esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### RAN Slicing
#### 3.2.2 Mobility Support  
When the device moves from one cell to another, a handover procedure is triggered. The handover request (from the source gNB to the target gNB) includes the network slices assigned to the UE, specifying the tuple [S-NSSAI, PLMN ID] for each active PDU session. According to the principles listed earlier, it is clear that handover requests between gNBs from the same tracking area are always successful. However, in the case of mobility outside a tracking area, it might happen that one or more PDU sessions could not be transferred, because the associated S-NSSAI are not available in the target cell. In traditional radio control admission solutions, where handover acceptance is subject to the admission of all radio bearers, this scenario would result in an automatic handover rejection. To solve this all-or-nothing approach, partial admission control mechanisms are being developed. These mechanisms allow the admission of those PDU sessions whose associated S-NSSAIs are supported in the target gNB. The logic is as follows:  
* The target gNB will send handover request ACK with Admitted PDU session and Not Admitted PDU session.
* If all the S-NSSAIs in the handover request are not admitted, the handover will be rejected.  
Figure 5 shows an example of mobility support using this partial admission control for handover management.  
Figure 5: Slice-aware mobility.
# Solid-Phase Extraction of Organic Dyes on Mixed-Ligand Zr(IV) Metal-Organic Framework
## 3 Results and Discussion
### Solid-Phase Extraction of Organic Dyes
The K\({}_{\rm L}\) values (0.93, 2.16, and 3.12 L mg\({}^{-1}\) at 283, 291, and 308 K, respectively) indicate an increase in dye adsorption on the sorbent with increasing temperature. The K\({}_{\rm F}\) and 1/n values increase and decrease with increasing temperature, respectively. The 1/n values were between 0 and 1, indicating favorable adsorption. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dyes**} & \multirow{2}{*}{**T [K]**} & **Pseudo First-Order Kinetics** & \multicolumn{2}{c}{**Langmuir Model**} & \multicolumn{2}{c}{**Freundlich Model**} & \multirow{2}{*}{**R\({}^{2}\)**} \\  & & **Constant \(k_{1}\)[min\({}^{-1}\)]** & \multicolumn{1}{c}{**K\({}_{\rm L}\)[L/mg]**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**K\({}_{\rm F}\)[mg\({}^{-1/n}\) L\({}^{1/n}\) g\({}^{-1}\)]**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \hline \multirow{4}{*}{CR} & 283 & 0.0173 & 0.93 & 0.996 & 3.17 & 0.912 \\  & 291 & 0.0555 & 2.16 & 0.991 & 4.12 & 0.956 \\  & 308 & 0.317 & 3.12 & 0.998 & 5.4 & 0.974 \\  & 283 & 0.078 & 0.85 & 0.895 & 2.76 & 0.889 \\ \cline{1-1}  & 291 & 0.08 & 1.96 & 0.981 & 3.94 & 0.954 \\ \cline{1-1}  & 308 & 0.082 & 2.74 & 0.915 & 4.22 & 0.963 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Best fit parameters for the dye adsorption onto the sorbent by the Langmuir and Freundlich models. Figure 8: Dependence of the removal efficiency of CR from an aqueous solution on pH. (**A**)—CR solution after 1 min of extraction at pH = 3 (**1**), 7 (**2**), and 11 (**3**). (**B**)—CR solution one day after reaching equilibrium of the solid-phase extraction at pH = 3 (**1**), 7 (**2**), and 11 (**3**). (**4**)—Initial solution. Figure 9: Kinetic model of adsorption in linearized form at 283 (**A**), 291 (**B**), and 308 K (**C**). The calculated values of K\({}_{\rm D}\) are presented in Table 2 and the dependence of the distribution constant on the reciprocal temperature (Figure 12) was used to calculate the thermodynamic parameters. Figure 11: Langmuir isotherms of CR and MV at 283 (**A**), 291 (**B**), and 308 K (**C**). Figure 10: Freundlich isotherms in linearized form at 283 (**A**), 291 (**B**), and 308 K (**C**). As shown in Table 3, the negative values of \(\Delta\)G\({}^{0}\) at temperatures of 283, 291, and 308 K indicate the spontaneous nature of the adsorption process. \(\Delta\)G\({}^{0}\) decreases with an increase in the temperature, which indicates more efficient adsorption at a higher temperature. In addition, a positive \(\Delta\)S\({}^{0}\) value indicates an increase in the degrees of freedom at the solid-liquid interface during the adsorption of dyes on the sorbent and reflects the affinity of the sorbent for dye ions in aqueous solutions and may indicate some structural changes in the adsorbents.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 5 Sensitivity of Pile Head Displacement under Scour
### Pile Diameter  
The pile length and wall thickness of large diameter monopile is fixed, and the pile diameters of 4, 4.5, 5, 5.5, and 6 m are input in sequence. When the pile diameter increases from 4 m to 6 m, the horizontal displacement of pile head decreases from 47.7 mm (\(11.925\times 10^{-3}\)) to 26.9 mm (\(6.725\times 10^{-3}\)), which is 58% of the original value. The scour sensitivity decreased slightly from 2.35 to 2.23. The normalized displacement and sensitivity of pile head calculated under different pile diameters are shown in Figure 16. This illustrates that increasing the pile diameter will reduce the pile displacement, and the susceptibility to scour decreases slightly.
