. In order to describe the clustering (re-clustering) criteria, we use the term pending (un-clustered) nodes. Clustering is required for pending nodes only. In Figure 5, we can see the virtual node's distribution in the concerned area.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Default Value** \\ \hline Monitoring field & 100 \(\times\) 100 \\ \hline Count of nodes & 100 \\ \hline Minimal distance among nodes & 2 m \\ \hline Simulation runs & 10 \\ \hline Simulation time & 120 s \\ \hline Base station position & (50, 50) \\ \hline Initial energy & 0.5 J \\ \hline Transmission range & 40 m \\ \hline Probability of turning a node as CH & 0.1 \\ \hline Energy for transmitting of each bit energy consumed for receiving & 50 \(\times\) 0.000000001 \\ \hline Tx/Rx electronics constant [2] & 50 nJ/bit \\ \hline Amplifier constant [1,2] & 10 pJ/bit/m\({}^{2}\) \\ \hline CH energy threshold [2] & 10–4 J \\ \hline Size of packet [2] & 30 bytes \\ \hline Packet rate [2] & 1 packet/s \\ \hline Sensing range [2] & 10 m \\ \hline Cluster radius [2] & 25 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameter.  
Figure 5: Deployment of nodes.  
All nodes are pending nodes in the first iteration of the round; however, in following iterations, two conditions must be met to declare a node as pending: (i) the node must be pending, and (ii) the node must be pending. Furthermore, when CH's energy falls below 25% of the energy of the cluster member with the highest energy in the cluster, all nodes in that cluster will be labeled as pending nodes. When two clusters come close enough to merge into a single cluster while moving through the operational area, nodes from both clusters will be labeled as pending nodes. If the total number of pending nodes in the network surpasses 25% of the nodes, clustering will be re-enabled. The CHs will maintain the cluster structure if the number of pending nodes in the network is less than 20% of the total nodes, and nodes will communicate with the elected CHs.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis
### Key Parameters Selection
#### 5.2.3 Number of Reference Tags  
We also discussed how the DNCL performance is affected by different distributions of reference tags to assess the effect of reference tags placement. We evenly distributed 4, 6, 8, 9, and 12 reference tags in the localization environment while keeping the other arrangements constant. In this case, the localization results of the tags are shown in Figure 9. We observed that the positioning error decreases with increasing the number of reference tags. However, even within the scenario where the number of reference tags is only four, the localization error is tolerable. Because NMDS uses the Euclidean distance information rank order relationship between each pair of tags for localization, the proposed algorithm has a high tolerance for errors.
## 3 Materials and Methods
### General Procedure for Solvent Screening (Table 1)  
2,4-dichloropyrimidine **1** (149 mg, 1.0 mmol) was dissolved/ suspended in the selected solvent (3.5 mL), where the air (oxygen) was displaced with argon before use. Then potassium carbonate K\({}_{2}\)CO\({}_{3}\) (415 mg, 3.0 mmol), phenylboronic acid **2** (122 mg, 1.0 mmol), and 5 mol% of tetrakis(triphenylphosphine)palladium(0) Pd(PPh\({}_{3}\))\({}_{4}\) (58 mg, 0.05 mmol) were added and stirred under argon atmosphere at selected temperature (i.e., 60, 80, or 100 \({}^{\circ}\)C). After 24 h, the reaction mixture was extracted with EtOAc, brine, and dried over anhydrous Na\({}_{2}\)SO\({}_{4}\). Solvents were removed under reduced pressure, the remaining solid was purified using flash column chromatography with EtOAc/n-Hex as an eluent.
## 4 Results and Discussion
### Metrics Obtained from the Different Datasets Generated with Open Hardware  
Five new public datasets (DSUALM, DSUALMH, UALM2, DSUALM10, and DSUALM10H) generated using the oZm v1, oZm v3, and OMPM devices [32] were reviewed. The datasets concerning NILM algorithms were analyzed and evaluated using NILMTK metrics. The goal was to guide the dataset selection and creation for NILM by comparing these metrics with other datasets.  
The datasets were created using devices with precise electrical measurements, enabling the capture of detailed usage patterns. By applying NILMTK metrics, this study evaluates the performance of the NILM algorithm across these datasets, helping to identify which datasets are most effective for specific applications. This analysis highlights the impact of factors like the sampling frequency and data resolution on NILM performance, contributing to advancements in energy efficiency and advanced grid management technologies.
# A Watermark-Based In-Situ Access Control Model for Image Big Data
## Security and Performance Analysis  
### Security Analysis  
**Defending Against Transferring Attack.** Images are encrypted by designated keys related to corresponding roles or accessor identifications, and accessors must present the correct keys to enable client tools to decrypt images for browsing. Encrypted images cannot be decrypted without keys, even if images are transferred to others again. Moreover, decrypted images can only be decrypted and displayed in client tools. Images will return to their original encrypted status after browsing.  
The control unit migrates to client tools and it maintains control even though images are redistributed again. The control policies are associated with images as watermarks, which specify what areas can be viewed for given roles. The decryption can only occur upon browsing, and the encrypted area returns back to confidential status after images are browsed in the client tools. That is, the encrypted areas (layers) are transparently decrypted and ephemerally displayed upon browsing.  
**Defending Against DDoS Attacks.** As access control logics are embedded in watermarks together with images, client tools can control access policies without consulting servers and relying on networking connections. Thus, DDoS attacks for servers and networking connections are not workable.  
**Defending Against Coarse Access.** Our model can differentiate the access privileges for various areas in a single image, and similarly, further access control for various layers in a single area are also possible iteratively.  
**Defending Physical Copy Attack.** As visible watermarks such as QR codes or invisible watermarks are incorporated with images, anyone who obtains physical copies of images by screen capture or outside camera shooting will be traced back by watermarks. The roles and identifications can be revealed by decrypted areas in captured images and control policies in watermarks.  
**Proposition 4**: _It is hard to compute \(k_{j}\) from \(k_{i}\) if \(k_{j}=g(k_{i})\), where \(f(\cdot)\) is a one-way function._  
**Proof.** Straightforward. We use a one-way function to drive keys in lower security levels from keys in higher security levels. As the function is one-way, the derivation of keys will be also one-way. That is, it is hard to compute \(x\) from \(g(x)\).