esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Trust Adaptation
### Experimental Results
#### 5.2.1 Test Environment  
Figure 7 illustrates two cloud providers: OpenStack [58] and AmazonWS [59]. In each domain, we have set up a Virtual Machine (VM) IDaaS with an orchestration engine and an IdP.  
Figure 6: (**a**) Protect relationship; (**b**) Trust relationship.  
We have developed two Web services to simulate a B2B relationship: A shopping service receives purchase orders from the user and then calls a delivery service for shipping the parcel. In the first scenario, the delivery service is a logistic department from the same company. From the technical point of view, they are two application components belonging to the same tenant, and the delivery service trusts the shopping service for user request authentication. In the second scenario, the delivery service is an external cloud service from a different tenant. Thus, it needs to authenticate and authorise the user request again.  
The migration process happens in the following phases. In the first phase, Web services are developed offline. They are unprotected and migrated to OpenStack. The IDaaS in OpenStack then provisions and updates the AAI implementation for the Web services to trust each other. In the second phase, the delivery service is migrated from OpenStack to AmazonWS. In the second domain, the delivery service not only provides Single Sign-On access for existing users in the IDaaS of AmazonWS but also maintains the existing trust with the shopping service in OpenStack.  
We use the open-source software WSO2 [60] and Alien4 Cloud [61] to implement the IdP and the orchestrator, respectively. We use Alien4 Cloud to control the VMs and the life cycle of the AAI components inside the VMs. We have developed the implementation artifacts and deployment artifacts for these components. At runtime, the orchestrator sends the artifacts to the VM (via SSH) and calls these artifacts during the life cycle of the components. Alternatively, Alien4 Cloud can deploy an orchestration agent on the VM (e.g., via Cloud-Init [62]). The orchestration agent then receives our artifacts via a messaging queue in a private network and executes them on the VM [63].
## 3 Results and Discussion
### Effectiveness Analysis
#### 3.2.4 Final Questions: VR Design Study  
After completing the experiment and the efficacy questionnaire, the participants were asked to complete a post-experiment survey to gauge the VR design. The survey contained the following questions:  
1. How easy was it to use this system?
2. How memorable did you find using this system?
3. How intuitive/easy was it to visualize data using this system?
4. How intuitive/easy was it to subset data using this system?
5. How intuitive/easy was it to merge data using this system?
6. Which system would you prefer to use?  
The questions were ranked on a 5-point Likert scale. Figure 16 presents the distributions for these final survey questions. The results show that most participants found IVEE easy or very easy to use (93.3%). Only one participant reported finding IVEE somewhat easy to use, and one participant found it moderately easy to use. The results also show that most of the participants found IVEE easy to remember (90%). This is because IVEE relies on easy interaction techniques, which makes it more memorable than its equivalent traditional data analytics software. Two participants found IVEE to be moderately memorable, and only one participant found it to be somewhat memorable. In addition, regarding intuitiveness, most participants found IVEE to be intuitive or very intuitive to use (more than 90%) and all participants completed the assigned tasks within IVEE correctly. The final question asked the participants to select their preferred interface. Out of 30 participants, 26 preferred IVEE's interface over R's. These results are encouraging and serve as the basis for further developing additional NUIs for handling various analytics problems. An ANOVA test was also conducted to observe the effect of the final questions on the time it took the participants to complete the tutorial tasks in R.  
Table 9 shows the results of this ANOVA test, which indicate that only the perceived level of data visualization intuitiveness had an impact on the time it took student participants to complete their tasks in VR.  
Figure 16: Distributions of final survey questions.
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
## 2 Materials and Methods  
As mentioned earlier, the objective of this work is to create a device capable of measuring the surface of silicon wafers using the stitching technique.  
First, the most important step is to construct a wavefront phase sensor. This can be done in two ways: reflection (see Figure 2) or refraction (see Figure 3). The technique involves capturing two images before and after the conjugate plane with respect to the sample. Two planes within an optical system are said to be conjugate planes if the intensity distribution across one plane is an image (generally magnified or demagnified) of the  
Figure 1: Definition of the geometry of silicon wafers.  
intensity distribution across the other plane. Likewise, two points are said to be conjugate points if one is the image of the other.  