## 2 Numerical Simulation Calculation Methods and Model Validation
### Numerical Simulation Calculation Methods  
#### 2.1.1 Meshing  
To improve calculation efficiency, the geometric model of the fan is simplified, and the entire axial fan duct is divided into four ducts: the inlet duct, the rotation duct, the OGVs duct, and the outlet duct. A simplified diagram of the turbine duct is shown in Figure 2a.  
The core components of the axial flow fan, the moving blades, and the OGVs are divided into structural grids by TurboGrid to improve calculation accuracy. The rotation duct grid is shown in Figure 2b, and the OGVs duct grids are shown in Figure 2c, while the more regular shape of the inlet and outlet ducts are divided into O-block grids.  
Figure 1: Structure of a nuclear grade axial flow fan. 1. Fairing. 2. Moving blades. 3. OGVs. 4. Shell. 5. Electrical machine. 6. Diffuser. (**a**) Object picture. (**b**) Structural diagram.  
For other ducts, due to the complex and irregular shape of the structure, the grid is divided by an unstructured grid, the grid density is increased, and the first node should be placed on the bottom of the viscous area, where \(y^{+}\) needs to be \(\leq\)5. The empirical formula of \(y^{+}\) is given by Equation (1) [24]:  
\[Y_{\text{wall}}=6\left(\frac{Y_{\text{ref}}}{v}\right)^{-\frac{7}{8}}\left( \frac{L_{\text{ref}}}{2}\right)^{\frac{1}{8}}y^{+} \tag{1}\]  
where \(Y_{\text{wall}}\) is the height of the first layer of the boundary layer grid in mm; \(V_{\text{ref}}\) is the reference speed in m/s; \(L_{\text{ref}}\) is the reference length in m; \(v\) is the fluid kinematic viscosity in m\({}^{2}\)/s, and \(y^{+}\) is a dimensionless parameter, indicating the boundary point between the viscous bottom and logarithmic layers. According to the empirical formula, the height of the first layer of the boundary layer grid should be less than 0.72 mm.  
After grid quality has reached the required level, the independence of the calculation grid also needs to be verified. As shown in Figure 3, when the total grid number of the axial flow fan reaches about 5.5 \(\times\) 10\({}^{6}\), the value of the total pressure of the axial flow fan obtained from the numerical simulation is unchanged. Therefore, taking into account calculation accuracy and time, the number of grids for the entire basin of the fan is 5,570,210.  
Figure 2: Grid model. 1. Inlet duct grid. 2. Rotation duct grid. 3. OGV duct grid. 4. Diffuser duct grid. 5. Outlet duct grid. (**a**) Complete duct grid. (**b**) Rotation duct grid. (**c**) OGVs grid.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 2 Related Work
### Public-Key Cryptography  
The main key management approach that employs public-key cryptography is based on the Secure Sockets Layer (SSL) handshake (The SSL Protocol Version 3.0. [[https://tools.ietf.org/html/rfc6101](https://tools.ietf.org/html/rfc6101)]([https://tools.ietf.org/html/rfc6101](https://tools.ietf.org/html/rfc6101))). An implementation has been analyzed in [17]. In this approach, each node owns a couple of public and secret keys. Moreover, each node owns a certificate that guarantees its authenticity and the public key of the administrator. The certificate is composed of the public key of the node, its identification number, and a signature computed by the administrator with its secret key. Any node can verify the authenticity of a certificate and can encrypt the messages used for the handshake with the public key of the other node. The main problem of this approach is represented by the computational and communication overheads. Each node must encrypt and decrypt a message and verify the signature of the certificate. Moreover, each node must send to the other their certificate and an encrypted message. For the sake of simplicity, in the following the described approach is called SSL.  
