esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Brain Dataset Results
The network was able to learn the input image features as well as to generalize the brain segmentation problem. All the evaluations were made using data independent to training and the validation data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Dense-U-Net** & **Human** \\ \hline P.A. & 0.99721 \(\pm\) 0.00026 & 0.99510 \(\pm\) 0.00032 \\ Dice c. & 0.98870 \(\pm\) 0.00134 & 0.98087 \(\pm\) 0.00167 \\ Lo.U. & 0.97873 \(\pm\) 0.00165 & 0.96032 \(\pm\) 0.00189 \\ A.H.D. [voxel] & 0.01302 \(\pm\) 0.00183 & 0.02519 \(\pm\) 0.00342 \\ A.u.R.C. & 0.99463 \(\pm\) 0.00022 & 0.98413 \(\pm\) 0.00093 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fine-tuned model trained using 3-folds cross-validation, results includes standard deviation. Used neural network is Dense-U-Net network. Achieved results are compared to another human expert results. For evaluation, several metrics were used: pixel accuracy, Dice coefficient, intersection over union, average Hausdorff distance [voxel] and area under ROC curve. Figure 7: Visualisation of predictions from 3D benchmark brain models and FSL segmentation. From left to right—Dense-U-Net, Residual-U-Net, U-Net, FSL.
# A Trusted Multi-Cloud Brokerage System for Validating Cloud Services Using Ranking Heuristics
## 2 Literature Review
### Extraction of Service Trust Factors from Multi-Cloud Environment
#### 3.1.1 Identification of Trust Factors and Feedback
It is calculated by  
\[\text{Availability}=\frac{Total\ time\ for\ which\ service\ available}{Total\ service\ time} \tag{8}\]  
_Reliability (p8)_  
Reliability defines the successful service provisioning without failure, which is calculated by  
\[\text{Reliability}=\frac{Number\ of\ successful\ service\ provisioning}{Number\ of\ time\ service\ provisioning} \tag{9}\]  
_Stability (p9)_Stability measures the variations in the service performance, which is calculated by  
\[\text{SRTF}=\
icefrac{{ns}}{{N}} \tag{2}\]0  
This definition ensures that only consistent, SLA-compliant service deliveries are counted as stable, providing a realistic assessment of the provider's reliability. _Cost (p10)_  
Cost is calculated based on volume-based metrics to show the difference in heterogeneous resources such as the cost of one unit of storage, processing unit, cache memory, and network bandwidth. Cost is calculated by  
\[\text{SRTF}=\
icefrac{{ns}}{{N}} \tag{2}\]1  
_Throughput (p11)_  
Throughput is measured from number of tasks completed by the cloud service per unit of time, which is calculated by  
\[\text{SRTF}=\
icefrac{{ns}}{{N}} \tag{2}\]2  
_Efficiency (p12)_  
Efficiency defines the effective utilization of cloud services, which is measured by  
\[\text{SRTF}=\
icefrac{{ns}}{{N}} \tag{2}\]3  
Here, time overheads are infrastructure initial delay, inter task communication delays, etc. _Scalability (p13)_  
Scalability defines the ability of a system which can handle a large number of application requests simultaneously. There are two types of scalabilities, such as horizontal scalability, which increases the same type of cloud resources in \(vm\), and vertical scalability which increases the ability of cloud resources in \(vm\). Based on [PERSON]'s law  
\[\text{SRTF}=\
icefrac{{ns}}{{N}} \tag{2}\]4  
here \(p\) is the number of processors; \(\alpha\) is the fraction of calculation, which is sequential (vertical); and \(1-\alpha\) is the fraction of calculation which is parallel (horizontal). These 13 service trust factors consider the performance, transparency, cost, and user experience. Each factor is quantifiable, adaptable to IaaS, PaaS, and SaaS, and ensures objective, comparable rankings in the proposed model.
## 5 Communication Dependability  
In wide-area applications, R-SV/R-GOOSE communication must be dependable (real-time and cybersecure) to ensure correct and authentic operation of the applications.
## 4 The Application of the Proposed MASCSA Method for OSWHT Problem
### The Whole Application Procedure of MASCSA for OSWHT Problem  
The whole solution process of the optimal scheduling of the wind-hydro-thermal system with the fixed-head short-term model is described in Figure 3, as follows:  
Step 1: Set values to \(P_{s}\) and \(Iter^{max}\)  
Step 2: Randomly initialize \(So_{s}\) (\(s\)=1,..., \(P_{s}\)) within the lower and upper bounds  
Step 3: Calculate \(PW_{m,i}\) using Equation (12)  
Step 4: Calculate \(Q_{hp,i}\), \(q_{hp,i}\) and \(PH_{hp,i}\) using Equations (19), (20), and (7).  
