esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Layer Security: Encrypting Information via Common Networked Physics
## 3 GLS Encryption Using Physical Networked Dynamic
### Active and Passive Attackers
#### 3.3.2 Active Attackers  
The active attacker in GLS aims to degrade the networked dynamics and their dependencies, which will subsequently deteriorate the GLS encryption that relies on them. The method is to (1) hack some of the network nodes and (2) frequently inject harmful jamming inputs from them to the networked dynamical system. Here, different from perturbation \(\mathbf{b}_{k}\) in Equation (1), the jamming injection is more frequent and with larger amplitudes. For example, in the electric bus system, an active attacker can steeply add huge and harmful power usages at a single or multiple buses, and subsequently affect the whole networked dynamic (such as rotor speed and angle).  
In this view, the GLS performance depends on whether the GFT-based surrogate matrix \(\mathbf{\Gamma}\) is still holding for analysing the linearly dependency of rows (i.e., relay selection and weight computation) in \(\mathbf{X}\). It is straightforward that an increasing rate of jamming injection will make \(\mathbf{\Gamma}\) fail to represent \(\mathbf{X}=\mathbf{\Gamma}\mathbf{\hat{X}}\), as a large perturbation can be artificially designed by orthogonal vectors that do not belong to the graph-bandlimited subspace spanned by the columns of \(\mathbf{\Gamma}\). We will evaluate the GLS performance versus the active attackers with different injection rates in Section 4.3.
# Green Small Cell Operation of Ultra-Dense Networks Using Device Assistance +
###### Abstract  
As higher performance is demanded in 5G networks, energy consumption in wireless networks increases along with the advances of various technologies, so enhancing energy efficiency also becomes an important goal to implement 5G wireless networks. In this paper, we study the energy efficiency maximization problem focused on finding a suitable set of turned-on small cell access points (APs). Finding the suitable on/off states of APs is challenging since the APs can be deployed by users while centralized network planning is not always possible. Therefore, when APs in small cells are randomly deployed and thus redundant in many cases, a mechanism of dynamic AP turning-on/off is required. We propose a device-assisted framework that exploits feedback messages from the user equipment (UE). To solve the problem, we apply an optimization method using belief propagation (BP) on a factor graph. Then, we propose a family of online algorithms inspired by BP, called DANCE, that requires low computational complexity. We perform numerical simulations, and the extensive simulations confirm that BP enhances energy efficiency significantly. Furthermore, simple, but practical DANCE exhibits close performance to BP and also better performance than other popular existing methods. Specifically, in a small-sized network, BP enhances energy efficiency 129%. Furthermore, in ultra-dense networks, DANCE algorithms successfully achieve orders of magnitude higher energy efficiency than that of the baseline.  
cellular networks; small cell; energy efficiency; belief propagation; optimization Article
# Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications
## 2 Antenna Geometry
The other applications of mm-wave are in radar and image sensing [27; 28], next-gen Wi-Fi (WiGig), virtual reality for 3D rendering, medical applications [29; 30], military applications [31; 32], and IoT applications [33]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline C1 & C2 & C3 & C4 & C5 & C6 & C7 & C8 & C9 & C10 & C11 & C12 \\ \hline
3.952 & 1.098 & 0.77 & 0.001 & 0.517 & 0.53 & 1.247 & 0.68 & 2.05 & 1.575 & 1.09 & 3.78 \\ \hline L1 & L2 & L3 & L4 & L5 & L6 & L7 & L8 & L9 & L10 & L11 & L12 \\ \hline
0.25 & 0.07 & 0.09 & 0.558 & 0.299 & 0.01 & 0.01 & 0.06 & 0.01 & 0.01 & 0.01 & 0.01 \\ \hline R1 & R2 & R3 & R4 & R5 & R6 & R7 & R8 & & & & \\ \hline
48.79 & 112.8 & 38.62 & 90.25 & 38.62 & 80.67 & 45.64 & 26.74 & & C1-C12 in pF, L1-L12 in nH, and R1-R8 in ohm \\ \hline \end{tabular}
\end{table} 
Table 1: Optimized lumped component values. Figure 1: Single element antenna geometry: (**a**) Top view, (**b**) Bottom view, and (**c**) Simulates refle-tion coefficient. Figure 2: Proposed single antenna element: (**a**) Equivalent lumped circuit model (**b**) Impedance real part, and (**c**) Impedance imaginary part. The developed MIMO antenna covers a 37-40 GHz frequency band, which has been reported in [34; 35; 36]. This also covers the FR2 bands above 6 GHz for 5G applications, which are n258/n259/n261 (24.25-29.5 GHz) and n260 (37-43.5 GHz) [37; 38; 39; 40; 41; 42; 43; 44].