Public-key cryptography has been also successfully used for specific tasks or contexts. In [19], the authors present a key-management scheme based on ECC for hierarchical networks with heterogeneous nodes. In [10; 20], the authors propose two acceleration mechanisms for public-key signature verification in broadcast authentication.
## 3 Methods
### Principle of SBAS-InSAR  
Among N time series (\(t_{1}\), \(t_{2}\),... \(t_{N}\)) SAR images, a SAR image is supposed to be chosen as the super master image automatically, and the remaining N-1 SAR images are aligned with the super master image as slave images. Consequently, after the differential interference processing of each image pair, the differential interference phase of random  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. 1** & **Date** & **No.** & **Date** & **No.** & **Date** \\ \hline  
\hline \hline \end{tabular}  
* No.’ is the image number relating to the 64 SAR images.  
\end{table}
Table 2: Detailed parameters of Sentinel-1A data used in this study.  
pixel x in the interferogram [35] created by any two SAR images at moment \(t_{A}\) and \(t_{B}\) relative to super master image (\(t_{M}\)) can be expressed as follows (Equation (1)):  
\[\delta\phi_{l,x}^{\text{int}}=(\phi_{l,x}^{t_{B}}-\phi_{l,x}^{t_{A}})=\frac{4\pi \Big{(}d_{i,x}^{t_{B}}-d_{i,x}^{t_{A}}\Big{)}}{\lambda}+\phi_{i,x}^{topo}+\phi_{ i,x}^{attn}+\phi_{i,x}^{orb}+\phi_{i,x}^{noi} \tag{1}\]  
where \(\phi_{l,x}^{t_{A}}\) and \(\phi_{l,x}^{t_{B}}\) are the phases of pixel \(x\) at moment \(t_{A}\) and \(t_{B};\lambda\) is the wavelength of the radar signal; \(d_{i,x}^{t_{B}}\) and \(d_{i,x}^{t_{A}}\) are the relative accumulative deformations in the direction of the line of sight of pixel \(x\) at moment \(t_{A}\) and \(t_{B}\); and \(\phi_{i,x}^{topo}\), \(\phi_{i,x}^{attn}\), \(\phi_{i,x}^{orb}\), and \(\phi_{i,x}^{noi}\) are the topographic phase error, atmospheric phase error, orbit phase error, and noise phase error, respectively.  
After eliminating diverse phase errors, accumulative deformation in the time interval between \(t_{A}\) and \(t_{B}\) can be seen as the product of average phase deformation velocity and the time interval under the assumption of linear deformation; thus, average phase deformation velocity can be formulated as follows (Equation (2)):  
\[v_{i}=\frac{d_{i}^{t_{B}}-d_{i}^{t_{A}}}{t_{B}-t_{A}} \tag{2}\]  
Through formulation of the above-mentioned equations for the differential interference phase of pixel \(x\) on every interference image pair, we can obtain the matrix form [36] as follows (Equation (3)):  
\[Bv=\delta\phi \tag{3}\]  
The average phase deformation velocity of pixel \(x\) at an N-1 time interval can be solved by singular value decomposition (SVD) from the matrix equation (Equation (3)). Consequently, the time series of relatively cumulative deformations can be obtained further by integrating average phase deformation velocity at each time interval.
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 7 Future Directions  
Some challenges in this topic stem from the required robustness and self-adaptation capability of analysis algorithms that operate adequately in non-controlled environments under non-professional guidance or support and must provide very high levels of sensitivity and specificity. In these contexts, we highlight the following requirements that have a decisive impact on the adoption of the technology:  
**Usability.** Solutions must be as simple as possible to provide high usability levels. In clinical applications, patients are mainly elderly people with significant physical impairment. To avoid impacting long-term utilization, systems should be applicable without any or much-reduced assistance. Single-signal solutions usually lend themselves to realizations with higher degrees of usability.  
**Robustness.** Solutions must operate under non-controlled environments and will be handled by non-experts. Robustness and self-adaptation/self-calibration capability will directly impact the long-term adoption of health solutions, as it will affect confidence in the system and usability. False-positive rates must be as low as possible to avoid unnecessarily alarming the user. On the other hand, false negatives must be avoided in order to not compromise confidence in the system.  
