esa-sceva/satcom-chunk-collection huggingface dataset:
# Make predictions from new data. y_pred = clf.predict(X_test),
## 7 Results
### Random Forests  
Figure 10 shows the evolution of the accuracy as a function of the number of trees considered for site 1. It has been verified that with a number of trees in the order of 50, it is ensured that the maximum precision has been reached. The calculation has been performed with 75 trees.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Fully Developed Tree** & \multicolumn{2}{c}{**GridSearchCV**} \\ \hline
**Site** & **Depth** & **Leaf Nodes** & **Depth** & **Leaf Nodes** \\ \hline
1 & 20 & 681 & 20 & 400 \\
2 & 22 & 437 & 15 & 177 \\
3 & 16 & 180 & 10 & 50 \\
4 & 20 & 269 & 10 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Complexity of fully developed trees compared to those obtained through GridSearchCV.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **1** & **2** & **3** & **4** \\ \hline
**criterion** & gini & gini & gini \\
**splitter** & random & random & random \\
**max\_depth** & None & 15 & 10 & 10 \\
**min\_samples\_split** & 2 & 8 & 2 & 4 \\
**min\_samples\_leaf** & 1 & 1 & 1 & 1 \\
**max\_leaf\_nodes** & 400 & 400 & 50 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Best parameters obtained by GridSearchCV.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Set** & **Accuracy** &
\begin{tabular}{c} **Correlation** \\ **Coefficient** \\ \end{tabular} & **MAE** & **MSE** & **R2** \\ \hline
1 &
\begin{tabular}{c} Test \\ Train \\ \end{tabular} & 0.12 (\(\pm\)0.02) & 0.62 (\(\pm\)0.04) & 4.08 (\(\pm\)0.26) & 34.56 (\(\pm\)4.34) & 0.29 (\(\pm\)0.09) \\
0.64 (\(\pm\)0.22) & 0.88 (\(\pm\)0.08) & 2.46 (\(\pm\)0.92) & 21.24 (\(\pm\)7.96) & 0.56 (\(\pm\)0.16) \\ \hline
2 &
\begin{tabular}{c} Test \\ Train \\ \end{tabular} & 0.23 (\(\pm\)0.03) & 0.49(\(\pm\)0.07) & 2.52(\(\pm\)0.18) & 15.99 (\(\pm\)2.34) & \(-\)0.12(\(\pm\)0.14) \\
0.53 (\(\pm\)0.21) & 0.60 (\(\pm\)0.15) & 1.40 (\(\pm\)0.55) & 9.33 (\(\pm\)3.77) & 0.35 (\(\pm\)0.26) \\ \hline
3 &
\begin{tabular}{c} Test \\ Train \\ \end{tabular} & 0.32 (\(\pm\)0.02) & 0.34 (\(\pm\)0.10) & 2.99 (\(\pm\)0.29) & 24.92 (\(\pm\)4.13) & \(-\)0.20 (\(\pm\)0.14) \\
0.60 (\(\pm\)0.08) & 0.54 (\(\pm\)0.11) & 2.63 (\(\pm\)0.35) & 22.19 (\(\pm\)3.71) & \(-\)0.07 (\(\pm\)0.17) \\ \hline
4 &
\begin{tabular}{c} Test \\ Train \\ \end{tabular} & 0.14 (\(\pm\)0.02) & 0.24 (\(\pm\)0.11) & 5.27 (\(\pm\)0.44) & 61.56 (\(\pm\)9.19) & \(-\)0.41 (\(\pm\)0.17) \\
0.50 (\(\pm\)0.16) & 0.54 (\(\pm\)0.18) & 3.92 (\(\pm\)0.94) & 47.35 (\(\pm\)12.41) & \(-\)0.09(\(\pm\)0.28) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Correlations obtained using GridSearchCV.  
The results obtained with RF are better than those obtained in all the analyzed metrics.
# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
## 3 An Energy Consumption Optimization for ICN  
In ICN, the energy consumption of a complete content distribution between nodes mainly includes two processes: the content is requested by the user and the content is transmitted to the user. The sum of the total energy consumed by these two processes is collectively referred to as complete energy consumption between nodes. Among them, the network energy consumption is closely related to the distance between nodes in the network. Therefore, through the network energy consumption model, the relationship between distance and energy consumption is established, which lays a foundation for the solution of the optimal stopping rule. Because the energy consumption caused by the distribution and reception of content between nodes is the main proportion, the network energy consumption model in this paper is constructed mainly by the energy consumption generated by the distribution and reception of content between nodes. The ICN energy consumption model and related parameters used in this paper are given below.
