esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Hybrid SDN is an inevitable phase of the SDN transition, which consists of both SDN devices and legacy devices. A hybrid SDN WAN is usually partitioned into several domains by multiple in-band controllers, which may be located at different places. The dynamic SDN controller assignment in hybrid SDN WANs is an important problem, in which this influence of legacy devices has been ignored by previous studies. In hybrid SDNs, control messages may pass through LFDs, which would increase the SFD-controller latency because of queuing in LFDs.  
In this paper, we propose a dynamic controller assignment scheme in hybrid SDNs, which is called the Legacy Based Assignment (LBA). This scheme can dynamically delegate each controller with a subset of SFDs in the hybrid SDNs, whose objective is to minimize average SFD-to-control latency. Firstly, we analyze the influence of LFDs of hybrid SDNs in SFD-to-control latency, which is caused by routing queuing in LFDs. Secondly, we formulate the dynamic controller assignment problem to be a 0-1 Integer Linear Programming. In the formulation, in addition to the impact of LFDs, the capacity limitation of the controllers and the available routing paths generated by the SDN partially deployment are also considered. Simulation results show that our solution has a better performance in decreasing the latency and packet loss rate, and we validate that the influence of LFDs cannot be ignored in solving this problem.  
Conceptualization, J.Y., X.L., T.Y., and E.L.; methodology, J.Y., X.L., T.Y., and E.L.; software, J.Y.; validation, J.Y., X.L., T.Y., E.L., C.K., and F.C.; formal analysis, J.Y., X.L., and T.Y.; investigation, J.Y., X.L., T.Y., and E.L.; resources, J.Y., X.L., T.Y., and E.L.; data curation, J.Y., X.L., T.Y., and E.L.; writing-original draft preparation, J.Y., X.L., T.Y., and E.L.; writing-review and editing, J.Y., X.L., and T.Y.; visualization, J.Y.; supervision, X.L.; project administration, C.K., F.C.; funding acquisition, X.L., E.L.  
This work was supported in part by the project \"Research on Security Architecture for Next Generation Distribution Automation System\" of State Grid Corporation of China, and by the NSFC-General Technology Fundamental Research Joint Fund U1836215.  
The authors would like to convey their heartfelt gratefulness to the reviewers and the editor for the valuable suggestions and important comments which greatly helped them to improve the presentation of this manuscript.  
The authors declare no conflict of interest.
# Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty
## 2 DRSMO Modeling
### DRSMO Inverse Optimization Framework  
As for the inverse optimization process in Figure 1, it is a continuous update source and mask parameter to meet the final target resist pattern and overcome the OPE. The inverse optimization process of DRSMO relies on the corresponding cost function establishment. In DRSMO, the multi-objective cost function is composed of the statistics expected in plentiful defocus disturbances, and the total cost function can be divided into two parts: the pattern fidelity part and defocus sensitivity part.  
The pattern fidelity part is in light of [PERSON]'s [19] approach, which is defined as the expectation of the Euclidean distance between the target resistance pattern \(\overline{Z}\) and the resistance pattern \(Z(\beta_{i})\) in plentiful defocusing disturbances. It can be formulated as  
\[F=\varepsilon_{\beta_{i}}\Big{\{}\|\overline{Z}-Z(\beta_{i})\|_{2}^{2}\Big{\}}, \tag{5}\]  
where \(\beta_{i}\) is a stochastic variable representing defocusing disturbances. It is subject to a certain range of evenly distributed disturbances, namely, \(\beta_{i}\in U(-\alpha,\alpha)\). \(\beta=\{\beta_{i}\}\) represents the whole training set. It should be noted that the sample range of \(\pm\alpha\) is selected according to the actual situation, larger \(\alpha\) can theoretically lead to wider DOFs, but too large a DOF will be beyond the potential of optimization and \(\overline{Z}\), \(Z(\beta_{i})\)\(\beta_{i}\), respectively. \(\varepsilon\{\}\) is the mathematical expectation.  
