esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurement Results and Analysis
### Measurement Results and Modeling
Even if the sensor nodes have to be randomly thrown to the ground in some specific applications, it is still beneficial to increase the antenna height of the sink node. As shown in Figure 3, as the antenna height of transceivers increased, the difference between the one-slope and two-slope model became more and more obvious. The detailed fitting results are shown in Table 1. When \(H_{\text{f}}=H_{\text{r}}=5\) cm, the one-slope model was identical to the two-slope model. Since the one-slope model is simpler and easier to implement, it is more practical to be adopted for scenarios with extremely low antenna height. However, when the antenna height reached 50 cm or higher, there were evident differences between these two models. In the two-slope model, the path loss exponent of the second slope \(n_{2}\) was significantly larger than \(n_{1}\). The higher was the antenna height, the more obvious was this trend. Furthermore, the precision of both models were obtained by the comparison of values predicted by models and the measured values. The mean error, root mean squared error (RMSE) and mean absolute percentage error (MAPE) of two-slope model were all smaller than those of the one-slope model. This suggests that it is better to adopt the two-slope model to describe the characteristics of near-ground propagation except for the scenario of extremely low  
Figure 4: Path loss models under the flat concrete road scenario: (**a**) one-slope model; and (**b**) two-slope model. Figure 5: Path loss models under the flat grass scenario: (**a**) one-slope model; and (**b**) two-slope model. antenna height. The low fitting errors shown in Table 1 also validated the effectiveness of using the log-normal shadowing model, which better reflected the real near-ground propagation characteristics.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 5 Power-Optimized Cooperative MAC Protocol
### Cooperative Node Selection Phase
#### 5.2.2 Optimal Cooperative Node Group
If the waiting time exceeds \(T_{\rm R}\), the cooperative contention phase ends because there will be no more HTS packet transmissions. The sender calculates the optimal transmission powers based on the received HTS packets and declares the results through an OPD packet. The reason for this HTS retransmission strategy is that the nodes with higher cooperative gain should always have priority to be employed than the nodes with lower cooperative gain. Figure 4: Second case of optimal cooperative node group: No cooperative nodes with higher cooperative gain contend within a time interval of \(T_{\rm E}\). Figure 5: Third case of optimalcooperative node group: help-to-send (HTS) packet transmission collisions occur. On receiving the OPD packet, each cooperative node knows whether it will take part in the following data packet transmission phase and the value of its corresponding transmission power. Then the PO-CMAC protocol enters into the data packet transmission phase. Combining this cooperative node selection strategy with the LETPO algorithm, we can consider both the average energy consumption of each data packet transmission (i.e., the nodes' energy efficiency) and the energy utilization of the network, thus extending the network lifetime efficiently.
## 2 Topology Optimization of the Mathematical Model Based on SIMP
### Density Filter Method  
This paper utilizes the widely validated density filter method [41] to alleviate checkerboard patterns and mesh dependence in TO. This approach can be mainly formulated as follows:  
\[\widetilde{\mathbf{x}}_{i}=\frac{\sum\limits_{j\in N_{i}}H_{ij}x_{j}}{\sum \limits_{j\in N_{i}}H_{ij}} \tag{2}\]  
where \(\widetilde{\mathbf{x}}_{i}\) represents the filtered unit density vector, \(N_{i}\) is the set of elements \(j\) for which the center-to-center distance \(\Delta(i,j)\) to element \(i\) is smaller than the filter radius \(r\), and \(H_{ij}\) denotes the weight coefficient, a function of the distance between adjacent units, as presented below:  
\[H_{ij}=\max(0,\ r-\Delta(i,j)) \tag{3}\]  
where \(\Delta(i,j)\) denotes the distance between the centers of units \(i\) and \(j\), and \(r\) signifies the filter radius. The filtered density could define a modified density field by updating Equation (1) as  
