esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussion
### Analyzing Downlink Throughput Distribution  
The simulation results in Figure 3 show that the downlink throughput distribution of macro users, which is also called non-CSG users in this case. The downlink throughput of macro user drops severely when there is no power control technique activated in the femtocell. However, the proposed active power control technique outperforms its counterparts. It can be seen from the Figure 3, the macro user achieve value of CDF of throughput 2.90 Mbps at 0.5 or 50% under the proposed APC technique, at the same point the FPCT, FUEAPCT, MUEAPCT and RBPCT achieve 2.60 Mbps, 2.73 Mbps, 2.77 Mbps and 2.71 Mbps receptively.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Assumptions** \\ \hline Carrier frequency \((f)\) & 2 GHz \\ Transmit power of macrocell \((P_{M})\) & 43 dBm \\ HeNB Noise Figure & 8 dB \\ Femtocell’s Transmit Power \((P_{F})\) & \(P_{max}=21\) Bm and \(P_{min}=0\) dBm \\ Lognormal shadowing standard deviation for Femtocell & 4 dB \\ Macrocell coverage Area \((R_{1})\) & 500 m \\ Shadowing standard deviation for Macrocell & 8 dB \\ Femtocell Coverage Area \((R_{2})\) & 25 m \\ Exterior wall penetration loss & 5 dB \\ Interior penetration loss \((L_{p})\) & 15 dB \\ Thermal Noise Density \((\eta)\) & \(-174\) dBm/Hz \\ Bandwidth \((B_{w})\) & 10 MHz \\ Macrocell antenna Gain & 14 dBi \\ Access Mode & CSG \\ Interference threshold, \(I_{Threshold}\) & \(-72\) dBm \\ Minimum separation UE to HeNB & 0.2 m \\ Minimum separation UE to eNB & 35 m \\ \(TL_{1}\) and \(TL_{2}\) & 200 ms \\ \(\Delta_{up}\) and \(\Delta_{down}\) & 2 dB \\ Traffic Model & Full Buffer \\ Minimum required SINR for FUE \((\Gamma_{FUE})\) & 10 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 4 shows the downlink CDF of the femto users. From the results, it can be seen that the proposed APC technique offers better performance except for \"No Power control approach\". With no power control, the HeNB transmits maximal power. Therefore, the femto users enjoy higher throughput at the cost of MUE performance. It can be seen from Figure 4 that the femto user achieves value of CDF of throughput 16.17 Mbps at 0.5 or 50% under the proposed APC technique, at the same point the FPCT, FUEAPCT, MUEAPCT and RBPCT achieve 16.98 Mbps, 15.23 Mbps, 14.36 Mbps and 14.01 Mbps receptively.  
From the Figures 3 and 4, we can easily see that the APC technique provide a balanced trade-off between the downlink throughput of macro users and femtocell users. In other words, this power control technique reduces the interference level to macro users while meeting the required QoS standard of the femto users.
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 4 Image Stitching
### Template Matching  
There are many objects where registration by the detection of feature points is unsuitable. Figure 3 shows an example of such objects. If an interest point algorithm was used to find a match for the areas marked by the yellow rectangle it would not be robust due to the lack of sufficient features in these areas.  
Although interest points may not be suitable for these objects, a method called template matching can be used. The most basic form of template matching for translation is sliding the smaller template image over the larger image, and subtracting the pixel intensity of the template image from the larger image at each pixel position and obtaining the sum of the absolute or squared error of the pixel intensity differences. Where the difference is lowest, the best match is found. If multiple matches are required, then a threshold can be set and any match below this threshold is deemed a match.  
A printed circuit assembly (PCA) is typically rich in colours with the printed circuit board (PCB) background, switches, connectors, resistors, capacitors and integrated circuits. Using a hue, saturation and value exploits the variety of colour.  
An experiment was carried out to determine the difference between template matching using the components of the HSV colour space. A template was selected, slid over the master image and the loss function plotted as colour maps. The loss functions were the absolute difference in hues, saturations, values and a combination of all three. As per Figure 4, the hue matching results in a wider funnel at the best match position (global minimum) but have more local minimums. The saturation and value (Lightness or grayscale) result in a much narrower global minimum funnel but less local minimums. A combination of all three HSV components results in a narrow global minimum and the least local minimums.  