## 2 Test Arrangement
### Loading Device  
Figure 3 shows the horizontal loading devices which include four hydraulic servo actuators. Only the third floor was loaded by two actuators with the capacity of \(\pm\)630 kN. A same actuator was employed on the middle of the Axes 1 and 2 for the second floor while an actuator with the capacity of \(\pm\)250 kN was employed for the first floor. The displacement measurement range of all the actuators was \(\pm\)250 mm. The errors of both force and displacement are the 0.1% corresponding to the capacity. To prevent the local damage of the beams connecting the actuator, two side beams of the third floor, which located at the end of horizontal loading devices, were partly enlarged to the section of 400 mm \(\times\) 400 mm. For the bottom two floors, to enable a uniform transfer of horizontal forces to each story, as well as to ensure a tight connection among the beam, the actuator and floor slab, the far end was passed through the wall along the B axis and beam-anchored by high-strength bolts, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Compress Strength of** & **Compress Strength of** & **Bending Strength of** \\  & **Concrete** & **Grouting Material** & **Grouting Material** \\ \cline{2-3}  & **(N/mm2)** & **(N/mm2)** & **(N/mm2)** \\ \hline Precast parts of each floor & 31.6 & — & — \\ Cast-in-place parts of 1 st floor & 22.9 & 75.7 & 6.1 \\ Cast-in-place parts of 2 nd floor & 29.5 & 73.8 & 5.9 \\ Cast-in-place parts of 3 rd floor & 32.8 & 80.1 & 8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of the concrete.  
Figure 3: Planned arrangement of horizontal loading devices for the hybrid simulation testing: (**a**) elevation; (**b**) Plan view.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Diameter** & **Yield Strength** & **Limit Strength** \\ \hline
**(mm)** & **(N/mm2)** & **(N/mm2)** \\ \hline
6 & 353.1 & 504.2 \\
8 & 290.6 & 433.3 \\
10 & 308.9 & 429.9 \\
12 & 386.6 & 560.3 \\
14 & 340.8 & 483.8 \\
16 & 283.1 & 443.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material properties of the steel reinforcement.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 5 Attack Description and Explanation
### Explanation of Man-in-the-Middle (MiTM)  
Man-in-the-Middle (MiTM) attacks are a type of cyberattack where an adversary intercepts and manipulates communication between two parties, deceiving them into believing they are directly connected. This attack can also be considered a form of eavesdropping, as the attacker gains unauthorized access to and control over the communication. In the context of the Advanced Message Queuing Protocol (AMQP), MiTM attacks can be particularly disruptive. For instance, an attacker could fabricate temperature data and transmit them to a remote server or disable unsecured HVAC systems during extreme heat, creating critical challenges for service providers reliant on vulnerable models [7].  
In this study, the **Ettercap** tool was used to perform an MiTM attack, enabling the interception of communication between the client and the AMQP server.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Abnormal Traffic** \\ \hline TCP Handshake & RST \(|\) SYN \(|\) SYN/ACK \\ TCP Segment Features (Length \(|\) Seq \(|\) & \(0|\) Randomized \(|\) \(1|\) Randomized \\ NxtSeq \(|\) Ack) & \(0|\) Randomized \(|\) \(1|\) Randomized \\ Window Size & 512 (Attacker) \(|\) 64,240 (Target) \\ Syslog Logs & TCP SYN flood detected on port 5672 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Abnormal traffic features of AMQP DoS attacks.  
Figure 6: DoS attack data exchange packets (Experiment 3).
## 4 Numerical Result Analysis and Discussion
### Off-Design Working Conditions  
The performance curves of the mass flow rate to the expansion ratio at 70-110% nominal rotational speeds are presented in Figure 9. The benchmark value is set as the nominal rotational speed of 42,000 r/min. As seen in Figure 9, the mass flow rate increases first at the nominal rotational speed and then chokes with the expansion ratio. The mass flow rate approaches the choked mass flow rate when the expansion ratio is 2.896 at the design working condition. At the off-design working conditions, the rotational speed significantly impacts the mass flow rate when the expansion ratio is lower than 3.0. The mass flow rate increases at the same expansion ratio as the rotational speed increases. However, if the  
Figure 8: Contours of enthalpy and entropy at 50% blade span. (**a**) Static enthalpy; (**b**) entropy.  