## 1 Introduction
At present, most studies have shown that real-time bus information, such as arrival time and in-vehicle congestion information, has a significant impact on passengers' bus-route selection. These studies have combined relevant theories and models to analyze passengers' route selection behavior under information-induced conditions. However, in the research process, these studies mainly focus on the path selection behavior of individuals, and the research content from the same group level is less. In addition, although the application of game theory models in the analysis of transportation choice behavior is becoming increasingly mature, this research mainly focuses on transportation mode selection and vehicle route selection. In contrast, few studies concentrate on passenger bus-route selection. Therefore, this study attempts to analyze the internal interaction of bus passengers by applying evolutionary game theory to study the time evolution law of bus passengers' travel route selection behavior under the condition of information induction. In addition, the study aims to clarify the dynamic adjustment results of bus passenger group behavior. From this, the goal is to propose guidance strategies and bus scheduling adjustments to achieve a win-win for passengers and bus operating companies.
## 5 Experiment Results and Analysis
The PEJA-NC algorithm is the same as the PEJA algorithm, except that the PEJA-NC algorithm does not consider the signal overhead and energy consumption generated by narrowband user handover during CC allocation, namely, the CC allocation process does not control the handover of narrowband users. Figure 10 shows the comparison of the standardized energy efficiency of the two algorithms. Suppose the average carrier aggregation capacity (\(\hat{\text{Ca}}=3\)) of the user.
## 6 Conclusions  
The article proposes a deep learning-based computer-aided diagnosis system for breast ultrasound reports, which can assist patients in quickly and conveniently obtaining diagnostic results through mobile terminals. The following conclusions can be drawn: Firstly, the breast ultrasound image generation method proposed in this article can effectively segment the ultrasound report and remove noise from the breast ultrasound images. After our testing, using denoised breast ultrasound images as input for benign and malignant classification can improve accuracy by 0.57%, recall rate by 1.18%, specificity by 3.43% and \(F1\) score by 3%. This indicates that adding additional recovery information can improve the recall rate of the classification model, thereby reducing missed diagnosis problems and saving more patients' lives. Secondly, the breast ultrasound report classification model proposed in this article still showed good classification performance when inputting breast ultrasound images containing noises, with 89.31% accuracy, 88.65% recall rate, 89.57% specificity, 89.42% \(F1\) score and 94.53% AUC. The main work in the future will be performed from two aspects. In terms of breast ultrasound image generation, the image generation method used in this paper may result in a decrease in the quality of generated images when encountering image deformation problems. In future work, it is considered to continue optimizing the generation model or using better generation modelsto complete this part of the work. Regarding breast ultrasound image classification, the task addressed in this article pertains to the classification of benign and malignant breast ultrasound images. In actual medical scenarios, BI-RADS grading is also performed on breast tumors. From grade 0 to grade 6, the possibility that the tumor is a malignant tumor gradually increases. Therefore, in subsequent work, a multi-classification task for breast tumors is planned. In addition, it is also planned to improve the existing model structure to further enhance the classification performance of the model.  
[PERSON] designed and performed the research and wrote the paper; [PERSON] proposed revisions to the paper and ensured the accuracy of the experiments; [PERSON] made critical revisions to the paper, proposed improvements to some of the methods and was responsible for data collection. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data available upon request.  
The authors declare no conflict of interest.