Figure 3: Example object not suitable for interest point matching.  
Figure 2: Example for the image stitching problem, where the two yellow areas represent the overlap to be found.  
Given a master image size of width 640 and height of 360 and a template of width 105 pixels and height 180 pixels, the number of absolute difference operations is 96,300 and takes 3.54 s on an Intel i5 quad core processor running at 1.6 GHz with 8 GByte RAM. This could be sped up by using different scales e.g., scaling the template and master image down and finding the global minimum, repeating this process at different scales up to 1:1 but only searching in the area of the previous global minimum.
# Transmission Condition Monitoring of 3D Printers Based on the Echo State Network
## 4 Results and Discussion  
### Experimental Results Using the Proposed Method  
The accuracy of the ESN model using different channel data and all nine channels under different proportion training samples is presented in Figure 5. The precision shown in the diagram is the mean of five tests. As can be seen in Figure 5a, the accuracy of the ESN will increase as the proportion of training samples increases. The accuracy of the ESN was 69.82% when only using 7-9 channels of the attitude sensor to generate data and the proportion of training samples was 50%. The monitoring accuracy of the ESN model was almost the same when the data from channels 1-3 and channels 4-6 were employed to train and test the ESN model using the same training sample proportion. The highest accuracy was 87.01% in these three methods to monitor the delta 3D printer. Consequently, the experimental results demonstrate that the choice of data will affect the accuracy of transmission condition monitoring of 3D printers.  
As depicted in Figure 5b, compared with the method in Figure 5a, a higher monitoring accuracy was obtained when all the channel data of the attitude sensor were used for sample generation. With the enlarged proportion of training samples, the mean increased and the variance decreased. This experimental result indicates that the increase in the training samples' proportion will improve and stabilize the accuracy of monitoring. The highest accuracy was 97.17%, which was obtained using the proposed method to monitor the delta 3D printer with all nine channels' data from the attitude sensor. It should be noted that when the proportion of training samples was only 20%, that is only 30 training samples, the accuracy of the ESN model for 3D printer transmission state monitoring was 82.17%. The results identified that the ESN can still guarantee high precision in small samples with low computational resources because the ESN is a shallow learning model.
## 2 EarM_EPnP
### Model of Aerial and Field of View Angle under Fixed Central Angle  
In Section 2.3, using EPnP, we solved the threshold value of the central angle, thereby calculating the theoretical model for the height of aerial photography and field angle. It is shown in the following picture.  
It can be seen from Figure 3 that O\({}_{1}\) and O\({}_{2}\) are the camera and the Earth's center, respectively. AB represents the arc of the Earth's surface. C is the center of the plane, and D is the center of the arc of the Earth's surface. These two points are located at the intersection of the O\({}_{1}\) and O\({}_{2}\) lines. The central angle is denoted as \"\(\partial\)\" and the camera field of view angle is denoted as \"\(\delta\)\".  
\[W=2R\sin(\partial/2) \tag{17}\]  
\(W\) represents the length of the line segment \(|AB|\), R represents the radius of the Earth, and \(\partial\) is the central angle of the Earth. Additionally, the flying height of the aircraft is \(H\).  
\[H=H_{1}-\Delta H \tag{18}\]  
\(H\) is the flying height and \(H_{1}\) is the length of \(|CO_{1}|\), in other words, the height errors plus flying height. \(\Delta H\) is the length of the segment \(|CD|\), so the height error is:  
\[\Delta H=R-R\cos\left(\frac{\partial}{2}\right) \tag{19}\]  
And:  
\[H_{1}=\frac{W}{2\tan(\delta/2)} \tag{20}\]  
\(H_{1}\) is the length of the segment \(|CO_{1}|\) in other words, the flying height plus the height errors. So, the function for flying height H and field angle \(\delta\) is as follows. Equation (21) describes the relationship between \(\delta\) and \(H\). Equation (22) is used to represent the relationship between \(H\) and \(\delta\).  
\[H=R\cdot\sin(\partial/2)\div\tan(\delta/2)-R+R\cos(\partial/2) \tag{21}\]  
\[\delta=2\arctan\left[R\cdot\sin\left(\frac{\partial}{2}\right)\cdot\left(H+R -R\cdot\cos\left(\frac{\partial}{2}\right)\right)^{-1}\right] \tag{22}\]  
Figure 3: The relationship between flying height and field angle.
