esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 2 Related Work
### Rendezvous Point  
In RV-based approaches, mobility is handled by a location resolution system (LRS) residing within a dedicated node, called a rendezvous point (RV), which binds information between the name prefix provided by a content producer and its current location. A mobile producer must send update notifications to the RV, notifying it of its mobility. The consumer must first contact the RV to learn about the location of the producer, and after that, the consumer may send interest packets directly to the producer. This approach encompasses a low signaling overhead, as only update notifications from the mobile producer to the RV are required. In addition, this approach is transparent to the consumer. However, the dedicated RV node is a single point of failure, and a great memory overhead is imposed on the RV because it should handle location-dependent names for each prefix offered by each mobile node. In addition, RV nodes should be strategically placed, because placement greatly affects the path stretch and hence the performance of the approach. Furthermore, in [15], the authors showed that the reactivity time of RV schemes is greater than the round-trip time (RTT), which is a serious matter and may lead to serving consumers with outdated data packets. Aside from violating core NDN principles by binding data to locations via RV nodes, RV approaches also encompass high latency due to the iterative lookup process and the need for RV synchronization. Proposals under this approach include that in [18], where each producer is associated with a designated RV. When a producer is about to change its location, it notifies the access router to which it is attached by sending a deregistration message. The access router informs the RV about this situation, and the RV holds the pending interests intended for this mobile producer. When the mobile producer attaches to the new access router, it informs the new access router about its assigned RV in order to retrieve the pending interests waiting to be fulfilled by the mobile producer. In [19], a similar solution was proposed with a focus on reducing unnecessary bandwidth consumption and the prevention of triangular data routing by proposing coordinator-assisted mobility support solutions. As an improvement to the RV model, in [20], the locator of the consumers is included in the interest packet for the producer to directly inform the consumer about any future
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 3 Solution Method
### [PERSON]'s Method  
[PERSON]'s method [43], which is also known as the Newton-Raphson method, is a popular numerical optimization technique used to find the roots of a function. The method utilizes an iterative approach to progressively refine the approximation of the root. Starting with an initial guess for the root, the method uses the first and second derivatives of the function to calculate a new approximation of the root. This process is repeated until the approximation is sufficiently close to the true root. The strength of [PERSON]'s method lies in its fast convergence rate, which can be quadratic under certain conditions. However, the method is sensitive to the initial guess and may converge to a local minimum instead of the global minimum. Additionally, the method requires the computation of derivatives, which can be computationally expensive for complex functions.  
The Gauss-Seidel method is used for iteration, and it has better convergence efficiency than that of the Jacobi method. The algorithm works as follows (Algorithm 1).  
Figure 1: Curves of the link performance function.
## 2 O-RAN Architecture and Development
### O-RAN Architecture
#### 2.2.7 Interface with 3 GPP  
3 GPP interfaces are also used in O-RAN to provide message exchanges between O-RAN components following 3 GPP signaling specifications. They are summarized as follows:  
* X2 interface: The X2 interface is used to exchange control information and user data between different eNodeBs (eNBs) in a 4G/LTE network. It is also used to facilitate inter-cell handovers and load balancing. O-RAN uses the same X2 interface for communication between O-RAN radio units and between the O-RAN radio units and 3 GPP core network;
* S1 interface: The S1 interface is used to exchange control and user plane information between the eNodeB and the 4G/LTE core network. This interface is responsible for mobility management, session management, and connection management. O-RAN uses the same S1 interface for communication between the O-RAN radio unit and the 3 GPP core network;
* F1 interface: The F1 interface is a new interface introduced by O-RAN that connects the O-RAN radio unit to the O-RAN distributed unit. This interface carries the radio frequency (RF) signals and also supports the exchange of control and management information. The F1 interface is similar to the W1 interface used in 3 GPP's split architecture;
* E2 interface: The E2 interface is used in the 5G RAN to exchange control and management information between different network functions, including the radio access network function (RANF), central unit (CU), and distributed unit (DU). The O-RAN Alliance has developed an E2 interface specification that is compatible with the 3 GPP E2 interface.