Figure 7: Contours of static pressure and temperature at 50% blade span. (**a**) Static pressure; (**b**) static temperature.  
expansion ratio is higher than 3.0, the rotational speed variation has little impact on the mass flow rate. The radial inflow turbine is always choked with an expansion ratio higher than 3.788 regardless of the rotational speed. The maximum allowable expansion ratio is 5.47, and the corresponding choked mass flow rate is 16.358 kg/s.  
Figure 10 shows the variation curves of the total-to-static efficiency to the expansion ratio at different rotational speeds. At the nominal rotational speed, the value of the total-to-static efficiency increases and decreases with the expansion ratio. The total-to-static efficiency at the design working condition is 85.77%, close to the maximum value at the performance curve. This means that the current one-dimensional optimization design is appropriate. Moreover, the total-to-static efficiency always yields a value higher than 70%, with the expansion ratio region of 1.76-5.47. The performance curve and the local maximum total-to-static efficiency point shift towards the left with the decrease in rotational speed. The total-to-static efficiencies are higher than 75%, with an expansion ratio of 1.75-4.48 at 80-110% nominal rotational speed. However, the total-to-static efficiency is lower than 70%, with most expansion ratios at the 70% nominal rotational speed. Hence, it is suggested that the current radial inflow turbine avoid operating under these working conditions.
# The Lengths of Certain Real Conjugacy Classes and the Related Prime Graph
## 2 Preliminaries
Then, \(|x^{K}|_{p}=|x^{G}|_{p}\) for all non-central \(x\in\mathrm{Re}(K)\)._  
Proof. : Let \(x\in\mathrm{Re}(K)\) be non-central and let \(S_{C}\in\mathrm{Syl}_{p}(\mathrm{C}_{G}(x))\) and \(S_{G}\in\mathrm{Syl}_{p}(G)\) such that \(S_{C}\leq S_{G}\). Since \(|G|_{p}=|K|_{p}\), then \(S_{C}\leq S_{G}\leq K\). In particular, \(S_{G}\in\mathrm{Syl}_{p}(K)\). It follows that \(\mathrm{C}_{K}(x)=K\cap\mathrm{C}_{G}(x)\) and \(S_{C}\leq K\cap\mathrm{C}_{G}(x)\). Therefore, \(S_{C}\leq\mathrm{C}_{K}(x)\leq\mathrm{C}_{G}(x)\) and hence \(S_{C}\) is also a Sylow \(p\)-subgroup of \(\mathrm{C}_{K}(x)\). Thus, \(|\mathrm{C}_{G}(x)|_{p}=|S_{C}|=|\mathrm{C}_{K}(x)|_{p}\), and then \(|x^{G}|_{p}=|G:\mathrm{C}_{G}(x)|_{p}=|S_{G}:S_{C}|=|K:\mathrm{C}_{K}(x)|_{p}= |x^{K}|_{p}\). Lemma 11.: _Suppose that \(\Gamma_{r}^{*}(G)\) has two connected components with vertex sets \(\pi_{1}\) and \(\pi_{2}\), where \(2\
otin\pi_{2}\). Then, there exists an involution \(i\in G\) such that \(|i^{G}|>1\) is a \(\pi_{2}\)-number and \(\mathrm{C}_{G}(i)\) has a normal Sylow 2-subgroup._  
Proof. : Let \(p\) be a prime in \(\pi_{2}\). Then, \(p\) must divide \(|i^{G}|\) for some \(i\in\mathrm{Re}^{*}(G)\). Clearly, every prime divisor of \(|i^{G}|\) is adjacent to \(p\in\pi_{2}\); this implies that \(|i^{G}|\) is a nontrivial \(\pi_{2}\)-number. Hence, \(|i^{G}|>1\) is odd and so \(i^{2}=1\) by Lemma 2(a) and thus \(i\) is an involution of \(G\). Assume that \(\mathrm{C}_{G}(i)\) has a nontrivial real element \(x\) of odd order. By Lemma 2(b), we can assume that \(|x|=q\), where \(q\) is an odd prime. Then, \(xi=ix\) and \(|x^{G}|\) is even, so \(|x^{G}|\) is a \(\pi_{1}\)-number and thus \((|x^{G}|,|i^{G}|)=1\). Lemma 3(a) implies that \(xi\) is a real element. Furthermore, since \((|x|,|i|)=1\), \(2p||(ix)^{G}|\) by Lemma 3(b). Since \(\pi(|ix|)\subseteq\{2,p\}\), \(ix\in\mathrm{Re}^{*}(G)\). Then, \(2\in\pi_{1}\) and \(p\in\pi_{2}\) are adjacent in \(\Gamma_{r}^{*}(G)\), and we get a contradiction. Therefore, \(\mathrm{C}_{G}(i)\) has no nontrivial real element of odd order and thus it has a normal Sylow 2-subgroup by Lemma 5. We close this section with the remark that trivial class is the only real class of \(G\) if and only if \(G\) has odd order ([10], Lemma 2.4(iii)), and therefore \(G\) has no nontrivial real primary elemnet if and only if \(G\) has odd order.