\[\mathbf{E}_{i}(\widetilde{\mathbf{x}}_{i})=\widetilde{\mathbf{x}}_{i}^{p} \mathbf{E} \tag{4}\]
## 4 Theoretical Analysis of Different Detection Schemes
### BER Analysis of D-QR-IC Detection  
D-QR-IC detection uses interference cancellation (SIC) after QR decomposition. The SNR at the \(k\)-th detection layer is:  
\[\gamma_{k}=\frac{|\mathbf{r}_{k,k}|^{2}}{\sigma^{2}+\sum_{l>k}|\mathbf{r}_{k,l }|^{2}\bar{P}_{l}} \tag{48}\]  
The BER at each layer is:  
\[P_{b,k}^{\text{D-QR-IC}}\approx Q\biggl{(}\sqrt{\frac{\gamma_{k}}{2}}\biggr{)} \tag{49}\]  
Thus, the average BER becomes:  
\[P_{b}^{\text{D-QR-IC}}\approx\frac{1}{K}\sum_{k=1}^{K}Q\biggl{(}\sqrt{\frac{| \mathbf{r}_{k,k}|^{2}}{2(\sigma^{2}+\sum_{l>k}|\mathbf{r}_{k,l}|^{2}\bar{P}_{ l})}}\biggr{)} \tag{50}\]  
Under Nakagami-\(m\) fading, this can be expressed as:  
\[P_{b}^{\text{D-QR-IC}}\approx\frac{1}{K\pi}\sum_{k=1}^{K}\int_{0}^{\pi/2} \biggl{(}\frac{m}{m+\frac{\gamma_{k}}{\sin^{2}\theta}}\biggr{)}^{m}d\theta \tag{51}\]
## 1 Introduction
### Hybrid-Based Classification Method  
Network hybrid classifier is defined as a classifier which uses more than one classification method. Port-based, payload-based, statistical-based and hardware-based are the common methods that are used in building hybrid classifiers. Each of the classification methods has some advantages as well as some limitations. The hybrid multistage classifier  
Figure 1: Real traffic and live traffic.  
makes full use of the advantages of each of the partial methods [7]. However, the disadvantage of hybrid classifier is the complexity involved when using more than one stage. This complexity can be evaluated through the classification time versus classification accuracy. In other words, what is the trade-off between complexity and accuracy (which is expected when more than one method is used) than can be achieved?  
Recent researches on network traffic classification focused on using statistical approach such as machine learning algorithm for classifying network traffic. However, the ML classification results (accuracy) becomes low over time as the application behavior changes [1].
## 2 System Principle
### Two-Dimensional Differential Positioning Algorithm for Tunnels with Dual T-Type LCX Cables Layout
 \tag{15}\]  
Because the satellite clock biases of various GNSS systems change over time, the positioning errors caused by these clock biases can be eliminated using differential methods. Additionally, the positioning errors resulting from factors such as the troposphere, ionosphere, and frequency division relay are independent of the baseline distance between the user's receiver and the reference point and can also be eliminated through differential methods. The receiver coordinate calculation formulas are given in Equations (16) and (17):  
\[\overset{\sim}{y}=\frac{1}{2}\left[(\rho_{BDS}^{i}-\rho_{GPS}^{i})-(r_{BDS}^{ i}-r_{GPS}^{i})+C\cdot(A_{1 GPS}.\delta t_{BDS}^{i}+A_{0 GPS}+\tau_{1}) \right]+H \tag{16}\]  
\[\overset{\sim}{x}=\frac{V}{2\mathbb{C}}\left[(\rho_{BDS}^{i}-\rho_{GLO}^{i})-( r_{BDS}^{i}-r_{GLO}^{i})+C\cdot(A_{1 GLO}\delta t_{BDS}^{i}+A_{0 GLO}+\tau_{2})+ \frac{\mathbb{C}}{\mathbb{V}}\cdot L\right] \tag{17}\]  
The differential correction values measured by the reference receiver are given in Equations (18) and (19):  
\[\Delta x=-\frac{V}{2\mathbb{C}}\left[C\cdot(t_{BDS}^{i}-t_{GLO}^{i})+(I_{BDS}^{ i}-I_{GLO}^{i})+(T_{BDS}^{i}-T_{BGLO}^{i})-(\dot{\epsilon_{BDS}^{i}}-\dot{ \epsilon_{GLO}^{i}})\right] \tag{18}\]  
\[\Delta y=-\frac{1}{2}\left[C\cdot(t_{BDS}^{i}-t_{GPS}^{i})+(I_{BDS}^{i}-I_{GPS}^{ i})+(T_{BDS}^{i}-T_{GPS}^{i})-(\dot{\epsilon_{BDS}^{i}}-\dot{\epsilon_{GPS}^{i}})\right] \tag{19}\]In Equation (13), the coefficient matrix \(\mathrm{G}\) for the coordinates \((\mathrm{x},\mathrm{y})\) is given in Equation (20):  
\[G=\left[ \begin{matrix}\mathbb{C}&1\\ -\mathbb{C}&1\\ \mathbb{C}&-1\end{matrix} \right] \tag{20}\]  
The Horizontal Dilution of Precision (HDOP) in the dual T-type LCX cables layout is 0.80653, and its calculation is given in Equation (21):  
\[HDOP=\sqrt{tr{(G^{\mathrm{T}}G)}^{-1}} \tag{21}\]  
In theory, the system using the dual T-type LCX layout achieves high positioning accuracy.