## 3 Results and Discussion
### Optimization of the SLM Processing Parameters
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Coefficient \(a\) & Results \\ \hline \(a_{0}\) & 60.1737 \\ \(a_{1}\) & 0.0518 \\ \(a_{2}\) & 0.0032 \\ Regression Constants & \(a_{3}\) & 123.5745 \\ \(a_{11}\) & \(-7.42\times 10^{-5}\) \\ \(a_{22}\) & \(-4.11\times 10^{-6}\) \\ \(a_{33}\) & \(-180.9538\) \\ \(a_{12}\) & \(3.47\times 10^{-5}\) \\ \(a_{13}\) & 0.0726 \\ \(a_{23}\) & \(-0.0688\) \\ \hline \(f\) test & \(F\) & 8.9160 \\ Correlation Coefficient & \(R^{2}\) & 0.95 \\ \hline \(P_{\rm o}\) (W) & 651 \\ Optimum Combination & \(v_{\rm o}\) (mm/s) & 500 \\ \(h_{\rm o}\) (mm) & 0.378 \\ \hline Calculated Results Using the Optimum Combination & \(\rho_{relative,max}\) (\%) & 100.01 \\  & Energy density (\(\rm{J/mm^{2}}\)) & 3.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The constants of the model, model testing, and the optimized SLM process parameters. was provided. The two combinations of parameters are both near to the energy density, which corresponds to the highest relative density in Figure 2. In Figure 3, the micro-pores can be observed in the samples built with large hatching distance and high scan speed--this phenomenon was also observed in other alloys [10; 22]. The difference in the density of SLMed samples between using the better parameter combinations and the best one could just be the mathematical results, which need to be confirmed by further experiments. However, when hatching distance is low, the optimal combinations of laser power and scan speed are in a bigger selectable range, in a wide operating window of suitable process parameters (scan speed and laser power).This is helpful to select the parameters according to the actual requirements. When hatching distance is low, the high relative density parts are easily to obtain. This can also be found in other studies [9; 19; 20; 22]. The SLMed AISi10 Mg alloy samples with the combination of higher laser power, higher scan speed, and lower hatching distance also have higher ultimate tensile strength (UTS) and yield strength [18]. Therefore, the hatching distance is a crucial parameter in the set of SLM parameters, as the range of a specific higher relative density reduces dramatically with the increase of hatching distance. Figure 5: The relationship of relative density with power and scan speed for the regression model using the suggested Archimedes method for measurement of relative density. (**a**) and (**b**) Response surfaces; (**c**) contour: \(h=0.275\) mm; (**d**) contour: \(h=0.29\) mm; (**e**) contour: \(h=0.3\) mm; (**f**) contour: \(h=0.35\) mm; and (**g**) contour: \(h=0.4\) mm.
# Onsite Testing for Nonlinear Analysis of an Earthquake Damaged Historical Church in Italy
## 5 Nonlinear Analysis
### Results and Discussion  
The displacement response obtained by the non-linear static analysis is in reasonable agreement with the response from the nonlinear dynamic analyses. The nonlinear dynamic analysis provides displacement capacity lower than the results from the pushover analysis considering the mass proportional load, while the modal pushover analysis returned the most conservative results out of all the performed analysis. In historical masonry buildings, the complex architectural forms and the existing damage play a relevant role in the seismic response of the structure. The nonlinear dynamic analysis accounts for the effects of the inertia and a wider number of modes, including possible higher local modes; however, the results are complex to interpret due to the random nature of the excitation.  
The nonlinear static analysis returns an overall simplified representation of the expected deformation under lateral loads, the mass proportional load uniformly encompassed the entire church while the modal proportional load focused on the top lantern highlighted the vulnerability of the base of this element. The choice of the control point is a critical issue since the resulting capacity significantly depends on the location of it. On the other hand, pushover analysis is more manageable and requires a lower computational burden. As a consequence, it is well codified in standards [15; 36; 37] and often applied in research and professional work.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 5 Android Vulnerability Detection
### Android Vulnerability Detection Methods Based on API Calls and Permissions  
The algorithm DroidSieve used extreme gradient boosting to perform various trial-and-error runs to trace and segregate Android malware pertaining to the fixed inflow of data. DroidSieve has evaluated over 1 million faulty apps, achieving a very high detection rate of over 99%. However, since it performs malware detection by checking for similar patterns in the app's code, it may not be robust against mimicry attacks and app cloning.  