# Composite Foundation Settlement Prediction Based on LSTM-Transformer Model for CFG
## 1 Introduction
By incorporating construction period filling information into the training set, it is possible to better extract data features and improve the prediction of the foundation, thereby effectively guiding construction. More classic cases should be added. By comparing and analyzing various neural network models used in existing settlement prediction studies, it was found that the long short-term memory (LSTM) networks [19; 20], due to their superior memory retention and selection capabilities, effectively handle nonlinear relationships within data and capture long-term dependencies among data points. They have been extensively applied in predicting landslide displacement [21], dam deformation [22], and stable parameters for tunnel boring machines [23], and modeling the evolution of shear behavior between soil and structural surfaces [24], among other applications. Based on these studies, this paper proposes a new LSTM-Transformer-based CFG composite foundation settlement prediction model. In this model, the first layer uses the gating mechanism of the LSTM to perceive coupling features related to the settlement rate in the sequence. After obtaining a comprehensive dependency relationship, it is transmitted to the Transformer layer. The Transformer, through self-attention, focuses on more crucial task information. With supervised training on extensive data, it resists the impact of noise and outliers, emphasizing important factors in long-term multivariate sequences, thereby improving prediction accuracy and the model's generalizability. Currently, the LSTM-Transformer model has been applied in fields like solar power generation forecasting [25], prediction of the remaining life of rolling bearings [26], and short-term electric load forecasting for industrial users [27], but it has not yet been applied in the field of foundation settlement. This paper, based on the LSTM-Transformer model, takes the newly constructed Chang-Zhu-Tan Intercity Railway and the Chang-Shi Railway connecting line's high fill CFG pile composite foundation engineering as the background. It integrates information observed during the construction period to perform foundational settlement modeling and predictive analysis. Additionally, traditional machine learning models--SVM, LSTM, and Transformer--are used for comparative analysis of settlement predictions.
## 4 Experiments and Analysis of Results
### Analysis of Results
#### 4.4.2 Analysis of Model Predictions for Various Types of Entities  
The precision, recall, and F1 scores of the MD-SERel model for extracting various types of pesticide entity relations are presented in Table 4. The results show that the model performed well in recognizing all types of relations, achieving balanced evaluation metrics across different relation categories. All F1 scores were above 90%. Among all recognized relationships, the identification performance for \"PE\" was relatively poor, with an F1 score of 90.6%. Upon analyzing the pesticide dataset, we found that the expressions in this category were diverse, such as \"long coat\", \"long pants\", \"hats\", \"long coat long pants\", \"long coat long pants hats\", etc., with complex overlap. Therefore, errors may have occurred during the model's prediction process. The \"CON\" had the best recognition effect, with an F1 value of 97.1%. In the data analysis, we found that weed entities usually appeared densely, and each weed entity was separated by \"\", so the contextual information was more fixed than other entities, which is easier to recognize using the model.