# The Experiments and Stability Analysis of Hypersonic Boundary Layer Transition on a Flat Plate
## 2 Experiment Facility and Model
### Experiment Model  
Figure 2 displays a photograph of the flat-plate model positioned within the wind tunnel at a zero angle of attack. The primary structure of the model is constructed from stainless steel to guarantee adequate stiffness. Positioned centrally within the model is an embedded polyether ether ketone (PEEK) component. PEEK is a high-temperature-resistant, high-strength resin material with excellent insulation performance. By utilizing the insulation performance of PEEK material, the strength of high-speed airflow and wall friction can be well reflected on the surface temperature of the model, making it convenient for monitoring by infrared thermal imagers. In addition, PEEK also has a high infrared emissivity, making the results monitored by the infrared thermal imager highly accurate, which makes it very suitable for the application of IR thermography. [PERSON] et al. [23] used infrared thermal imaging technology to study the surface transition phenomenon of the HIFiRE-5 model made of polyether ether ketone (PEEK) material under Ma = 6 conditions. The experimental results showed that the infrared thermal image can clearly display the high- and low-temperature bands flowing towards the model surface and can capture the transition process. The Mach 5 freestream direction is from left to right. The  
Figure 1: Sketch of NHW wind tunnel.  
infrared camera (FLIR T630 sc, FLIR Systems Inc, American) observes the flat plate from the upper observation hole with a zinc selenide (ZnSe) window. The detailed flow conditions in experiment are shown in Table 1; Ma, P\({}_{0}\), T\({}_{0}\), and Re/m are the freestream Mach number, freestream stagnation pressure, freestream stagnation temperature, and freestream unit Reynolds number, respectively. The unit Reynolds number is changed by adjusting the total pressure of the freestream flow. Three freestream flow conditions with different Reynolds numbers were set up to study the effect of unit Reynolds number on transition Reynolds number.
## 3 Proposed Current Detection Schemes with Reduced Sensors  
### Phase Grouping in EMSRMs  
For an SRM, the minimum absolute value of phase-shift angle, (i.e., absolute value of the phase-shift angle between two adjacent phases) can be derived as  
\[\theta_{\text{ps,min}}=\tau_{\text{r}}/N_{\text{p}} \tag{2}\]  
where \(\tau_{\text{r}}\) denotes the electrical cycle [24]. The phase-shift angle between two arbitrary phases can be given by  
\[\theta_{\text{ps}}=\theta_{\text{ps,min}}Z_{1},\,Z_{1}=\pm 1,\,\pm 2,\,\ldots,\, \pm E(N_{\text{p}}/2) \tag{3}\]  
where \(E\) (-) denotes the floor function.  
Phase number of an EMSRM can be expressed as  
\[N_{\text{p}} =2Z_{2},\,\,Z_{2}\geq 2\text{ and }Z_{2}\text{ is an integer} \tag{4}\]  
Combining (2)-(4), the phase-shift angle can be obtained by  
\[\theta_{\text{ps}}=\tau_{\text{r}}Z_{1}/(2Z_{2}) \tag{5}\]  
When \(Z_{1}=Z_{2}\), the angle equals half of the electrical cycle as shown in  
\[\theta_{\text{ps}}=\tau_{\text{r}}/2,\,\,Z_{2}=Z_{1} \tag{6}\]  
The phase-shift angle between the aligned and unaligned rotor positions of one phase also equals half of the electrical cycle. Therefore, for two phases \(j\) and \(k\) which are furthest from each other in an EMSRM, the aligned and unaligned positions of phase \(j\) exactly coincide with the unaligned and aligned positions of phase \(k\), respectively. The torque direction of phase \(j\) is opposite to that of phase \(k\) as long as the rotor is not at the aligned and unaligned positions of these two phases. Simultaneous conduction of these two phases should be avoided to guarantee that all the phase torques are in the same direction of the motor torque reference, facilitating the use of a single time-multiplexed sensor to sense the currents of these two phases. Thus, we divide the phases in an EMSRM into \(N_{\text{p}}/2\) groups in each of which two phases are furthest from each other and are supposed to be conducted asynchronously.  