Step 5: Calculate \(PT_{Nt,i}\) using Equation (21)  
Step 6: Calculate the fitness function using Equations (22)-(25)  
Step 7: Determine \(So_{Gbest}\) and set current iteration to 1 (\(Iter\)=1)  
Step 8: Generate new solutions using Equation (13) and correct the solutions  
Step 9: Calculate \(Q_{hp,i}\), \(q_{hp,i}\), and \(PH_{hp,i}\) using Equation (19), (20), and (7).  
Step 10: Calculate \(PT_{Nt,i}\) using Equation (21)  
Step 11: Calculate fitness function using Equations (22)-(25)  
Step 12: Compare \(FF_{s}^{new}\) and \(FF_{s}\) to keep better solutions  
Step 13: Generate new solutions using MAMM and correct the solutions  
Step 14: Calculate \(Q_{hp,i}\), \(q_{hp,i}\), and \(PH_{hp,i}\) using Equations (19), (20), and (7).  
Step 15: Calculate \(PT_{Nt,i}\) using Equation (21)  
Step 16: Calculate fitness function using Equations (22)-(25)  
Step 17: Apply NSM in Section 3.2.1.  
Step 18: Determine \(So_{Gbest}\)  
Step 19: If \(Iter\)= \(Iter^{max}\), stop the solution searching algorithm. Otherwise, set \(Iter\)= \(Iter\)+1 and go back to Step 8
## 4 Results and Discussion
### Observation Analysis: Physical Features, Interactions and Creativity
Additionally, this research provided insights into peoples' perceptions and behaviour in public spaces, within the context of satellite science precincts [85; 86]. The results support evidence from previous observations, that buildings and public spaces of modernistic satellite science campuses are spatially and socially disconnected from the immediate environment [7; 72], resulting in a masterplan with a 'weak spatial structure' [15]. Commonly, in such a type of campus, public spaces are placeless environments, overshadowed by fragmentation and remoteness. These are consequences of a top-down modernistic masterplan, which was considered to be a 'creative solution' in post-war Europe. Therefore, we suggest that, for the Dutch context, Zernike campus is a representative study case, given that satellite science campuses are similar in terms of the way in which they were developed and implemented.
## 2 Experimental Setup and Methods
### PTV Image Processing  
The PTV is a non-contact, transient whole-field particle velocity measuring method to identify the movement of sparse particles [30; 31]. Figure 4 shows the post-processing process of particle motion caught by the PIV system [32]. Within the square region (0.01 m \(\times\) 0.01 m) of the PIV image, the number of sand particles was counted to calculate the velocity and number density of sand particles according to the following Equations (1) and (2).  
\[v_{i}=\frac{\min(\sqrt{(x_{2i}-x_{1i})^{2}+(y_{2i}-y_{1i})^{2}})}{\Delta t}, \tag{1}\]  
\[\frac{S_{2i}}{S_{1i}}\leq 3, \tag{2}\]  
where \(\min(\sqrt{(x_{2i}-x_{1i})^{2}+(y_{2i}-y_{1i})^{2}})\) represents the minimum distance between all particles in the second frame and the \(i\) th particle in the first frame, \(v_{i}\) represents the velocity of the \(i\) th particle in the first frame image, \(\Delta t\) represents the time interval between two frames, \(S_{1i}\) is the sand particle area in the first frame, and \(S_{2i}\) is the sand particle area in the second frame.  
The sand particle number density \(N_{z}\), horizontal particle velocity \(v_{x}\), and horizontal sand flux \(Q_{x}\) around the three wind-break walls were calculated out from the obtained images according to the following Equations (3)-(5) [33].  
\[N_{z}=\frac{n_{z}}{\Delta x\Delta y\Delta z}, \tag{3}\]  
\[v_{x}=\frac{L_{x}}{\Delta t}, \tag{4}\]  
\[Q_{x}=\frac{\pi}{6}\rho\dot{x}^{3}N_{z}v_{x}, \tag{5}\]  
Figure 3: Configuration of numerical simulation. The dimensional channel size of the numerical simulation was consistent with the working section of the wind tunnel.  
where \(L_{x}\) is the moving distance of a particle along \(x\)-direction; \(\Delta t\) is the time interval of 110 us (\(u_{*}=0.9701\) m/s), 170 us (\(u_{*}=0.6595\) m/s) and 250 us (\(u_{*}=0.4756\) m/s) between two laser pulses; \(n_{z}\) is the total number of sand particles in a cuboid; \(\Delta x\), \(\Delta y\), and \(\Delta z\) are the three sides of the cuboid, in which both \(\Delta x\) and \(\Delta y\) are 0.01 m, and \(\Delta z\) (1 mm) is the depth of the laser; d (\(\sim\)160 um) is the average sand particle diameter in the railway track; and \(\rho\) is the particle density (2650 kg m\({}^{-3}\)).  