# Interference-Robust Transmission in Wireless Sensor Networks
## 5 Performance Evaluation
When the SNR is not high enough to employ the highest rate, however, DRACER I may not outperform DRACER II (as shown in Figure 7). It can also be seen that the proposed scheme outperforms DRACER I and II by adjusting both the transmission rate and the payload size, maximally exploiting the white space of the interference signal. The performance gap between the proposed scheme and DRACER I increases as the SNR decreases. Figure 10c depicts the performance of the proposed scheme without adjustment of the transmission rate, DPLC and the PPR schemes. It can be seen from Figure 10c that DPLC provides poor throughput performance, which is mainly due to the slow adaptation of the payload size. It can also be seen that the performance of PPR schemes is limited mainly due to the use of a fixed frame structure and small maximum payload size. It can also be seen that the proposed scheme outperforms the other schemes, which is mainly due to the fast adaptation of the payload size with a large maximum payload size. Note that it may not be easy for the PPR schemes to increase the maximum payload size since the computational complexity may considerably increase as the number of blocks for the packet partitioning increases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR** & **2 dB** & **2 dB** & **6 dB** & **6 dB** & **12 dB** & **12 dB** \\ \hline \(\rho\) & **0** & **0.2** & **0** & **0.2** & **0** & **0.2** \\ \hline DRACER I (300 B) & 5.9 & 19.0 & 3.9 & 7.0 & 2.0 & 3.0 \\ DRACER I (1000 B) & 6.9 & 46.0 & 2.9 & 11.0 & 1.0 & 3.0 \\ DRACER II (300 B) & 6.9 & 17.0 & 3.9 & 7.0 & 2.0 & 3.0 \\ DRACER II (1000 B) & 5.9 & 61.1 & 2.9 & 14.0 & 1.0 & 3.0 \\
15.4 baseline (300 B) & 7.9 & 34.1 & 5.9 & 28.1 & 4.9 & 24.0 \\
15.4 baseline (1000 B) & 7.9 & 690.4 & 5.9 & 396.8 & 4.9 & 396.8 \\ Seda & 8.8 & 19.0 & 6.9 & 16.0 & 5.9 & 14.0 \\ HiFrag & 8.8 & 18.0 & 6.9 & 15.0 & 5.9 & 14.0 \\ GreenFrag & 13.8 & 20.0 & 12.8 & 15.0 & 11.8 & 14.0 \\ Proposed scheme & 5.9 & 14.0 & 2.0 & 6.0 & 1.0 & 3.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Transmission delay according to the SNR when \(\rho=0\) and 0.2 (unit: s). DRACER, dynamic rate adaptation and control for energy reduction; HiFrag, hybrid frame fragmentation scheme. Figure 10: _Cont._
## 2 Modeling Principles
### Modeling the Semi-Active Hybrid Mass Damper  
Through the use of an SHMD, the energy-dissipation capacity of a semi-active device is exploited and energy is added only when required through force actuators. The main difference between an ATMD and the novel SHMD configuration lies in the fact that the actuators of the ATMD both add and dissipate energy, whereas the forcing provision of the SHMD can only add energy. To this end, it can be realised that when the actuators of the ATMD are required to add energy to the system, sufficient power should be provided so that the \"braking\" force acting on the DVA's mass by the passive damping elements of the ATMD is surpassed for the mass to be accelerated, and sufficient control force can then be applied to the system. On the contrary, the novel SHMD configuration lowers the active actuation demands by lowering the semi-active damping component to its minimum value throughout the period of active actuation. The steps required for the implementation of this configuration and the calculation of the envisaged control action, \(u_{shmd}(t)\), are explicitly introduced below and summarised in Figure 3:  
1. Design of a semi-active controller based either on an active controller that is clipped using Equation (7) for semi-active control purposes or using direct output feedback control algorithms such as the ones found in the groundhook control scheme for alleviation of the online computational burden of Equations (7) and (8).
2. Design an active controller using active control algorithms such as LQR, PID or similar designed to satisfy performance and robustness specifications of the non-linear system (i.e., system including the semi-active controller).