**Computational complexity.** Solutions must be efficient in terms of computational cost to provide feedback to users in real-time and for energy efficiency reasons.  
**Energy efficiency.** Health systems must be energy-efficient to avoid frequent battery charges, which would affect the usability of the system. Single-channel solutions usually significantly impact the required hardware and local processing, lending themselves to more straightforward and energy-efficient realizations.  
From the above indicators, it becomes clear that existing methods do not adequately meet these requirements; therefore, further research is necessary.  
**Author Contributions:** Conceptualization, N.E.N.-R., L.M.A.-V. and D.I.I.-Z.; methodology, N.E.N.-R. and E.A.G.-R.; formal analysis, N.E.N.-R., E.A.G.-R., G.N.-R., R.R.-D.L. and A.S.; investigation, N.E.N.-R., E.A.G.-R., G.N.-R., R.R.-D.L. and A.S.; writing--original draft preparation, N.E.N.-R., E.A.G.-R., G.N.-R., R.R.-D.L. and A.S.; writing--review and editing, L.M.A.-V. and D.I.I.-Z.; visualization, N.E.N.-R., E.A.G.-R. and G.N.-R.; supervision, L.M.A.-V. and D.I.I.-Z.; project administration, N.E.N.-R. and E.A.G.-R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Tecnologico de Monterrey and CONACyT (CVU: 963990).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Acknowledgments:** Thanks to [PERSON] for the artwork of Figure 1.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
## 2 Compression Scheme  
In this work, a loop predictor is used for near-accurate prediction of target pixels by removing the correlation of hyperspectral images. The input image data is passed through the loop predictor point by point. The differences between the predicted and original values are quantized. Subsequently, the entropy encoder encodes the quantized residual image and outputs the compressed codestream. The same algorithm with the same parameters must be used at the decoding stage during the reconstruction process after inverse entropy coding. Figure 1 presents a pictorial representation of the compression scheme, which explains the essential steps. The five significant steps of the proposed scheme, including spatial predictor, spectral predictor, controlled quantization, sample representative, and entropy coding, are discussed in the following subsections.
## 3 Results and Discussion
### Influence Factor Analysis  
According to the analysis of Section 3.1, we know that the real WOB and ROP improve significantly after applying vibration at the surface, which means that the drill-string axial vibration at the surface technology is effective. However, the questions as to whether it is efficient and how to set the vibrating parameters should be answered. In the application process of this technology, we can only adjust the amplitude and the frequency of the  
Figure 4: The effect of horizontal section length on WOB.  
Figure 5: The effect of friction coefficients on WOB and ROP.  
exciting force to control the effectiveness of load transfer. Figure 7 shows the influences of the amplitude and the frequency of the exciting forces on the WOB and ROP. As can be seen from Figure 7, the WOB becomes smooth and fluctuates less with the increase in the amplitude or frequency of the exciting force. The stick-slip phenomenon is suppressed significantly. The WOB and ROP are sensitive to the exciting frequency at a low exciting amplitude, but they are rarely affected at a larger exciting force. Therefore, better load transfer results can be obtained at a higher exciting force amplitude even though the exciting frequency changes a lot in drilling.
## 1 Introduction
Therefore, this work thoroughly analyzes the contributions of various features and classification algorithms to detecting phishing attacks to find the best classifier and feature set for improved detection. For this purpose, a brand-new dataset was prepared and made publicly available at [[https://github.com/sibelkapan/phishing_dataset](https://github.com/sibelkapan/phishing_dataset)]([https://github.com/sibelkapan/phishing_dataset](https://github.com/sibelkapan/phishing_dataset)) (accessed on 10 October 2023) for the research community. The dataset consists of 500 phishing and 500 legitimate websites represented with 25 features that are composed of uniform resource locator (URL), hypertext markup language (HTML), and hypertext transfer protocol (HTTP) attributes. Using an exhaustive selection strategy, every combination of these feature groups was then fed into various well-known classification algorithms, including k-nearest neighbors (k-NN), support vector machine (SVM), naive Bayes (NB), decision tree (DT), multi-layer perceptron (MLP), and stochastic gradient descent (SGD) [5; 6], to detect phishing websites. While evaluating the effectiveness of the different features and classifiers, five different performance metrics, including accuracy, precision, recall, F1-score, and classification time, were utilized [5; 6]. During the experiments, two existing datasets were also used in addition to the newly introduced dataset. Hence, the main contributions of our work can be summarized as follows:  
* Introducing a new publicly available dataset for phishing attack detection,
* Comparing the performance of the features and finding the best feature subset for phishing attack detection in terms of different performance metrics,
* Comparing the performance of the classification algorithms and finding the best classifier for phishing attack detection in terms of different performance metrics.