Take the four-phase 8/6 SRM for example, all aligned positions of this SRM are shown in Figure 3, from which we can see that phase C is the furthest phase from phase A, and the aligned and unaligned positions of phase C exactly coincide with the unaligned and aligned positions of phase A, respectively. Thus, phases A and C should be contained in a phase group. Similarly, phases B and D should be included in another group.  
Figure 3: Aligned positions of the four-phase 8/6 SRM. (**a**) Phase A. (**b**) Phase B. (**c**) Phase C. (**d**) Phase D.
## 3 Composite Laminates Toughened by Nano-Particles
### One-Dimensional (1D)  
1D nanoparticles have stimulated an increasing interest as reinforcing nanoparticles for the research on toughening in composites. 1D nanoparticles are used in different forms of fibers or tubes such as double-walled CNTs, multi-walled CNTs [86; 97; 98; 99], cup-stacked carbon nanotube (CSCNT) [100]; vertically-aligned CNT (VACNT) forest grown directly on fiber surface [101] or on Si substrate and then transfer-printed onto prepregs [102]; vapor-grown carbon nanofiber (VGCNF) [103; 104]; and halloysite nanotube (HNT) [105; 106].  
Significant increase was reported using 1-D nanoparticles as reinforcement for matrix-dominated mechanical properties such as mode-I and mode-II fracture toughness, albeit with substantially varying degrees [72; 73; 107; 108; 109; 110].  
Most of the studies reported a positive effect on low-velocity impact [108; 109; 111; 112; 113; 114; 115; 116] and ballistic velocity impact energies [75; 117]. However, some of the researchers reported no improvements in the impact and CAI behaviors [118; 119], or even a negative effect was reported by others [76]. These differences were because of the type and content of the nanoparticles and different manufacturing methodologies that were found to be very important factors in impact performance of composite materials. Aligned CNTs offer excellent mechanical toughness improvements for traditional composite laminates, and additionally enable multifunctional capabilities; i.e., to improve the impact performance (reduced damage area and better CAI) [73; 120] and also as a promising damage monitoring technique of the carbon fiber laminated composites [118; 120; 121]. The compression strength and compression-compression fatigue after impact performance was improved [111].  
Figure 12: SEM image of carbon/epoxy laminate modified with silica/rubber nanoparticles (fracture surface of GIIc test specimen). The white arrow indicates the direction of crack propagation [68].  
Figure 13: SEM micrographs of fracture surfaces near crack tips (**a**) pure epoxy, and (**b**) 8 wt % nano-silica [84].  
The reason behind the increase in fracture toughness was linked to the extraordinary high interface area of the 1-D nanoparticles [97] and the bridging mechanism of the 1-D nanoparticles that suppresses the growth of nano-pores, as well as the propagation of cracks that contributes positively to the increase in fracture toughness [67]. Figure 14 shows SEM images of the fracture surfaces of a baseline and a nano-modified specimen at which micro-cracks and hackles, which are both related to microscale matrix failure modes are dominant toughening mechanisms involved with Mode II fracture tests [73].