3. Limit the capacity of the active actuator to only add power to the system: \[q_{a}(t)=f_{a}(t)\dot{x}_{r}(t)>0\] (11)
4. Incorporation of both active and semi-active forces to the system using: \[u_{shmd}(t)=u_{sa}(t)+f_{a}(t)\] (12)
5. Optimisation of maximum and minimum damping ratios of the semi-active control device for the case of on-off control.  
Using the steps described, the resulting control signals relative to the active control counterpart should attain the form shown in Figure 4b,c. Evidently, the active control component of the novel device configuration (Figure 4c) can only supply force in the \(q_{a}\geq 0\) regions, whereas the semi-active control component is able to only supply force in the \(q_{a}\leq 0\) regions. With reference to Figure 4b, the fifth and final step of the SHMD design procedure, the optimisation of the maximum and minimum damping coefficients of the semi-active component determines the slope and the magnitude of the control signal which in turn severely influence the performance of the hybrid system.
## 3 Grey Model with Seasonal Index Adjustment
### Prediction Steps of Grey Model with Seasonal Index Adjustment  
The grey model with a seasonal index adjustment uses the seasonal index to adjust the original number series, then uses the grey model to fit the data development trend, and finally uses the seasonal index reverse adjustment to restore the seasonal fluctuations of the data. The main prediction steps are as follows:  
(1) Calculate the seasonal index. Calculate the data according to Equations (1)-(3) to obtain seasonal index.  
(2) Data preprocessing. Divide the original sequence by the seasonal index, we can obtain a group of smooth sequence \(x^{(2)}=\left\{x_{1}^{(2)},x_{2}^{(2)},\cdots,x_{i}^{(2)},\cdots x_{n}^{(2)}\right\}\), which is a stationary smooth sequence without seasonal fluctuations. The \(x_{i}^{(2)}\) can be given by:  
\[x_{i}^{(2)}=x_{i}^{(0)}/SI \tag{7}\]  
(3) Model calculation. We use the new sequence \(x^{(2)}\) as the modeling sequence \(y^{(0)}\) of the grey model, establish the grey prediction model, and obtain the prediction sequence \(\hat{y}^{(0)}\).  
(4) Data recovery. We use the seasonal index to make a reverse adjustment. That is, the grey prediction sequence \(\hat{y}^{(0)}\) is multiplied by the seasonal index \(SI\) to obtain the simulation value \(\widetilde{x}^{(0)}\), where \(\widetilde{x}^{(0)}\) is as follows:  
\[\widetilde{x}^{(0)}=\hat{y}^{(0)}\times SI \tag{8}\]
## 3 Results and Discussion
### Substrate Temperature  
The dominant factor of different substrate temperatures is the thermal energy of the substrate atoms. Substrate temperatures of 350 K, 500 K, 650 K, 800 K, and 950 K were discussed in this study. To investigate the effect of substrate temperature, tantalum was selected as the substrate material, while the other parameters were set as follows: incident energy = 5 ev, deposition height = 5000 A, incident angle = 30\({}^{\circ}\), and deposition rate = 10 atoms/ps. The corresponding copper seed layers under different substrate temperature at 2500 ps are shown in Figure 5.  
By increasing the temperature of the substrate, thermal energy of the substrate atoms increased, which also delivered the thermal energy to the deposited copper. Consequently, this increased the diffusivity of both the substrate atoms and the deposited copper atoms. Overall distribution of the deposited copper atoms with respect to substrate temperatures is shown in Figure 6.  
At the wing of the trench, copper atoms gradually diffused into the trench and accumulated at the top sidewall, which caused the necking to decrease. As necking decreased, the number of atoms reaching mid sidewall would also decrease. The situation at the lower sidewall was more complicated; with the decreasing necking, atoms reaching the lower sidewall decreased. However, with the increasing diffusivity caused by the increasing temperature, atoms from the island at the bottom diffused to the lower sidewall. No significant change of number of atoms at the lower sidewall was observed due to the equalizing influence of necking and diffusivity.  
Figure 5: Copper seed layers under different substrate temperatures.  
Figure 6: Overall distribution of the deposited copper atoms with respect to substrate temperatures.