Code conversions [25] from one form to the other enable an analysis of the performance of any application in terms of the time taken to carry out the conversion process. A sequence of API calls also can be associated with a set of permissions to keep the malicious access away from creating any havoc across the network. However, the use of various machine-learning algorithms to estimate API-based detection on any real-time dataset can achieve better accuracy rates, but, at the same time, can also be computationally expensive concerning retrieving the set of permissions associated with the specific call, DroidMat, another approach based on fixed information analysis, includes permissions, intents, which are the activating objects that contain information about other elements, and API calls, in addition to the type of components integrated with the service. The use of various machine-learning algorithms such as K-Means and k-nearest neighbors (KNN) to infer conclusions about the static analysis are not applicable in dynamic analysis. Hence, it should be implemented in a manner that supports both fixed and variable analyses.
# Image Interpolation Based on Spiking Neural Network Model
## 4 Proposed Method
### Proposed SNN Model for Edge Detection
 \tag{8}\]  
The four output neurons in the output layer have only excitatory synaptic connections with pairs of neurons in the intermediate layer. Calculations for output neurons are performed by Equations (9)-(11). \[\frac{dg_{out}(t)}{dt}=-\frac{1}{\tau_{out}}g_{out}(t)+\frac{S_{A1}(t)+S_{A2} (t)}{A_{ex}} \tag{9}\]  
\[I_{out}=-g_{out}E_{out} \tag{10}\]  
\[v(t)_{O90}=\bigg{(}\frac{1}{g_{I}}\bigg{)}\bigg{\{}\bigg{(}-\exp\bigg{(}\frac{ g_{I}t}{c_{m}}\bigg{)}\bigg{)}\big{(}I_{out}+70g_{I}+g_{I}E_{I}\big{)}+I_{ out}+g_{I}E_{I}\bigg{\}} \tag{11}\]  
where \(g_{out}\) is the conductivity value, \(\tau_{out}\) is the time constant, \(I_{out}\) is the total synaptic current, \(E_{out}\) is the reverse potential for synapses, and \(v(t)_{O90}\) is the membrane potential of the \(O_{90}\) neuron. \(S_{A1}\) and \(S_{A2}\) are the spike trains of the M\({}_{\text{A1}}\) and M\({}_{\text{A2}}\) neurons, respectively. The spike train \(O_{90}\) of the neuron \(S_{90}\) is also calculated by Equation (8). The number of spikes \(F_{90}\) generated by the output neuron \(O_{90}\) during time \(T\) can be calculated by Equation (12). The spike numbers of other output neurons are also calculated using the equations above. \[F_{90}=\sum_{t=0}^{T}S_{90}(t) \tag{12}\]Instead of edges obtained using gradient-based edge detection approaches, edge images consisting of \(F\) values, which include the number of spikes produced by each output neuron during time \(T\), are determined by HVS with the help of SNNs. The edge images of the proposed model are given in Figure 5.  
Whereas vertical lines are more prominent in Figure 5a, horizontal lines are seen uninterrupted and distinctly in Figure 5b. The edges at angles 45\({}^{\circ}\) and 135\({}^{\circ}\) appear uninterrupted and distinctly in Figure 5c,d. The edge information from the four angles obtained with the proposed SNN model is used when calculating the image interpolation.
# DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data
## 3 Results
### Ablation Study
#### 3.3.1 Binary Classification  
In this section, we discuss the performance of different modes of DifffRS-net on the binary classification of breast cancer subtypes. Table 5 shows the average value of Accuracy, AUC, and F1 score of classifying any two subtypes of breast cancer using different DSCCN modes. As depicted in Table 5, we can find that both DifffRS-net2 and DifffRS-net3 are superior to DifffRS-netI overall. This clearly indicates that the application of the differential analysis and the robustness-aware adaptive SMCCA algorithm can effectively enhance the model's accuracy and robustness. Additionally, as can be seen in Table 5, DifffRS-net shows a superior performance compared to DifffRS-net4 and DifffRS-net5. This result further validates that attention learning network models can achieve better outcomes in regard to the binary classification of breast cancer subtypes. Ultimately, the overall superiority of DifffRS-net over other models suggests that the combined use of differential analysis, Sparse Canonical Correlation Analysis, and attention learning networks is an effective approach for the binary classification of breast cancer subtypes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Optimization Techniques** & **Classifier** \\ \hline DifffRS-net1 & None & Attention learning model \\ DifffRS-net2 & Differential analysis & Attention learning model \\ DifffRS-net3 & rAdaSMCCA & Attention learning model \\ DifffRS-net4 & Differential analysis; rAdaSMCCA & XGBoost \\ DifffRS-net5 & Differential analysis; rAdaSMCCA & Attention-off learning model \\ DifffRS-net & Differential analysis; rAdaSMCCA & Attention learning model \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization techniques and classifier used in different DifffRS-net models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Accuracy**} \\ \hline