To the core ideal of DRSMO is introduced the defocus sensitivity penalty function, which aims at minimizing the expected quadratic change ratio of the aerial image to defocusing and can be formulated as  
\[Y=\varepsilon_{\beta_{i}}\Big{\{}\|\partial^{\mu t_{\text{feas}}}(\beta_{i})/ \partial\beta_{i}\|_{2}^{2}\Big{\}}, \tag{6}\]  
This penalty function directly controls the change rate of the aerial image to the defocus, which improves the consistency of the pattern and CD in different defocus disturbances. Therefore, it is beneficial to optimize a more robust source and mask with a lower defocus sensitivity. Besides, due to the improvement of process robustness for the optimized system, the PW will also enlarge. Appendix A and Equation (A5) define the details about the analytical sensitivity penalty \(Y_{i}\), so that the total cost function \(G\) consists of the weighted sum of \(F\) and \(Y\).  
\[\text{min}G=F+\omega Y. \tag{7}\]  
where \(\omega\) is the weighting factor of the sensitivity part. Typically, \(\omega=0\) means the optimization only operates at the fidelity part \(F\). In Section 3, we will discuss that the optimization results only operate at the fidelity part or simultaneously operates at the fidelity part and sensitivity part.
## 2 Concepts and Theory  
### Radar Range Equations  
Radar relies on backscattering to generate a detectable echo from a target. Weather radar emits radio energy from a transmitting antenna and propagates the radio wave outward in a narrow beam through the atmosphere. In the path of the beam, a small portion of the reflected and scattered energy travels back and is intercepted by the receiving antenna. The radar range equation is the foundation of radar remote sensing and describes the physical dependencies on the transmission power and how the wave propagates up to receiving the echo signals. To express the power of the echo signal as a function of the distance \(R\) to the target, and the target radar reflectivity \(\eta\), the equation can be written as follows:  
\[P_{r}=\frac{P_{\mathrm{r}}G^{2}\theta^{2}l\lambda^{2}}{1024 ln2\pi^{2}}\frac{ \eta}{R^{2}} \tag{1}\]  
where \(P_{\mathrm{r}}\) is the received power, \(P_{\mathrm{t}}\) is the peak power transmitted by the radar, \(G\) is the gain of the antenna, \(\theta\) denotes the antenna beamwidth, \(l\) is the pulse length of the transmitted signal, and \(\lambda\) is the wavelength. The reflectivity \(\eta\) is the sum of radar cross section per unit volume.  
In radar meteorology, the reflectivity factor (\(Z\) hereafter) is a common term for the received power because it equals the sum of the sixth-power of the diameters of the water drops in a unit volume of space, which means establishing the relationship between the precipitation intensity and the signal power. For Rayleigh scattering, the reflectivity is expressed as follows [2; 20]:  
\[Z=\frac{\eta\lambda^{4}}{\pi^{5}K^{2}} \tag{2}\]  
where \(\lambda\) is the wavelength; \(K^{2}\) is often taken to be 0.93 for water as a dielectric constant. Equation (2) is derived only for Rayleigh scattering, considering \(K^{2}\) for water. During actual atmospheric precipitation, there is not only Rayleigh scattering but also Mie scattering, and the precipitation phase is more complex.
# The Effects of Augmented Reality Interaction Techniques on Egocentric Distance Estimation Accuracy
## 2 Methods
### Experimental Design and Variables
#### 2.3.1 Independent Variables  
This experiment had three independent variables: two interaction techniques (direct pointing and indirect cursor technique), three egocentric distances (target displayed at 90 cm, 120 cm, and 150 cm from the participant), and six indices of task difficulty (2.7, 3.5, 4.1, 4.5, 6.05, and 6.15 bits). Therefore, the experiment was designed as a 2 (interaction technique) \(\times\) 3 (egocentric distance) \(\times\) 6 (index of difficulty) within-subject design, and repeated-measures ANOVA was used as the analysis method. The levels of task difficulty were further classified into low (easy task), medium (moderate task),  
Figure 3: Illustration of (**a**) the experimental setup and (**b**) a participant during the experiment in the direct interaction condition. A chinrest was used to keep the participant’s head fixed, and the 3D projector was placed under the right-hand table. The origin for reference of measurement was in line with the center of the projector and at table height (75 cm above the floor).  