After the sedimentation of sand particles within I, II, and III on the railway track was weighed, the sand deposition rate was determined according to the following Equation (6).  
\[\lambda=\frac{m_{z}i}{t\cdot s_{i}}, \tag{6}\]  
where \(\lambda\) is the deposition rate, \(m\) is the total sedimentation of sand particles, \(t\) is the experimental time, and \(s_{i}\) is the area of I, II, and III.
###### Abstract  
In this study, a new approach was investigated to extract reference phases from the scanning acoustic microscope to calculate the speed of sound when dealing with the slope of the stage and fluctuation of the scanning lines. To capture the slope and the fluctuation pattern, data of the first lines along the horizontal and vertical axes on the stage were used. A corrective function was then utilized to improve the accuracy of reference phase extraction. The method was then corroborated by demonstrating tumor discrimination in mice skin by means of scanning acoustic microscopy (SAM). B16-F10 melanoma cells were used to grow the tumor. Hematoxylin and eosin (H&E) staining was applied for histology characterization of the sample. A comparison of both acoustics and histology was conducted. Phase analysis was performed to examine the effects of both slope and fluctuation. The results showed that our approach significantly improved the tumor detection and accuracy of scanning acoustic microscopy.  
reference phases; scanning acoustic microscopy; tissue characterization; slope; speed of sound; melanoma cells +
Footnote †: journal: Applied Sciences
## 2 Experimental System and Test Procedure
### Design of Flexible Device for Slope Subsurface Monitoring
Compared to other AE monitoring instruments, a significant improvement of the device is the novel mechanical structure of waveguides instead of conventional metal tubes. Based on this particular structure, the proposed device with excellent resilience can endure large deformations during a landslide to sustainably monitor subsurface deformation. Moreover, AE and mechanical parameters can be collected synchronously using one flexible device and then multiple parameters can be correlated and verified with each other.
## 6 Experimental Results in the GeoLife Case
### Step 2  
A further advantage is the reduction of execution times significantly. We found two candidate corridors, in the same path shared by a union of 22 trajectories, found in Steps 1 and 1.2.  
The grid is out of phase, because 500 (meters) is not a multiple of 1300 (meters), but this gives a greater number of trajectories that are recorded on the same road, with the union of the results obtained.  
Figure 5: Candidate corridor detected with Step 1.2.  
Figure 6: Trajectories mapping into a grid with cells of 500 m per side.  
Analysis of Step 2  
The output obtained for data in \(\Delta t_{1}\) was a list of frequent itemsets of cells, a table of 1398 \(K\)-frequent itemsets, for \(K\) from 1 to 9. The longest sets of cells were three 9-frequent cells:  
\(F=\{1956,1703,1804,1550,1905,1906,1652,1754,1855\}\),  
\(G=\{1956,1703,1550,1905,1906,1652,2007,1754,1855\}\) and  
\(H=\{1956,1703,1804,1905,1906,1652,2007,1754,1855\}\).  
Compared to the previous steps, the higher trajectory support (min_sup equal to 50) gives us shorter paths of shared cells.  
We set \(A=F\). Among the 8-frequent itemsets we looked for those that were not a subset of \(A\). If there was a 8-frequent itemset that shared at most one cell with \(F\), then we saved it as a potential corridor. At the end, we added to \(A\) all the elements of the 8-frequent itemsets that were not in \(A\) and we repeated the procedure with the 7-frequent itemsets. Continuing in this way until the 2-frequent itemsets, we found 7 potential corridors with at least 2 cells.  
Doing the same starting from \(G\) or \(H\), we found the same potential corridors of parameter \(M\) from 2 to 5:  
\(C_{1}=[1601,1602,1603,1604,1656]\), shared by 53 trajectories;  
\(C_{2}=[1753,1702,1703]\), shared by 58 trajectories;  
\(C_{3}=[1548,1549]\), shared by 58 trajectories;  
\(C_{4}=[1548,1599]\), shared by 64 trajectories;  
\(C_{5}=[1550,1551]\), shared by 91 trajectories;  
\(C_{6}=[1600,1601]\), shared by 74 trajectories;  
\(C_{7}=[1804,1805]\), shared by 53 trajectories.  