# Effect of Systemic Zoledronic Acid Dosing Regimens on Bone Regeneration in Osteoporotic Rats
## 3 Results
### Histomorphometric Analysis  
BA% and RBG% were determined for the prepared histological specimens. One sample of the ZA-Pre+Post group was not included into the measurements due to the loss of this specimen in the histological preparation procedure.  
Data showed mean BA% of 50.1 \(\pm\) 3.5% for ZA-Pre, 49.2 \(\pm\) 8.2% for ZA-Post, 40.7 \(\pm\) 16.0% for ZA-Pre+Post, and 35.4 \(\pm\) 5.4% for saline control group. A repeated-measures analysis of variance with [PERSON]'s multiple comparison test indicated a significant difference in BA% between ZA-Pre and saline control (\(p\) = 0.031) and ZA-Post and saline control (\(p\) = 0.043). ZA-Pre+Post group and saline control group showed similar BA% (\(p\) = 0.663; Figure 4).  
RBG% is considered e a measure for the osteocl activity and dimensional stability of a bone substitute material. RBG% values were 44.5 \(\pm\) 8.8% for ZA-Pre, 47.1 \(\pm\) 10.4% for ZA-Post, 36.3 \(\pm\) 7.6% for ZA-Pre+Post, and 42.3 \(\pm\) 7.5% for saline control group. No significant differences in RBG% existed between the various experimental groups (\(p\) > 0.5; Figure 5).  
Figure 3: Representative histological images at high magnification showing bovine bone granules (yellow stars) well integrated into and completely covered by newly formed bone (NB) and osteoclast-like cells (black arrows). The left image represents the saline group, and the right image represents the ZA-post group.
## 2 Materials and Methods
### Features of the Test Field  
As the test field, a part of the fortress of Saint [PERSON] (aka Fortezza da Basso) in Florence was chosen. A previous survey of the entire fortress was performed by the University of Florence's GECO (Geomatics for Environment and Conservation of Cultural Heritage) Laboratory in 2015-2016 in order to meet the municipality of Florence's needs to support the refurbishment of the Fortress [28]. An agreement was signed between the Italian Military Geographic Institute, the University of Florence, the National Research Council Institute for the Preservation and Enhancement of the Cultural Heritage, and the Florence City Council. A scientific committee, headed by Professor [PERSON], coordinated a critical survey, material testing, diagnostic investigations, and stratigraphic analyses to define the building's state of preservation. Using an articulated system of topographic networks allowing the measurement of 196 control points, the three-dimensional (3D) surveying of the fortress was performed using terrestrial photogrammetry (more than 3000 images, mainly in the external walls), TLS (725 scans, almost 20 billion points in the internal areas), and Unmanned Aerial Vehicle UAV photogrammetry (about 2500 images for orthophotos of the walls and roofs). Moreover, some first tests were done with IMMSs (the Leica Pegasus Backpack and the GeoSlam Zeb-Revo) in a stretch of the underground passageways.  
For the examination of IMMSs presented in this paper, two different paths (one closed and one open) were defined. Both paths are set out in the entrance part of the Fortress and both consist of indoor and outdoor environments. The surfaces to be measured consist of ancient structures and buildings, in order to recreate conditions that are as similar as possible to real surveying works and, at the same time to compare different systems in similar conditions.  
The ground is not even everywhere, and small stairs and ramps connect the different spaces. The planned paths pass through a big, octagonal room, with an 8-m-high vault, connected by a small passage to a smaller rectangular room and, at the end of a small stairway, there is an irregular room with a big pillar in the middle and without a complete vault. Although spotlights are present in some inner spaces, they are darker than the outside. Along the outside part of the path, some noisy objects (trees, railings) are in the scene, and some others were inevitably moved during the tests (doors, crush barriers, cars).  
Figure 2 shows the paths: the first one is shaped like a number eight and its end point (A) corresponds approximately to the start point (P); the second one is shorter, the start and end points do not correspond, and are at different heights as well.  
The duration of the walk along the test paths was, for all the systems, about ten minutes.  
Figure 2: The test footpaths: (**a**) path 1, closed; (**b**) path 2, open, with an elevation difference from the start point (P) to the end point (A).