###### Abstract  
In the present contribution, new-generation bar adsorptive microextraction devices combined with microliquid desorption, followed by high-performance liquid chromatography-diode array detection (BA\(\mu\)E-\(\mu\)LD/HPLC-DAD) are proposed for the determination of two very polar ultraviolet (UV) filters (2-phenylbenzamideimidazole-5-sulfonic acid (PBS) and 5-benzoyl-4-hydroxy-2-methoxybenzenesulfonic acid (BZ4)) in aqueous media. Different sorbents were evaluated as BA\(\mu\)E coating phases, in which polystyrene-divinylbenzene polymer showed the best selectivity for the analysis of both UV filters, with average extraction efficiency of \(61.8\pm 9.1\%\) for PBS and \(69.5\pm 4.8\%\) for BZ4. The validated method showed great reproducibility for the analysis of PBS and BZ4 UV filters, providing suitable limits of detection (0.04 \(\mu\)g L\({}^{-1}\) and 0.20 \(\mu\)g L\({}^{-1}\)), as well as good linear dynamic ranges (0.16-16.0 and 0.8-80.0 \(\mu\)g L\({}^{-1}\)), respectively. The proposed methodology was applied for monitoring the target analytes in several real matrices, including tap, sea, and estuarine waters, as well as wastewater samples. Despite some matrix effects being observed for some real samples, good selectivity and linearity were obtained. The present contribution showed an innovative analytical cycle that includes the use of disposable devices, which make BA\(\mu\)E much more user-friendly and suitable for the routine work, being a remarkable analytical alternative for trace analysis of priority compounds in real matrices.  
bar adsorptive microextraction; floating sampling technology; high-performance liquid chromatography; polar UV filters; real matrices +
Footnote †: journal: materials materials
## 2 Waveguide Design
The DW occurs when the phase-matching condition is satisfied [7]:  
\[\beta(\omega)=\beta(\omega_{\text{s}})+(\omega-\omega_{\text{s}})v_{\text{g}} ^{-1}+\frac{\gamma P_{0}}{2} \tag{1}\]  
where \(\beta(\omega)\) is the propagation constant of the anti-symmetric mode, \(\omega_{\text{s}}\) is the central angular frequency of soliton, \(v_{\text{g}}\) is the group velocity of soliton, \(\gamma\) is the nonlinear coefficient, and \(P_{0}\) is the peak power of the pump. According to Equation (1), the integrated dispersion \(\beta_{\text{int}}\) (or phase mismatching parameter) is given by:  
\[ \begin{array}{c}\beta_{\text{int}}=\beta(\omega)-\beta(\omega_{\text{s}})-( \omega-\omega_{\text{s}})v_{\text{g}}^{-1}-\frac{\gamma P_{0}}{2}\\ \approx\sum_{n\geq 2}\frac{(\omega-\omega_{\text{s}})^{n}}{n!}\frac{ \beta^{n}\beta(\omega_{\text{s}})}{\delta\omega^{n}}\end{array}  \tag{2}\]  
Since \(\gamma P_{0}\) is relatively small compared to the former Taylor expansion, it is usually neglected. Figure 2g shows the calculated \(\beta_{\text{int}}\) as a function of the wavelength at a 1.55 \(\upmu\)m pump wavelength. The anti-symmetric mode of Si\({}_{3}\)N\({}_{4}\)-coupled waveguides in [11], an uncoupled mode in a single-core waveguide on LNOI, and the anti-symmetric mode of the LNOI dual-coupled waveguide are shown together as a comparison. When compared with the single-core waveguide on LN, our structure has a lower MIR dispersion barrier and a higher visible one, which is beneficial for transferring power to the MIR-DW. By comparison, for the anti-symmetric mode of Si\({}_{3}\)N\({}_{4}\)-coupled waveguides, it features a shorter phase-matching wavelength range at the MIR region compared with our structure. We find that there is an upward trend towards zero in the \(\beta_{\text{int}}\) curve of our structure around 2.5 \(\upmu\)m, which will produce another MIR DW, as mentioned later. However, the upward trend towards zero is between 2.5 and 3 \(\upmu\)m in the \(\beta_{\text{int}}\) curve of the anti-symmetric mode of Si\({}_{3}\)N\({}_{4}\)-coupled waveguides. This part of the \(\beta_{\text{int}}\) curve is closer to the MIR phase-matching wavelength point compared with our structure. That results in a limited bandwidth and flatness of the MIR-DW generation.