To achieve this, a collimated beam of light is directed towards the surface of the silicon wafer. The incident light interacts with the surface, then the reflected light is captured by a camera, as said before, at two different points. The captured images are then processed using advanced algorithms to reconstruct the complete surface profile of the wafer.  
The stitching technique can be used to combine multiple measurements taken from different areas of the wafer, allowing a more comprehensive and accurate representation of the surface to be obtained. This is particularly useful in applications where the surface features of the wafer exhibit variations across different regions.  
Overall, the development of a wavefront phase sensor and the implementation of the stitching technique enable the creation of an apparatus capable of accurately measuring the surface of silicon wafers with high axial and lateral resolution.  
Figure 3: Refractive method. P and P’ are the planes in the object plane where the intensity can be measured to obtain the wavefront, and S is the sample surface.  
Figure 2: Reflective method. P and P’ are the planes in the object plane where the intensity can be measured to obtain the wavefront, and S is the sample surface.
###### Abstract
Besides finding solutions in hardware to reduce motion artifacts, a number of methods were proposed, such as using artificial intelligence [27], Fourier spatial frequency information [28], a regression-based algorithm [29], filters and morphological operations [30], an adaptive classifier [31], tensor voting [32], and automatic registration [33; 34; 35]. However, these are post-processing methods. In fundamentally reducing the appearance of motion artifacts, angiography reconstruction algorithms play a vital role. As a reconstruction algorithm with theoretical maximum clutter suppression [4], ED can utilize the statistical analysis to achieve an adaptive high-pass filter to reduce bulk motions, which were developed into A-scan ED (aED) [11] and B-scan ED (bED) [12]. However, one default requirement of the ED algorithm is that the input data are normally distributed [11; 12; 36], which is often not met during in vivo scenarios. The scanned tissue is often heterogenous on the axial axis, which can cause the acquired data not to be entirely random and, hence, not normally distributed. For example, when OCTA is used for dermatology applications, the epidermis, papillary dermis, and reticular dermis layers are all within the penetration range of OCT systems (1-3 um) [2; 37; 38]. As a result of this commonly layered structure, the scales of motion effects can differ from layer to layer [39], which can also cause non-Gaussian distributions of OCTA data. To reduce this limitation of the ED algorithm, this study proposes a novel, easy-to-deploy approach to the ED algorithm, windowed-ED (wED). The wED algorithm utilizes each A-scan with a moving window to apply ED statistical analysis. In addition, the size of the moving window can be optimized. As a result, the A-scan data within a selected depth range would be the most correlated to the normal distribution, generating OCTA results with fewer motion artifacts. Lastly, wED is compared with SV, aED, and bED to evaluate its performance. The proposed approach presents a novel perspective for reconstructing OCTA signals. It can reduce motion artifacts and background noise compared to other aforementioned algorithms. The proposed wED algorithm gives a robust and easy-to-deploy option for OCTA reconstruction.
## 4 Experimental Verification and Analysis
### Comparative Analysis
In test sets A and C, the diagnostic accuracy of the proposed method is 99.8%, and the misdiagnosis rate is 0.2%. In test set B, the diagnostic accuracy of the proposed method is 99.7%, and the misdiagnosis rate is 0.3%. Except for two samples of two teeth wear were misdiagnosed as normal, the classification accuracy of the other four states reached 100%. However, it is worth noting that the worst diagnosis results of the proposed method of multi-view data fusion were compared with the best diagnosis results of the single-channel CNN model of single-view data. In order to further verify whether the proposed model can learn discriminative features, t-SNE [25] was used to project the features learned by the fully connected layer of the CAM_MCFCNN model on the multi-channel dataset into the two-dimensional space for visualization and compared with the features learned by the single-channel CNN model on the ninth view dataset. The corresponding visualization results are shown in Figure 8. Each point in the figure represented a sample, and different colors represented different health states of the planet wheels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**View Data**} & **Diagnosis** & \multicolumn{3}{c}{**Average Testing Accuracy \(\pm\) Standard Deviation (\%)**} \\ \cline{3-5}  & **Strategies** & **A** & **B** & **C** \\ \hline View 1 & Separate & 96.11 \(\pm\) 2.78 & 96.02 \(\pm\) 1.26 & 99.57 \(\pm\) 0.16 \\ View 2 & Separate & 96.74 \(\pm\) 0  
Figure 8: T-SNE visualization of features learned in the fully connected layer: (**a1–a3**) indicate the features of single-channel CNN from testing datasets A, B, and C; (**b1–b3**) indicate the features of CMA_MCFCNN from testing datasets A, B, and C.  