## 1 Introduction
The objective is to scrutinize the earthquake's impact on their accuracy, considering their positions serve as references for various geophysical studies, rendering their accuracy crucial to a diverse scientific and civilian audience. Given the inadequacy of relying on a singular method for identifying surface crustal deformation, we employed two distinct remote sensing techniques: Satellite Aperture Radar (SAR) interferometric and GNSS positioning procedures. By using these concurrent yet independent methods, we aimed to provide a comprehensive and reliable assessment of surface crustal deformation. This approach aligns with recent studies emphasizing the value of multi-sensor analyses in comprehensively addressing co-seismic deformations [24]. Since the occurrence of the Mw = 6.2 Petrinja earthquake at the end of 2020, numerous studies have been conducted and published on the subject. Geomatic and geological investigations primarily focused on displacement in the narrower area surrounding the epicenter [25]. Some studies exclusively adopted an SAR analysis approach [26], while others concentrated solely on GNSS analysis, albeit with only a few GNSS stations being considered [27]. However, until now, there has been no published research specifically addressing the multi-sensor analysis of co-seismic displacement within the CROPOS network. Our study utilized interferometric SAR processing to examine SAR phase data over the observed area, enabling the detection of centimeter-scale surface movement through a multi-temporal series of SAR data. By creating interferograms based on the time series of SAR images, we identified and tracked surface deformation events over time spans preceding and following the earthquake. This SAR analysis complements GNSS sensors, which continuously measure positioning deformations on GNSS CORS stations. The adoption of a multi-method approach allowed for an independent assessment of the obtained results and the surface deformation. The outcomes of this multi-method analysis, presented in this paper, align with and confirm the hypotheses stated in [25], are consistent with the partial analysis in [27], and independently corroborate the analyses in [26; 28]. The primary novelty of our research lies in introducing a pioneering approach by combining CROPOS GNSS stations with SAR data for the first time in a geodynamic study across Croatia. This innovative methodology provides a comprehensive analysis of the impact of the Mw = 6.2 Petrinja earthquake, offering unprecedented insights into the spatial heterogeneity of displacements and revealing potential geological structures and fault zones. Additionally, the study establishes a correlation between geodetic and geological findings, enhancing our understanding of the earthquake's effects on both horizontal and vertical station displacements.
# The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review
## 3 Pipeline Residual Strength Assessment Methods
### Finite Element Methods  
[PERSON] described the elastic-plastic buckling of pipes under external pressure [47]. In recent years, non-linear finite element analysis has been used as an accurate tool to predict the buckling collapse capacity of pipes under external pressure, bending, and axial force [48].  
[PERSON] and [PERSON] [10] found that finite element methods (FEMs) such as ABAQUS and ANSYS gave reliable results compared to experimental results, especially in terms of predicting buckling modes and the bending-moment vs. curvature relations. This is possible with the proper application of FEM settings, such as ensuring large deformation effects, material non-linearities, strain anisotropy, and kinematic hardening [49].  
The application of finite element techniques also improves current industrial corrosion assessment guidelines by reducing excessive conservatism [31]. However, it has been noted that in cases with axial compressive stress, FEM predictions only improve with decreasing axial compressive stress. This is attributed to the coarse meshing used to model the length of the pipe (excluding the corrosion defect), which may lead to wrinkles that adversely affect the accuracy of FEM predictions. The difference in the failure pressure prediction is often due to various limit loads set to determine the point of rupture. For wrinkling, failure is predicted when the resisting moment decreases with increasing deformation [35].
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 4 SPN Models
### Extended Model B: Warm-Standby  
Figure 7 depicts the SPN Base model with some additional modifications. This paradigm utilizes warm-standby redundancy, in which the primary computer is continuously operational and a secondary machine remains idle in the background (OC). If the main machine fails, the secondary machine is activated and returns to an operational state (OP).  