# Development of a Novel De-NOX Technology for the Aftertreatment of Ship Exhaust Gases
## 3 Results and Discussion
### Operation of Catalytic Oxidation Unit  
The purpose of the Catalytic Oxidation unit is to oxidize PM and CO to CO\({}_{2}\). The catalyst selected for this purpose (see Section 2.1) presents the highest conversion and selectivity towards CO\({}_{2}\) at 300 \({}^{\circ}\)C. Therefore, the gaseous stream after the SWA unit (30 \({}^{\circ}\)C) was preheated to 300 \({}^{\circ}\)C before entering the catalytic oxidation unit with the use of heating tapes, covering the external surface of the pipeline that leads to the catalytic oxidization reactor. The PM concentration at the outlet of the Catalytic Oxidation Reactor was measured to be 69 mg/m\({}^{3}\), a value that corresponds to a 67.5% reduction and a 72.4% overall reduction (before the SWA unit). In addition, during the operation of the oxidizing catalyst, the concentration of CO in the feed stream was found to be reduced from 645 ppm to 451 ppm (30% reduction). The latter results are in good agreement with the existing literature and common practice, which has shown that catalytic oxidation and/or multi-catalytic soot filtration are quite effective methods for the elimination of PM from marine exhaust streams [22].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & & & **Qualitative Inferences for** & \\  & & **Pre** & **Post** & **Effect Magnitude** & **rANOVA** \\  & & & & **(Mean Difference, \(\pm\)90\% CL)** & \\ \hline  & SG & 2.09 \(\pm\) 0.37 & 1.99 \(\pm\) 0.45 & * (\(-\)0.10, \(\pm\)0.15) & \\  & HG & 2.05 \(\pm\) 0.51 & 2.23 \(\pm\) 0.50 & ** (0.19, \(\pm\)0.14) & \(p\) = 0.061 \\  & CG & 2.10 \(\pm\) 0.42 & 2.06 \(\pm\) 0.37 & * (0.04, \(\pm\)0.18) & \\ \hline  & SG & 42.0 \(\pm\) 4.3 & 46.9 \(\pm\) 3.0 a & *** (4.88, \(\pm\)1.80) & \\ CMJ [cm] & HG & 42.1 \(\pm\) 3.3 & 44.0 \(\pm\) 2.8 & *** (1.89, \(\pm\)0.98) & \(p\) \(<\) 0.001 \\  & CG & 43.0 \(\pm\) 4.1 & 42.8 \(\pm\) 3.3 & (\(-\)0.23, \(\pm\)1.23) & \\ \hline  & SG & 55.6 \(\pm\) 6.4 & 59.5 \(\pm\) 7.4 a & *** (3.93, \(\pm\)1.45) & \\ PSJT [cm] & HG & 54.4 \(\pm\) 5.2 & 55.2 \(\pm\) 5.2 & * (0.86, \(\pm\)1.30) & \(p\) = 0.002 \\  & CG & 55.2 \(\pm\) 4.8 & 54.9 \(\pm\) 3.9 & (\(-\)0.29, \(\pm\)1.57) & \\ \hline  & SG & 663 \(\pm\) 0.6 & 698 \(\pm\) 0.5 & *** (0.35, \(\pm\)0.16) & \\ CH [cm] & HG & 624 \(\pm\) 0.5 & 630 \(\pm\) 0.5 & * (0.06, \(\pm\)0.19) & \(p\) = 0.082 \\  & CG & 671 \(\pm\) 0.4 & 671 \(\pm\) 0.5 & (0.00, \(\pm\)0.23) & \\ \hline  & SG & 1.02 \(\pm\) 0.04 & 0.97 \(\pm\) 0.05 & *** (\(-\)0.05, \(\pm\)0.02) & \\
5 m ST [s] & HG & 1.03 \(\pm\) 0.05 & 1.02 \(\pm\) 0.05 & * (\(-\)0.01, \(\pm\)0.03) & \(p\) = 0.088 \\  & CG & 1.10 \(\pm\) 0.08 & 1.10 \(\pm\) 0.09 & * (0.00, \(\pm\)0.03) & \\ \hline  & SG & 1.75 \(\pm\) 0.06 & 1.72 \(\pm\) 0.06 a,b & *** (\(-\)0.04, \(\pm\)0.02) & \\
10 m ST [s] & HG & 1.78 \(\pm\) 0.07 & 1.77 \(\pm\) 0.07 & (\(-\)0.01, \(\pm\)0.02) & \(p\) = 0.050 \\  & CG & 1.84 \(\pm\) 0.11 & 1.84 \(\pm\) 0.11 & (0.00, \(\pm\)0.03) & \\ \hline  & SG & 3.03 \(\pm\) 0.09 & 2.94 \(\pm\) 0.11 a,b & *** (\(-\)0.09, \(\pm\)0.03) & \\
20 m ST [s] & HG & 3.08 \(\pm\) 0.11 & 3.05 \(\pm\) 0.10 & * (\(-\)0.02, \(\pm\)0.03) & \(p\) = 0.002 \\  & CG & 3.12 \(\pm\) 0.14 & 3.13 \(\pm\) 0.14 & (0.01, \(\pm\)0.04) & \\ \hline  & & & & \\ \end{tabular} Values are given as mean \(\pm\) SD; a = sig. dif. to pre, b = sig. dif. SG-CG; chances that the true magnitude of the effects is beneficial, * = possibly (25-75\%), ** = likely (75-95\%), *** = very likely (95-99.5%); CL = confidence limits, RSI = reactive strength index, CMJ = counter movement jump, PSJT = pivot-step–jump test, CH = crossover triple-hop test, 5 m ST = 5 m sprint time, 10 m ST = 10 m sprint time, 20 m ST = 20 m sprint time.  