Figure 7: Confusion matrices of five wear fault conditions. (**a1–a3**) are the confusion matrices of the single-channel CNN method in testing datasets A, B, and C, respectively. (**b1–b3**) are the confusion matrices of the CAM_MCFCNN method in testing datasets A, B, and C, respectively. As can be seen from the figure, in terms of the feature distribution of the fully connected layer, the features of the same health state learned by the CAM_MCFCNN model show better clustering performance within the class, while different health states show better separation performance. The single-channel CNN model shows good features separation ability on the test set C, but on the test dataset A, the features of two teeth wear and three teeth wear overlap. On the test dataset B, except that the features of all teeth wear were clustered together, there was overlap between three teeth wear and two teeth wear, normal state and single tooth wear. This shows that the CAM_MCFCNN model has better discriminative features than the features learned by the single-channel CNN model.
## 3 Numerical Simulations  
### Simulation Model  
#### 3.1.1 The Geometry of the Bridge in the Field  
The geometric dimensions and measurement point distribution of the constructed Chenxi Bridge are shown in Figure 2a. This is a large-span, heavy concrete box rigid bridge. The cross-sectional design of this bridge follows a symmetric distribution on both sides of the pier and is divided into six segments, each measuring 90 m. The control cross-section dimensions are illustrated in Figure 2b, with detailed specifications provided in Appendix A. It should be noted that the units in Figure 2a are meters and in Figure 2b are millimeters.  
A total of eight measurement points were strategically positioned along the bridge, specifically at the mid-span of each span and at the quarter-span locations of the main span. Taking the geometric center of the bridge as the origin, the coordinates of measurement points P1 through P8 were defined as \(-\)225 m, \(-\)135 m, \(-\)90 m, \(-\)45 m, 45 m, 90 m, 135 m, and 225 m, respectively. For the finite element modeling, BEAM188 elements with a trapezoidal cross-section were utilized. The material properties assigned to the model included an elastic modulus of 3.45 \(\times\) 10\({}^{10}\) Pa and a Poisson's ratio of 0.24. BEAM188 was chosen, and the static solution module was adopted. In addition, the damping effect and nonlinearity were not considered given the small deformation in the case. The finite element model was developed in ANSYS 21.0, as illustrated in Figure 2c. The boundary of the FEM model is that the middle three piers are fixed and the endpoints of the two ends are supported on vertical constraints (UY). The default settings were accepted.  
Figure 1: The flowchart of the proposed method.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 6 Detection and Mitigation
### Detection Mechanisms--Techniques
#### Domain Name System (DNS) Based Detection
Forensics are done through an analysis module that uses a k-cluster machine learning algorithm to decide whether or not a host might be compromised by a botnet. Other papers seek to develop methods for botnet detection based on botnet behaviour called C&C Tracer. The C&C Tracer [160] works by using C&C active behaviour feature extracting (CAFE), domain name status querying (DNSQ) and C&C status tracing analyser (CSTA) along with allow lists from multiple external sources such as the Honeypot project and Shadowserver Foundation. An analysis done by [PERSON] et al. [156] to test the feasibility of botnet detection through domain name system (DNS) records. The analysis shows that in the 5.5 million DNS TXT record queries obtained from their campus network, around 2293 queries where classified as \"unconfirmed\". In their further investigation, ~22% of these queries were targeting suspicious URLs identified by virustotal [169]. A similar approach is used by [PERSON] et al. in [157], which proposes a novel DNS-based detection approach for detecting botnet activity. The paper focuses on direct outbound DNS queries on non-standard authoritative name servers to identify botnets, which use TXT records to send commands. The paper finds that a similar 19% of identified potentially malicious DNS queries have been flagged by online websites, such as [169], for being used for botnet activity. Reference [159] also proposes a similar idea, but with a focus on UDP network traffic, focusing on DNS MX queries, the DNS packet request and various behaviour that might be botnet attacks based on UDP traffic. Reference [170] talks about a profiling dataset. \"UMUDGA: a dataset for profiling DGA-based botnet\" aims to enable researchers to move the data collection, organisation and pre-processing phases forward. Ensuring the availability of good datasets also help the general research community in providing novel detection mechanisms.
# Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC
## 7 Discussion  
The extension of the proposed algorithm when the feedback link is blocked is indeed an intriguing topic for discussion. The concept of reconfigurable intelligent surfaces (RIS) presents a promising solution in such scenarios. As mentioned in the references [18; 19], RIS can be used to intelligently reflect and manipulate signals in a desired manner, even in the absence of a direct feedback link. By leveraging the phase-shifting capabilities of RIS elements, it is possible to adaptively adjust the propagation environment and optimize the system performance. This could potentially mitigate the effects of a blocked feedback link and maintain the efficiency and reliability of the communication system. Concurrently, further investigation into the design of filters or the selection of appropriate coding schemes would also be a valuable avenue of research.  
Conceptualization, K.Y. and H.Y.; methodology, K.Y.; software, K.Y.; validation, K.Y., K.Z. and Y.N.; formal analysis, Y.N.; investigation, K.Y.; resources, K.Y.; data curation, K.Z.; writing--original draft preparation, K.Y.; writing--review and editing, K.Y.; visualization, Y.N.; supervision, K.Y.; project administration, H.Y.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Foundation of China [grant numbers 62371461].  
Figure 9: Comparison of different horizon prediction algorithms under burst jamming.
## 5 Results
### Throughput Analysis without Nonlinear Effects  
After the coverage analysis, we conducted a system-level simulation of the UCCF M-MIMO network without taking into account the nonlinear effects at the PA. For this  
Figure 4: CDFs depicting the ratio between the RSS averaged over RBs achieved under a UCCF network architecture and that following a network-centric approach, with varying numbers of RUs.  
Figure 3: Comparison of RSS averaged over RBs between network-centric and UCCF approaches. The buildings are marked in gray, whereas RUs are marked with black dots (the larger dot corresponds to the macro-RU, whereas the smaller dots correspond to the micro-RUs).  
purpose, we implemented the simulation environment described in Section 4 in MATLAB. We considered 20 users randomly placed in the alley (see Figure 4), where the effects of the selected network architecture were most visible during the coverage analysis. The initial positions of UEs followed a uniform distribution over the alley area. The users moved in random directions at a speed of 1.5 m/s, i.e., the azimuth of each user was uniformly distributed within the range of 0-360 degrees. Individual simulation runs involved continuous service and movement of these 20 users for 500 time slots (250 ms). During each time slot, the main loop depicted in Figure 2 was executed, but the fifth step was omitted. To achieve smooth plots, we conducted 30 independent simulation runs with different random generator seeds. Due to the high computational complexity of radio channel generation in the Wireless InSite 3D ray-tracer software, the realization of users' movements and the related channel coefficients for all simulation runs were pre-generated. This ensured a fair comparison, i.e., UEs followed the same paths and had identical radio channel coefficients in each simulation run, independent of the number of serving clusters.
## 5 Optimization and Scheduling Model of the Complementary System
### Equation Constraints
The output power of the PV is inversely proportional to temperature and directly proportional to solar irradiance. The expression is given as follows: \[P_{s}(t)=\eta\text{SI}(1-0.005(T_{0}+25))\] (27) where \(P_{s}(t)\) is the output of the PV in the \(t\)-th period, kW; \(\eta\) is the solar energy conversion efficiency; S is the total surface area of the PV panels, m\({}^{2}\), \(I\) is the solar irradiance, kW/m\({}^{2}\); \(T_{0}\) is the ambient temperature, \({}^{\circ}\)C.