**Breast Cancer** & **DiffRS-Net1** & **DifffRS-Net2** & **DifffRS-Net3** & **DifffRS-Net4** & **DifffRS-Net5** & **DifffRS-Net** \\ \hline Basal vs. Her2 & 0.911 & 0.917 & 0.913 & 0.869 & 0.874 & **0.952** \\ Basal vs. LumA & 0.945 & 0.958 & 0.958 & 0.955 & 0.946 & **0.984** \\ Basal vs. LumB & 0.937 & 0.952 & 0.976 & 0.923 & 0.924 & **0.982** \\ Her2 vs. LumA & 0.936 & 0.952 & 0.952 & 0.931 & 0.916 & **0.955** \\ Her2 vs. LumB & 0.875 & 0.891 & 0.894 & 0.869 & 0.815 & **0.909** \\ LumA vs. LumB & 0.815 & 0.835 & 0.833 & 0.817 & 0.787 & **0.894** \\ \hline \multicolumn{5}{c}{AUC} \\ \hline Basal vs. Her2 & 0.967 & 0.980 & 0.964 & 0.947 & 0.934 & **0.983** \\ Basal vs. LumA & 0.971 & 0.969 & 0.969 & 0.986 & 0.957 & **0.996** \\ Basal vs. LumB & 0.983 & 0.988 & 0.981 & 0.973 & 0.977 & **0.997** \\ Her2 vs. LumA & 0.965 & **0.980** & 0.933 & 0.924 & 0.861 & 0.947 \\ Her2 vs. LumB & 0.971 & 0.967 & 0.959 & 0.864 & 0.887 & **0.962** \\ LumA vs. LumB & 0.870 & 0.889 & 0.898 & 0.878 & 0.836 & **0.939** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of different DifffRS-net models on each binary classification dataset.
# Smart Platform for Monitoring and Control of Discrete Event System in Industry 4.0 Concept
## 2 Preliminaries and Problem Formulation
### Development of a Mixed Reality Application
#### 3.4.2 MQTT Communication
During MQTT communication, all data were received in a string format and therefore it was necessary to first convert them to the JSON format, from which the name of the variable and its value were then obtained. The global data could not be updated from the Unity application; every data change was sent to the PLC device, and, based on the change in the given variable in the PLC device, its current value was accepted. In this way, we ensured the reliable display of the current values of all variables from the PLC device, and it was impossible for the application to display the wrong value. An example of communication can be seen in Figure 14. When the button is pressed, we send the value of the given variable using MQTT communication, which sets it in the PLC device. The local server records the change of the given variable and then sends it using MQTT communication to the Unity application, where the change in the value of the variable is stored in (from the point of view of the application) a global variable.
## 3 Results
### Simulation Results
This means the laser linewidth tolerance is low, such as the target BER is \(1\times 10^{-2}\), when Qphase of EKF is \(5\times 10^{-4}\), \(1\times 10^{-4}\), and \(1\times 10^{-5}\), the corresponding laser linewidth is about 3.5 MHz, 3 MHz, and 1.35 MHz, respectively. The AKF with pilot symbol scheme with a different Qphase value shows the same estimation accuracy and estimation ranges which is about 8.3 MHz. The proposed AKF with pilot symbol scheme is regardless of the initial Qphase value. This means that the proposed scheme can achieve optimal carrier recovery even with different laser linewidth within a wide allowable range of the initial Qphase. Figure 5: The convergence curves of AKF. Figure 6: Dynamic polarization rotation tolerance of AKF and EKF with pilot symbols. The joint performance of pilot AKF with dynamic polarization rotation tracking and laser linewidth estimation is carried out by comparison with EKF. The system parameters in the simulation are set as follows: FR is 1:7, symbol rate is 26.13 Gbaud, OSNR is 19.5 dB, and the optimal R for EKF and AKF are found to be 0.25 \(\mathbf{I}_{2\times 2}\) and 0.08 \(\mathbf{I}_{2\times 2}\), respectively. In practical applications, carrier phase noise and dynamic polarization rotation exists at the same time.Qpol and Qphase need to be set simultaneously. The BER performance of AKF and EKF with pilot symbols is plotted as a function of Qpol and Qphase in Figure 8. The laser linewidth is set to be 2 MHz, and the polarization rotation rate is set to be 10 M rad/s. Qpol and Qphase is scanned from \(10^{-5}\) to \(5\times 10^{-4}\). As shown in Figure 8a, the BER of EKF is changed from 2.1 \(\times\)\(10^{-3}\) to 0.25. When Qpol = \(10^{-5}\) and Qphase = \(3\times 10^{-5}\), it achieves the best performance. Figure 8b shows the BER performance of the AKF with pilot symbols scheme and the BER fluctuation can be negligible and has the range from 2.2 \(\times\)\(10^{-3}\) to 3.2 \(\times\)\(10^{-3}\). Therefore, the advantage of the AKF with the pilot symbol scheme and the conventional EKF is obvious. The former can achieve almost the same performance regardless of the initial value of Qpol and Qphase, which means the performances do not rely on the initial value and the update of these two parameters are adjusted adaptively and simultaneously. Figure 8: **(a)** The influence of Qpol and Qphase value of EKF with pilot symbols and **(b)** AKF with pilot symbols. Figure 7: Laser linewidth tolerance of AKF and EKF with pilot symbols.