Figure 2: Illustration of the experimental task. (**a**) Eight virtual spherical targets appeared one at a time in sequence, with target width (W) and amplitude of inter-target distances (A), and (**b**) the sequence of pointing trials for the first four targets.  
and high (difficult task) indices of difficulty. These difficulty levels respectively indicated an index of difficulty (ID) less than or equal to four, greater than four but less than or equal to six, and greater than six [58]. The ID was computed using Equation (1):  
\[ID=log_{2}\Big{(}\frac{A}{W}+1\Big{)} \tag{1}\]  
where \(A\) is the amplitude of inter-target distance and \(W\) is the target width, as shown in Figure 2. A farther inter-target distance or smaller target results in increased difficulty.
## 5 Conclusions  
In this paper, the FMFC model was established in order to predict the connector loads of multi-module VLFSs with hinged connectors. Compared with the traditional RMFC method, this method combines multi-module structures with the hydroelasticity theory, so the elastic deformation of each module and connector can be considered. Moreover, it is more convenient to obtain the relative displacement between modules with this method because the relative pitch mode can be identified through modal analysis. A model test was carried out and the results show that the established method is correct. We also found that the model mooring greatly influenced the horizontal loads of the connector when comparing the simulation results with the experimental results. This serves as a reminder that special attention must be paid to the selection of mooring springs before initiating the experiment and during data processing after experiment. A lot of calculation work has been carried out for connector loads. We conclude that the loads in all three directions reached their maximum values near 75 degrees, where the value of \(Fy\) was relatively smaller. In  
\begin{table}
\begin{tabular}{c c c c} \hline
**Wave DirectionType** & \(\mathbf{C_{1}\text{-}Fx}\) **(MN)** & \(\mathbf{C_{1}\text{-}Fy}\) **(MN)** & \(\mathbf{C_{1}\text{-}Fz}\) **(MN)** \\ \hline
0.0 & 1.18 & 0.02 & 1.83 \\
15.0 & 1.28 & 0.10 & 1.92 \\
30.0 & 2.88 & 0.21 & 2.12 \\
45.0 & 6.47 & 0.43 & 2.56 \\
60.0 & 8.26 & 0.69 & 6.62 \\
75.0 & 13.69 & 1.63 & 7.89 \\
90.0 & 2.14 & 0.11 & 0.04 \\ \hline \end{tabular}
\end{table}
Table 3: Significant values of connector loads.  
addition, the horizontal loads (along the \(x\) and \(y\) directions) were more sensitive to changes in the wave direction. Therefore, we should pay more attention to the \(Fx\) and \(Fz\) when designing connectors. Furthermore, an important conclusion for designers is that the wave direction at which the maximum short-term significant value appears is not consistent with the wave direction where the maximum transfer function is located. Therefore, all the wave directions should be searched in order to obtain an accurate maximum short-term significant value before conducting a structural performance analysis.  
In this paper, the influence of connector stiffness on connector loads was not referred to. However, parameter sensitivity analyses are planned for future work. All in all, the FMFC method established in this paper can be used to calculate the loads of VLFSs, and the laws summarized in this paper provide reference for future strength calculations and connector designs.  