The performance of this procedure depends on the number of \(K\)- and \((K-1)\)-cells and size \(K\) for every iteration and on the number of new cells found until level \(K\).  
Starting for example from the candidate corridor \(C_{1}\) obtained, we carried out the filtering which is the final phase of our work. \(C_{1}\) is not contained in the last result of Apriori, which is formed by the 3 sets \(F\), \(G\), \(H\) of 9 cells each. So the length of the candidate corridor is less than 9 kilometers and its plot, zoomed in Figure 7, is given by 52 sub-trajectories that cross diagonally 5 adjacent cells.  
Figure 7: \(C_{1}\) candidate corridor detected in Step 2, with parameters \(M=5\) cells and \(S=52\) trajectories.
# Bi-Photon Entangled Airy Beams through Unstable Ocean Turbulence
## 3 Numerical Results and Discussion
The basic conditions of oceanic turbulence are essential for the design of an UOC system. Figure 6 illustrates the effect of the ratio of temperature and salinity contributions to the refractive index \(\varpi\) on the relative detection probability \(p_{rm_{1}}\). Different from atmospheric turbulence, both temperature and salinity fluctuations are important factors in determining OAM transmission in oceanic turbulence. As \(\varpi\) varies from 0 to \(-5\), the oceanic turbulence turns from salinity-induced dominated into temperature-induced dominated [9]. For the same distance, \(p_{rm_{1}}\) increases as \(\varpi\) approaches \(-5\) and gains its maximum value at \(\varpi=-5\). Physically it reveals that the OAM of bi-photon entangled Airy beams will experience fewer adverse effects of turbulence in temperature-induced dominated oceanic turbulence than salinity-induced dominated oceanic turbulence. Figure 5: Relative detection probability of signal photon versus propagation distance for different (**a**) wavelengths \(\lambda\) and (**b**) main ring radius \(r_{0}\). Figure 6: Relative detection probability of signal photon versus propagation distance for different ratios of temperature and salinity contributions to the refractive index \(\varpi\). Except for the ratio \(\varphi\), oceanic turbulence is also affected by turbulence factors, including \(L_{0}\), \(\eta\), \(\chi_{T}\) and \(\epsilon\), whose influences on the entangled Airy beams are illustrated in Figure 7. Since our employed oceanic spectrum taking outer scale of turbulence \(L_{0}\) into consideration agrees well with the experimental data in all wavenumber regions, we investigate the impacts of \(L_{0}\) and \(\eta\) on the OAM propagation. The outer- and the inner-scales define the scale sizes of turbulence eddies in the inertial range. The effective turbulence eddies in the inertial range would decrease with the increment of \(\eta\). Thus the beam scattering is reduced [12], corresponding to our result in Figure 7a, where \(p_{rms_{1}}\) increases when \(\eta\) increases. By comparing \(\eta\) and \(L_{0}\), we note that \(L_{0}\) has less influence on OAM detection. Then, we demonstrate the relative detection probability against different dissipation rates of mean-squared temperature \(\chi_{T}\) and different dissipation rates of turbulent kinetic energy per unit mass of fluid in Figure 7b. The probability decreases with increasing \(\chi_{T}\) and decreasing \(\epsilon\). It generally illustrates that a larger \(\chi_{T}\) and a smaller \(\epsilon\) correspond to stronger oceanic turbulence and, thus, a smaller detection probability [14].
## 3 Case Study
### Simulations Performed  
In order to evaluate the energy and comfort performance, three simulations were performed with DIGITHON for each combination of building structures and radiant systems:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type of Building**} & \multicolumn{2}{c}{**Design Heat Load**} & \multicolumn{2}{c}{**Water Temperature (\({}^{\circ}\)C) for Each Type of Radiant System**} \\ \cline{2-6}  & **(kW)** & **(W m\({}^{-2}\))** & **FW** & **FD** & **C** \\ \hline N & 3.77 & 37.7 & 29.8 & 28.6 & 35.7 \\ I-M & 2.39 & 23.9 & 27.1 & 26.4 & 30.8 \\ I-E & 2.29 & 22.9 & 26.9 & 26.2 & 30.5 \\ I-I & 2.28 & 22.8 & 26.9 & 26.2 & 30.5 \\ WI-E & 1.97 & 19.7 & 26.3 & 25.7 & 29.4 \\ WI-I & 1.98 & 19.8 & 26.3 & 25.7 & 29.4 \\ WI-X & 1.90 & 19.0 & 26.1 & 25.6 & 29.0 \\ WI-L & 1.94 & 19.4 & 26.2 & 25.6 & 29.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design heat load, specific heat load (per floor area) and supply water temperature evaluated considering a water temperature drop of 5 \({}^{\circ}\)C.  