## 3 Circuit Design  
### Power Amplifier Structure  
The goals for this design include a 7-13 GHz bandwidth, an output power gain of 14 dB, an average \(P_{out}\) of 40 dBm, and a PAE of better than 30%. The main specifications for the amplifier are summarized in Table 1.  
According to Table 1, a minimum power gain of 14 dB is among the design goals of the MMIC PA. Since the maximum available gain of the chosen transistors is about 10 dB at 13 GHz, and assuming a total loss of 6 dB for the matching networks, at least two amplification stages are required.  
The primary task of the power-stage transistors is to deliver the required \(P_{out}\). According to load-pull simulations biased at \(V_{gs}\) = \(-\)2 V and \(V_{ds}\)= 28 V, the power density of the used transistors is about 5.6 W/mm gate width. To achieve a 40 dBm \(P_{out}\), a total transistor width of 2 mm is required. Trading off the number of fingers and the finger width, a total amount of 16 fingers is chosen and arranged as four 4 \(\times\) 125 um transistors. Figure 1a,b show the I-V curve and transfer curve (at \(V_{gs}\) = \(-\)2 V and \(V_{ds}\) = 28 V) of the 4 \(\times\) 125 um device, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & _Specification_ & **Unit** \\ \hline Frequency range & 7–13 & GHz \\ Average \(P_{out}\) & 40 (10) & dBm (W) \\ PAE & 30 & \% \\ Power gain & 14 & dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Requirements for 7-13 GHz MMIC PA.  
Moreover, the driver stage needs to provide enough linear \(P_{out}\) so that the power stage can reach saturation. Simultaneously, it is also necessary to minimize the power consumption on the HEMT of the driver stage in the design to improve the overall output efficiency of the developed PA. Taking the above into consideration, we adopt two HEMT cells with a total gate width of 1 mm (2 \(\times\) 4 \(\times\) 125 \(\upmu\) GaN HEMT) for the driver stage. Moreover, the driving ratio of the multistage wideband amplifier is about 1:2, which can ensure good inter-stage matching (IMN) and broadband performance and avoid the complexity or the asymmetry in the ratios of 1:4 or 1:8 [13]. Figure 2 shows the block diagram of the proposed MMIC PA.
# Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
## 3 Results
### Pohang 2017 Earthquake: Analysis of in Situ Observations
#### 3.2.2 Underwater Position of Underwater Glider  
The glider surfaced every 5 h and sent the GPS location information during the survey. The underwater trajectory of the glider was calculated by a linear interpolation between every two surfaced GPS points. When the earthquake occurred at 05:29:32 (UTC), the surfaced GPS positions were 37.3482\({}^{\circ}\)N, 129.6948\({}^{\circ}\)E at 00:55:19 (UTC) and 37.3806\({}^{\circ}\)N, 129.6994\({}^{\circ}\)E at 05:52:46 (UTC), respectively. The calculated GPS position of the glider is at 37.378\({}^{\circ}\)N, 129.699\({}^{\circ}\)E, approximately 140 km from the Pohang epicenter when the glider recorded acoustic sounds from the earthquake at 05:29:51 (UTC). The depth of the seafloor bottom and the glider at the GPS position was at about 430 m and 200 m, respectively.