\end{table} 
Table 3: Comparison of jumping and speed performance among the SG (\(n\) = 9), HG (\(n\) = 9), and CG (\(n\) = 7) pre- and post-intervention. Figure 2: Illustration of absolute and percentage changes in selected change of direction (CoD) performances of the different intervention groups pre- and post-intervention (the dashed area represents the SWC for a small effect). Each color represents one participant (red-yellow color scheme with squares = SG; blue color scheme with triangles = HG; green color scheme with circles = CG).
# Cooperative Jamming with AF Relay in Power Monitoring and Communication Systems for Mining
## 3 Optimization Problem Transformation
We compare the proposed schemes with a similar one without jamming by presenting the numerical results in Figure 4. Our proposed scheme achieves better performance in terms of the average secrecy rate, especially with lower uncertainty and higher transmit power. Therefore, to some extent, jamming can disturb the interception of eavesdroppers even with higher uncertainty. Figure 3: Average secrecy rate versus power constraint, a comparison of different antenna numbers and CSI uncertainty. Figure 2: Average secrecy rate versus the number of iterations, a comparison of different ports number and CSI uncertainty.
## 7 Conclusions  
We consider an integrated electro-absorption modulator within a coupled-cavity VCSEL structure (EAM-VCSEL) and demonstrate that it is possible to modulate the output light power at frequencies  
Figure 8: Eye diagrams of EAM-VCSEL output power for digital, non-return-to-zero modulation at 100 Gb/s. (**a**) \(d_{EAM}=0.5025\) μm; (**b**) \(d_{EAM}=0.501\) μm; (**c**) \(d_{EAM}=0.504\) μm.  
Figure 7: (color online) Time traces for digital, non-return-to-zero modulation of EAM VCSEL at 100 Gb/s. (**a**) Output power; (**b**) \(\alpha_{EAM}\).  
as high as 100 GHz by modulating the EAM voltage. Such an ultrahigh bandwidth modulation can be achieved by a careful design, utilizing detuned loading and the chirp factor of the EAM material in order not to disturb the amplitude lasing condition of the VCSEL. We derive expressions for the modulation transfer function (MTF) of the EAM-VCSEL for small-signal modulation of either the EAM losses or the VCSEL injection current. We present a number of MTF characteristics for three typical cases of strong, intermediate and small coupling strength. These three cases are also considered when carrying out the large-signal modulation response of the EAM-VCSEL and a clear open-eye diagram is demonstrated at 100 Gbs for an optimal EAM cavity length.  
On the base of our analysis, we conclude that it is not only the coupling strength between the VCSEL and EAM cavities that is important but also the refractive index induced effect in the EAM cavity. Therefore, an optimal EAM cavity length exists when modulating the EAM absorption in order to obtain best modulation. Ideally, for an optimized EAM-VCSEL design, the photon density in active section will not vary as the EAM losses are modulated. The field inside the active layer will then be purely frequency modulated. In addition, the field from the bottom of the VCSEL will exhibit flat frequency modulation without any residual amplitude modulation. Only the field from the top will exhibit amplitude modulation due to the EAM loss modulation and the frequency-to-amplitude modulation conversion caused by the detuned loading during output coupling, i.e., the wavelength dependence of the EAM output mirror transmittivity.  
The two authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO) (No. G0E5819N) and the Swedish Research Council project \"Ultra-Fast Optical-Cavity-Enhanced Micro-Lasers\".  
The authors declare no conflict of interest.