Conceptualization, X.G.; Methodology, S.W.; Validation, Y.G.; Investigation, S.W.; Resources, Y.C., J.D. and L.X.; Data curation, Y.G. and S.Z.; Writing--original draft, S.W.; Writing--review & editing, S.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data that were used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 7 Analysis from Recorded IQ Signal and Discussion
### Orbital Calculation from TLE and Correction of Signal Strength  
The latest TLEs of the time are available from space-track [14]. The contents of this are shown in Table 6. Python and the ephem library [15] were used to calculate the change in position and sight distance of the satellite at each signal arrival time. Please refer to \"TLE2 POS.py\" in Supplementary Materials for detailed code of this part. The change in the sight distance over the period under analysis is shown in Figure 17. The distance varied from 425 to 658 km during reception. Since the free space loss is expressed by Equation (1), the respective Free Space Pass Loss (FSPL) varied from 137.9 to 141.6 dB, a change of 3.7 dB.  
\[FSPL_{dB}=20\log\left(\frac{4\pi r}{\lambda}\right) \tag{1}\]  
Figure 18 shows the normalization with respect to the most strongly received signal, considering the amount of change in free space loss. The highest signal of each beacon from Figure 18 is compared. A zoom view of the signal from Loop Hentenna \"KYUTECH\" is shown in Figure 19 and a zoomed-in view of the beacon \"BIRDS4\" from the bus system is shown in Figure 20. Focusing on the most strongly received letters, K and B, they have signal ratio strengths of \(-0.3\) dB and \(-15.9\) dB, respectively. The difference is 15.6 dB.  
Figure 16: Received signal with elapsed time 60.5 to 66.5 s.  
Figure 15: Received signal at elapsed time 26 to 30 s.  
Figure 17: Elapsed seconds and the sight distance from the ground station to Tsuru.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Name & TSURU \\ NORAD ID & 47,927 \\ Epoch (UTC) & 14 December 2021 14:20:47 \\ Inclination & 51.639 deg \\ RA of A. node & 162.871 \\ Eccentricity & 0.0000359 \\ Argument of perigee & 276.418 \\ Revs per day & 15.56956963 \\ Mean anomaly & 83.677 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Orbital element for calculation.  
Figure 18: Elapsed seconds and normalized peak intensity since 06:19:40 (UTC) on 15 December 2021.
## 3 Solution of the Nonlinear Problem  
### The Galerkin Method  
In order to derive the ordinary differential equation of motion from the partial differential Equation (35), the Galerkin method was here adopted, according to which the displacement function \(w(x,t)\) can be expressed as [38]  
\[w(x,t)=\psi(x)\cdot(t) \tag{36}\]  
where \(q(t)\) is the unknown time dependent function and \(\psi(x)\) is the mode shape (test) function which must satisfy the kinematic boundary conditions. Based on the selected boundary conditions (S-S, C-C and C-F), the following admissible functions are considered [39; 40]  
\[\psi(x)=sin\big{(}\frac{\pi x}{L}\big{)}\hskip 56.905512 pt\text{For S}-\text{S Beam} \tag{37}\]  
\[\psi(x)=\frac{1}{2}\big{[}1-cos\big{(}\frac{2\pi x}{L}\big{)}\big{]}\hskip 14.226378 pt \text{For C}-\text{C Beam} \tag{38}\]  
\[\psi(x)=cos\Big{(}\frac{\vartheta x}{L}\Big{)}-cosh\Big{(}\frac{\vartheta x}{ L}\Big{)}-\kappa\Big{[}sin\Big{(}\frac{\vartheta x}{L}\Big{)}-sinh\Big{(}\frac{ \vartheta x}{L}\Big{)}\Big{]}\hskip 56.905512 pt\text{For C}-\text{F Beam} \tag{39}\]  
where  
\[\kappa=\frac{cos\varphi+cosh\varphi}{sin\varphi+sinh\varphi},\varphi=1.87510407 \tag{40}\]  