The presence of the token Front_ON indicates that the machine is active. In the event of a system failure, the token is transferred to Front_OFF. The token for the secondary machine, which was previously in Front_ON_OC in its idle state, is transferred to Front_ON_OP, indicating that the secondary machine has transitioned from standby to active mode. When the primary machine is restored and operational, it returns to its idle state (secondary token  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Transition** & **Index** & **Expression** & **Time** \\ \hline \multirow{2}{*}{Front-End} & SWITCH\_TIME1 & [C-01] & (\#Front\_ON = 0) & 0.8 \\  & T0 & [C-02] & (\#Front\_ON = 1) & - \\ \hline \multirow{2}{*}{Back-End} & SWITCH\_TIME2 & [C-03] & (\#Back\_ON = 0) & 0.8 \\  & T1 & [C-04] & (\#Back\_ON = 1) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Guard conditions and delay used in Cold-Standby model transitions.  
Figure 7: Extended model using warm-standby redundancy.  
in Front_ON_OC). This technique also applies to the Back-End, with only the component terminology and values being modified.  
Unlike Cold-Standby, the construction of the secondary machine in Warm-Standby involves both machines being constantly turned on. However, because the secondary machine is not fully functioning, it consumes fewer resources. The values for MTTR and MTTF insertion for the secondary machine in Warm-Standby are shown in Table 4, which are based on the values for the main machine in the base model of the project, with some modifications. Due to the secondary machine being in an idle state (OC), it has a longer time to failure, approximately 50% longer compared to other machines according to [23].  
Table 5 shows the guard conditions for the model with warm standby. Similar to cold standby, when the ON component of the main machine is equal to zero, and the SWITCH_TIME function is activated, allowing the token to be transferred from OC mode to OP mode. When the ON component of the main machine returns to its initial value, the immediate transition T is activated. Warm standby is a more structured paradigm, and the time between transitions is typically eight times shorter than in cold standby.
# Contrast Sensitivity Based Multiscale Base-Detail Separation for Enhanced HDR Imaging
###### Abstract  
High dynamic range (HDR) imaging is used to represent scenes with a greater dynamic range of luminance on a standard dynamic range display. Usually, HDR images are synthesized through base-detail separations. The base layer is used for tone compression and the detail layer is used for detail preservation. The representative detail-preserved algorithm iCAM06 has a tendency to reduce the sharpness of dim surround images, because of the fixed edge-stopping function of the fast-bilateral filter (FBF). This paper proposes a novel base-detail separation and detail compensation technique using the contrast sensitivity function (CSF) in the segmented frequency domain. Experimental results show that the proposed rendering method has better sharpness features and image quality than previous methods correlated by the human visual system.  
HDR image; iCAM06; bilateral filter; base-detail separation Article
## 3 Results
### Case Study  
A case study aims to capture the unique aspects of a single case, providing insight into its behavior in significant situations. The case studies presented here offer detailed information on each method's performance in specific contexts. To illustrate the competitive performance of GBNSS, we show two case studies with concrete metric values EC, LCCS, S3, and GOSim.  
The first case study was conducted using the Saccharomyces cerevisiae dataset, which includes 81 biological networks. The pathway sec04130 (SNARE interactions in vesicular transport) was randomly picked up from the biological query networks and used to search against the Saccharomyces cerevisiae dataset. Another pathway, sce00520 (Amino sugar and nucleotide sugar metabolism), was randomly chosen from the dataset and ranked within the top 10% of pathways similar to sce04130. The EC, LCCS, S3, and GOSim scores between sce04130 and sce00520 are 0.78, 10, 0.4, and 28.05, respectively. When GBNSS was applied to search the Saccharomyces cerevisiae dataset, it successfully identified sce00520 within its top 10% of similar pathways. In contrast, NSSRF and PathEmb could only detect sce00520 within their top 50% of similar pathways. The visual comparison of pathways sec04130 and sce00520 is shown in Figure 8.  