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 2 Materials and Methods
### Deep Neural Network Approaches  
In this section, we present the shallow ANN and deep ANN architectures used for forecasting of solar radiation amount as an output, including conventional multi-layer deep ANN, sequential model, recurrent neural network, and long-short-term memory.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **\textless{}300 Eosinophils** & **\textgreater{}300 Eosinophils** & **\textgreater{}** & **RR** & **OR** \\ \hline Simple size & 956 & 340 & - & - & - \\ \hline Age & 41.47 \(\pm\) 14.01 & 38.26 \(\pm\) 13.37 & 0.001 & - & - \\ \hline Men & 286 (30\%) & 130 (38\%) & 0.005 & 0.782 & 1.450 \\ \hline Women & 670 (70\%) & 210 (62\%) & 0.005 & 1.135 & 1.450 \\ \hline BMI & 26.85 \(\pm\) 5.32 & 27.00 \(\pm\) 5.52 & - & - & - \\ \hline FEV 1 Volume & 3.14 \(\pm\) 0.84 & 3.15 \(\pm\) 0.81 & - & - & - \\ \hline FEV 1\% & 102.49 \(\pm\) 14.65 & 98.09 \(\pm\) 14.90 & 0.001 & - & - \\ \hline FEV 1 Zscore & \(-\)0.61 \(\pm\) 1.45 & \(-\)0.71 \(\pm\) 1.48 & - & - & - \\ \hline FVC Volume & 3.90 \(\pm\) 1.02 & 4.01 \(\pm\) 0.99 & - & - & - \\ \hline FVC \% & 107.82 \(\pm\) 13.84 & 105.48 \(\pm\) 12.73 & 0.006 & - & - \\ \hline FVC Zscore & \(-\)0.52 \(\pm\) 1.47 & \(-\)0.42 \(\pm\) 1.45 & - & - & - \\ \hline FEV 1/FVC & 80.54 \(\pm\) 6.93 & 78.77 \(\pm\) 7.78 & 0.001 & - & - \\ \hline FEV 1/FVC Zscore & \(-\)0.20 \(\pm\) 0.99 & \(-\)0.55 \(\pm\) 1.11 & 0.001 & - & - \\ \hline FEF 25–75 Volume & 3.16 \(\pm\) 1.14 & 3.02 \(\pm\) 1.14 & - & - & - \\ \hline FEF 25–75\% & 83.66 \(\pm\) 24.86 & 76.47 \(\pm\) 25.11 & 0.001 & - & - \\ \hline + Bronchodilation & 44 (6\%) & 31 (12\%) & 0.019 & - & - \\ \hline * Bronchodilation & 61 (9\%) & 23 (9\%) & 0.019 & - & - \\ \hline – Bronchodilation & 582 (85\%) & 206 (79\%) & 0.019 & - & - \\ \hline Asthma diagnosis & 501 (52\%) & 232 (68\%) & 0.001 & 0.768 & 0.513 \\ \hline Asthma onset prior to 12 years old & - & - & - & - & - \\ \hline Asthma onset prior to 40 years old & - & - & - & - & - \\ \hline Allergic asthma & - & - & - & - & - \\ \hline Asthma in symptomatic period & - & - & - & - & - \\ \hline Persistent asthma & - & - & - & - & - \\ \hline Current asthma symptoms & - & - & - & - & - \\ \hline Rhinitis & 828 (87\%) & 315 (93\%) & 0.003 & 0.935 & 0.513 \\ \hline Atopy & 750 (79\%) & 301 (86\%) & 0.001 & 0.886 & 0.472 \\ \hline Food allergy & 122 (13\%) & 40 (12\%) & - & - & - \\ \hline Drug allergy & 59 (6\%) & 29 (9\%) & - & - & - \\ \hline Contact dermatitis & 27 (3\%) & 7 (2\%) & - & - & - \\ \hline Other allergies & 20 (2\%) & 4 (1\%) & - & - & - \\ \hline FeNO & & & & & \\ (ppb) & 24.70 (23.70) & 43.75 (59.45) & 0.001 & - & - \\ \hline Eosinophil counts & & & & & \\ (Eos/uL) & 200 (200) & 500 (300) & 0.001 & - & - \\ \hline Total IgE & & & & & \\ (kU/L) & 118.00 (229.8) & 161.00 (372.9) & 0.001 & - & - \\ \hline Comorbidities & 160 (17\%) & 61 (18\%) & - & - & - \\ \hline Atopic dermatitis & 31 (3\%) & 10 (3\%) & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 2: Clinical characteristics of asthmatics and non-asthmatic patients.