Substituting Equation (36) into Equation (35) yields  
\[\bar{q}\psi+a_{1}q+a_{2}q^{2}+a_{3}q^{3}=0 \tag{41}\]  
where \(\tilde{q}(t)\) is the second derivative of \(q(t)\) with respect to time, respectively. Additionally, the coefficients \(a_{1}\), \(a_{2}\) and \(a_{3}\) are defined as  
\[a_{1}=\frac{1}{l_{0}}\Bigg{[}\Bigg{(}D_{11}-\frac{B_{11}{}^{2}}{A_{11}}\Bigg{)} \psi_{xxxx}-N_{T}\psi_{xx}(x)\Bigg{]} \tag{42}\]  
for S-S and C-C beam  
\[a_{2}=\frac{1}{l_{0}}\Bigg{[}\frac{B_{11}}{L}(\psi_{x}(L)-\psi_{x}(0))\psi_{ xx}(x)\Bigg{]} \tag{43}\]  
\[a_{3}=\frac{1}{l_{0}}\Bigg{[}-\frac{A_{11}}{L}\int_{0}^{L}\frac{1}{2}(\psi_{x })^{2}dx\Bigg{]}\psi_{xx}(x) \tag{44}\]  
and for C-F beam  
\[a_{2}=\frac{B_{11}}{l_{0}}\psi_{xx}(L)\psi_{xx}(x) \tag{45}\]  
\[a_{3}=-\frac{A_{11}}{2I_{0}}(\psi_{x}(L))^{2}\psi_{xx}(x) \tag{46}\]  
In which, \(\psi_{x}\), \(\psi_{xx}\), \(\psi_{xxx}\) and \(\psi_{xxxx}\), are the first, second, third and fourth derivatives of \(\psi(x)\) with respect to \(x\), respectively. Multiplying Equation (41) with \(\psi(x)\) and integrating over the domain (0,\(L\)) leads to the following nonlinear ordinary differential equation  
\[\ddot{q}+\omega_{0}^{2}q+\eta_{2}q^{2}+\eta_{3}q^{3}=0 \tag{47}\]  
where  
\[\omega_{0}^{2}=\frac{\int_{0}^{L}a_{1}\psi(x)dx}{\int_{0}^{L}\psi^{2}(x)dx} \tag{48}\]\[\eta_{2}=\frac{\int_{0}^{L}a_{2}\psi(x)dx}{\int_{0}^{L}\psi^{2}(x)dx} \tag{49}\]  
\[\eta_{3}=\frac{\int_{0}^{L}a_{3}\dot{\psi}(x)dx}{\int_{0}^{L}\psi^{2}(x)dx} \tag{50}\]
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 4 The Proposed FL-IDS Architecture  
The overall system architecture, as shown in Figure 1, comprises several smart buildings cooperating in a federated learning scenario. A server denoted as FL-Server coordinates the FL training of multiple clients, validates the derived global models using a validation dataset, and applies countermeasures to deal with malicious clients. The client, FL-Client, participates in FL training to build a global machine learning model without directly sharing its data, which are produced and processed locally.  
Considering a single smart building element of the architecture shown in Figure 1, the FL-Client is part of a system composed of four disjoint modules. The IoT space represents the cyber space of the smart building. It comprises all the IoT devices and the network modules needed to enable the smart building's capabilities. In particular, this component collects the raw data. The data pre-processing and engineering unit is tasked with the responsibility of converting raw data into a format that is suitable for machine learning. The objective is to clean and compress a substantial quantity of data without the need for human intervention. The zero-touch principle is employed to facilitate seamless integration into MLOps systems. The data storage unit stores both the converted data, which are ready for inference, and the labeled data generated by the inference. The labeled data are stored to enable the smart building to contribute to future federated training. The FL-Client represents the AI component of the system. The FL-Client is responsible for storing the model that is utilized for prediction, which is distributed by the FL-Server. This component makes predictions over the smart building data, generates alerts, and stores the labeled data. Additionally, the model's predictions can be analyzed alongside information generated by the data pre-processing and engineering unit to enable human verification of the AI's inference results.