The second case study was performed using the Homo Sapiens dataset, which includes 314 biological networks. The pathway hsa05410 (Hypertrophic cardiomyopathy) was randomly picked up from the biological query networks to search against the Homo Sapiens dataset. Another pathway, hsa04976 (Bile secretion), was randomly chosen from the dataset and ranked within the top 10% of pathways similar to hsa05410. The EC, LCCS, S3, and GOSim scores between hsa05410 and hsa04976 are 0.6, 10, 0.3, and 63.46, respectively. When GBNSS was applied to search the Homo Sapiens dataset, it successfully identified hsa04976 within its top 10% of similar pathways, while PathEmb and NSSRF identified hsa04976 within their top 30% and top 90% of similar pathways, respectively. The visual comparison of pathways hsa05410 and hsa04976 is shown in Figure 9.  
Figure 8: Visualization of (**a**) sce04130 and (**b**) sce00520.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Features** & **Attention Mechanism** & **Result** \\ \hline \(GBNSS_{\_}1\) & None & NO & 0.43 \\ \(GBNSS_{\_}2\) & None & Yes & 0.56 \\ \(GBNSS_{\_}3\) & BOF & NO & 0.51 \\ \(GBNSS\) & BOF & YES & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Attention mechanism and features used in different GBNSS models about metric GOSim about topological similarity searches in Bos Taurus species.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 4 Secure and Efficient Routing Protocol for Jellyfish Attack Prevention in V2X (SecE-V2X)
### Honesty Characteristics  
In designing our SecE-V2X protocol, we consider the following characteristics:  
* Only honest nodes can participate in the routing path.
* Source and destination nodes are considered honest nodes.
* Lack of symmetry: This can be defined as the lack of Honesty equivalence between nodes. If node U is honest to node V, it does not necessarily mean that node V is honest to node U.
* Transitive relation: The honest relationship between two nodes can be provided to other nodes as recommended confidence.
* Composite: The honest values collected from many possible paths can be combined to produce an integrated honest value.
## 4 Scaling Gene and Cell Therapy ATMPs from Research to the Clinic with AI
### Automation  
The manufacturing of ATMPs involves numerous supplementary operations beside the bioprocess itself, including supply chain management, sample tracking, the changeover between batches, contamination control, equipment maintenance, regulatory compliance adherence, quality control, batch record reviewing, and data management. AI enables automation of many tasks, to enhance precision and consistency, increase efficiency, allow scalability according to production demands, improve cost-effectiveness, and reduce biotechnology personnel workloads. Robotic process automation can replicate and mimic administrative and repetitive activities like data entry, basic data manipulation, and report generation. ML algorithms can schedule planned equipment calibration, preventive maintenance, and cleaning procedures based on the runtime. Cleanroom environmental monitoring data analyzed by AI models can provide alerts on contamination risks and suggest corrective actions, to avoid batch losses or disruptions to production. Automated microscopy employing computer vision techniques permits precise characterization of cells. The searching of batch records by an AI makes use of natural language processing and provides appropriate results related to valuable data. Augmented reality helps operators carry out critical manual operations, in order to minimize mistakes. Multi-faceted AI-driven automation can be used to address the spatial and staffing limitations \begin{table}
\begin{tabular}{l l} \hline
**Advantages of AI in AIMP Production** & **Disadvantages of AI in ATMP Production** \\ \hline
**Enhanced Process Monitoring** & **Data Security Concerns** \\ \hline Real-time oversight and data analysis for maintaining consistency and quality. & Risks associated with patient data privacy and data integrity in AI systems. \\ \hline
**Automation of Manufacturing Tasks** & **High Initial Investment** \\ \hline Increases efficiency, precision, and scalability, reducing the workload of biotechnologists. & Significant upfront costs for AI integration and infrastructure development. \\ \hline
**Dynamic Control and Adaptability** & **Limited Data in Early Development** \\ \hline AI systems can adjust to the variability in patient cell samples, optimizing batch consistency. & AI models may lack accuracy in early stages due to insufficient data for machine learning. \\ \hline
**Improved Data Management** & **Requirement for Specialized Personnel** \\ \hline Effective handling of large datasets, enhancing process comprehension and decision-making. & Need for staff with expertise in AI and bioprocessing, who can be scarce. \\ \hline
**Systems Biology Integration** & **Regulatory Challenges** \\ \hline AI aids in understanding complex biological relationships, enhancing predictive medicine. & Ambiguity in regulatory compliance for emerging AI applications in healthcare. \\ \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of AI in ATMP production.