Figure 5: Example of calculation of the mean value of the water temperature required to fulfil the design heating load of the non-insulated building (3770 W) with the three different types of radiant systems. Average temperatures of the active surfaces are 23.4 \({}^{\circ}\)C in the floor heating cases and 28.3 \({}^{\circ}\)C in the case of radiant ceiling.  
* an ideal convective simulation with air temperature set to 20.0 \({}^{\circ}\)C; and
* two radiant simulations with a reduced value of air temperature setting, so that the mean value of the operative temperature in the point P (in the middle of the room at 1.1 m height, as shown in Figure 2) in the period from December to February is the same of the ideal convective simulation. The radiant simulations were two because of the two examined control modalities of the supply water temperature (fixed and variable according to outdoor ambient temperature).  
The air temperature setting and the resulting average operative temperature in the different types of buildings are shown in Table 5.  
For the comfort analysis other 8 ideal convective simulations were done, one for each kind of building, removing the layers of the radiant system. In this way a comfort comparison was done between a building heated by a radiant system and the same building heated by an ideal convective system.  
The external heat transfer coefficient of the external walls was set to 25 W m\({}^{-2}\) K\({}^{-1}\) and the solar absorption coefficient was set to 0.6.  
As found in a previous work [23], the choice of constant values for convective heat transfer coefficients instead of variable values implies negligible changes in the overall thermal balance calculated by DIGITHON. The convective heat transfer coefficient used for the different heated and unheated surfaces are reported in Table 6.  
Infiltrations and internal gains (Table 7) have been maintained fixed in all the simulations.
# A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models
## 6 Mitigation Strategies for CIA Dimensions in LLM Attacks
### Mitigating Confidentiality Attacks  
Confidentiality ensures that sensitive information remains protected from unauthorized access or exposure. Prompt attacks such as data extraction and model inversion exploit the ability of LLMs to recall sensitive or proprietary information inadvertently. Effective mitigation strategies include the following:  
* **Differential Privacy:** Adding noise to the training data prevents the model from memorizing individual records, significantly reducing the risk of unintentional data leakage. For example, OpenAI's GPT models have implemented techniques including differential privacy to limit the retrieval of training data, protecting against attacks  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**CIA** & **Technique 1** & **Technique 2** & **Technique 3** \\ \hline Confidentiality & Diff. Privacy & Access Control & Audits \\ Integrity & Input Validation & Adv. Training & Bias Fix \\ Availability & Rate Limit & Context Mgmt & Anomaly Det. \\ \hline \hline \end{tabular}
\end{table}
Table 8: CIA dimensions with mitigation techniques.  
such as the one demonstrated by [PERSON] et al. [42] where adversaries extracted names and sensitive details from training sets used by LLMs.
* **Access Controls:** Restricting access to sensitive APIs and limiting query access to privileged users helps to prevent unauthorized prompts from accessing sensitive information. A notable case involved financial institutions implementing token-based API restrictions to secure access to transaction data from unauthorized actors attempting prompt injection.
* **Regular Audits:** Conducting periodic reviews of model outputs helps to identify and mitigate inadvertent data leakage. This practice became essential after an incident where a leaked ChatGPT response inadvertently exposed proprietary business strategies due to inadequate output filtering.
* **Prompt Monitoring Tools:** Deploying tools that analyze query patterns can flag and block malicious prompt sequences. For example, companies have started adopting real-time prompt analysis to detect and prevent extraction attacks, such as those mimicking legal or healthcare inquiries to elicit private data.
## 3 Performance Analysis of iPyroxy Block
### S21 Transmission Coefficient with Block  
The system demonstrates exceptional performance with a mutual coupling coefficient consistently measuring below \(-15\) dB across the entire operating range. The S21 transmission coefficient, evaluated without including a blocking component, further emphasizes the system's capability to transmit signals efficiently with minimal coupling between elements. The low mutual coupling coefficient signifies effective isolation between different components or channels within the system (Figure 9). The S21 transmission coefficient, unaffected by a blocking element, showcases the inherent ability of the system to maintain a high level of signal integrity and minimize signal leakage between interconnected elements. This performance characteristic is crucial in applications where precise signal transmission is vital, such as in wireless communication systems where minimizing interference between channels is essential for achieving reliable and high-quality communication.  
Figure 8: (a) S21 VNA without iPyroxy block; (b) S21 transmission coefficient without block.  
Figure 9: (a) S21 VNA with iPyroxy block; (b) S21 transmission coefficient without block.