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 2 Routing Decision in Double Q-Learning Environment
### Q-Value Update
\(\frac{T_{avg}}{T_{b}}\) represents the contribution of node \(a\)'s choice of node \(b\) as the next hop to reducing the average network delay. The smaller the value of \(t_{b}\), indicating a greater contribution to reducing the average network delay, the larger the reward value. \(Hops_{b}\) signifies the hopsit takes for \(m_{d}^{i}\) to reach the destination node after being forwarded from node \(a\) to node \(b\). \(\frac{3}{Hops_{b}}\), which denotes the contribution of node \(a\)'s selection of node \(b\) as the next hop to reducing the average number of hops in the network. The smaller value of \(Hops_{b}\) signifies a greater contribution to reducing network overhead, resulting in a higher reward value. In this context, the definition of 3 represents the average number of hops obtained through multiple simulations and emulations of the prophet routing algorithm. The discount factor will affect the likelihood of selecting the node previously chosen for forwarding. In this paper, the discount factor is defined to be updated when \(m_{d}^{i}\) is forwarded or reaches the destination node. It is calculated by (5)  
\[\gamma_{a}(m_{d}^{i})=\gamma^{*}e^{-\left(\frac{Hops}{3}+\frac{T^{*}}{TTL}\right)} \tag{5}\]  
\(\gamma^{*}\) is a discount factor constant, \(T^{*}\) represents the total lifetime of the message, and \(TTL\) indicates the remaining time to live for the message. \(Hops\) denotes the number of times \(m_{d}^{i}\) has been forwarded at this time. The larger the value of \(Hops\), the more frequently \(m_{d}^{i}\) will be forwarded, indicating that the efficiency of previously selected forwarding nodes is moderate. The smaller the value of \(TTL\), the shorter the remaining lifetime of \(m_{d}^{i}\), emphasizing that node \(a\) should prioritize the results obtained from selecting the current next hop. Incorporating the relationship between nodes and messages, as well as real-time performance metrics in the network, such as average message hops and average latency, enables the double Q-learning algorithm to accurately assess the corresponding Q-values when nodes forward messages through learning from the network environment.
## 5 TSN Scheduling Problem Formulation
### TSN Scheduling Problem Formulation Assumptions
#### 5.3.1 Assumptions on Synchronization  
A common view of the time is required for TAS to function properly. Therefore, TAS scheduling can not happen without assuming global synchronization. While the authors of [11, 15, 24, 31, 41, 47, 49, 65, 67, 68, 69, 70, 72, 73, 74, 94, 105, 116, 117, 118, 119, 123] assumed a globally synchronized network, this assumption is not required for asynchronous traffic shapers such as ATS or CBS. Ideally, time synchronization has no error [68], but in real networks, synchronization imprecision is inevitable. For example, [46] assumed a network precision of 5.008 \(\upmu\)s, and [105] assumed a maximum clock deviation between nodes after an out-of-sync event to be 1200 \(\upmu\)s which are more realistic assumptions.
## 3 General Strategies for Energy Efficient BSN Hardware
### Communication versus _Computation_
While there are many COTS radios and protocols available today that are serviceable for BSN applications, there is still a large opportunity for custom radios that provide better energy efficiency for the applications of the BSN community. These radios can take advantage of the small transmission distance and asymmetry of the channel and provide a substantial benefit to the power consumption of the devices. For example, [13] presents a 830 pJ/bit 2.4 GHz radio that can transmit with a data rate of 500 kbps. [14] presents a 2 Mbps low power receiver that consumes 0.18 nJ/b and [15] shows a 0.65 nJ/b 100 kbps receiver at 1.9 GHz. For sub 1 GHz transmission, [16] shows a 1 Mbps OOK transceiver that operates at 10 nJ/bit with very fast startup time of 2.5 us to allow for efficient duty cycling. All the previously mentioned low power transmitters and receivers take advantage of short range requirements of BSNs and consume much less energy compared to common COTS radios such as Bluetooth and Zigbee. With these improvements in energy consumption, BSNs can run much longer on a single battery charge or the device can be made smaller by allowing the same runtime but with a smaller battery. It is worth noting that since standards for BSNs are still under development, a concrete guideline for low power radios is not readily available.