## 4 Landscape of Immunotherapy for HCC
### Combination of PD-1 and CTLA4 Inhibitors (Dual Checkpoint Blockade)  
Since cancer cells can exploit different checkpoints to evade immune surveillance, the dual blockade of the CTLA4 and PD-1/PD-L1 pathway was tested in order to enhance the immune response against the tumor. This strategy has been adopted in the phase III HIMALAYA trial, that compared the association of the anti-PD-1 durvalumab plus the anti-CTLA4 tremelimumab vs. sorafenibib in the first-line setting. The ICIs combination (STRIDE, single-tremelimumab regular-interval durvalumab) was administered as a single dose of tremelimumab plus durvalumab, followed by a scheduled infusion regimen of durvalumab. The primary outcome of this trial was the superiority in survival for STRIDE vs. sorafenibib, while the secondary outcome was noninferiority of durvalumab alone vs. sorafenibib [9]. The median OS was 16.4 months (95% CI, 14.2-19.6) with STRIDE, 16.6 months (95% CI, 14.1-19.1) with durvalumab, and 13.8 months (95% CI, 12.3-16.1) with sorafenibib. The trial met both the primary and secondary endpoints, since the hazard ratio (HR) for STRIDE vs. sorafenibib was 0.78 (96% CI, 0.65-0.93), and the durvalumab monotherapy was noninferior to sorafenibib (HR 0.90; 96% CI, 0.75-1.03; noninferiority margin 1.08). Grade 3/4 treatment-emergent AEs occurred for 25.8% of patients with STRIDE, 12.9% with durvalumab, and 36.9% with sorafenibib [9].  
Another phase III trial comparing nivolumab (anti-PD-1) plus ipilimumab (anti-CTLA4) vs. sorafenibib (Chackmate9 DW) is currently ongoing. Nevertheless, the FDA already granted accelerated approval for this combination in second-line settings after the high overall response rate (31%) without significant safety issues demonstrated in the phase II study [47].
## 6 Reliability Analysis for Transposition System
### Reliability Optimization  
Based on the rules of reliability and reliability-based sensitivity, the mean values of the gears' positions are regarded as design variables, (\(\mu_{X11}\), \(\mu_{X21}\), \(\mu_{X31}\), \(\mu_{X41}\), \(\mu_{X51}\), \(\mu_{X61}\), \(\mu_{X71}\), \(\mu_{X81}\)). Also, the indices of the mean value reliability-based sensitivity are defined as the optimization object \(H(\textbf{X})\), which can be written as  
\[H(X)=\sqrt{\sum_{i=1}^{8}(\partial R/\partial\mu_{X_{i}1})^{2}} \tag{24}\]  
The constraint condition of the reliability index can be defined by \(R>R_{u}\), \(R_{u}=0.99\). The scope of the design variables can be depicted as  
\[\begin{array}{l}62.17\leq\mu_{X_{1}1}\leq 62.37\\ 197.90\leq\mu_{X_{2}1}\leq 198.10\\ 113.08\leq\mu_{X_{3}1}\leq 113.28\\ 215.90\leq\mu_{X_{4}1}\leq 216.10\\ 163.14\leq\mu_{X_{5}1}\leq 163.34\\ 188.90\leq\mu_{X_{6}1}\leq 189.10\\ 115.90\leq\mu_{X_{7}1}\leq 116.10\\ 105.90\leq\mu_{X_{8}1}\leq 106.10\\ \end{array} \tag{25}\]  
Figure 5: Reliability-based sensitivity for random variables’ mean values.  
Figure 6: Reliability-based sensitivity for random variables’ covariance.  
The constraint conditions of center distances can be depicted as  
\[\left\{\begin{array}{l}\sqrt{\left(\mu_{{X_{1}}1}-\mu_{{X_{1}}1}\right)^{2}+ \left(\mu_{{X_{4}}1}-\mu_{{X_{2}}1}\right)^{2}}\geq a_{1}\\ \sqrt{\left(\mu_{{X_{5}}1}-\mu_{{X_{3}}1}\right)^{2}+\left(\mu_{{X_{6}}1}-\mu_ {{X_{4}}1}\right)^{2}}\geq a_{2}\\ \sqrt{\left(\mu_{{X_{7}}1}-\mu_{{X_{5}}1}\right)^{2}+\left(\mu_{{X_{8}}1}-\mu_ {{X_{6}}1}\right)^{2}}\geq a_{3}\end{array}\right. \tag{26}\]  
in which parameter \(a_{v}=0.5m(z_{v}+z_{v+1})\text{cos}20^{\circ}/\text{cos}\alpha^{\prime}\), \(v=1\), \(2\), \(3\), and \(\alpha^{\prime}\) is the working pressure angle [41]. Based on Table 3, the initial optimization object \(H(\mathbf{X})\) is 0.3693. Considering the constraint conditions and scopes of the parameters, the optimization object \(H(\mathbf{X})\) can be reduced to 4.762 \(\times\) 10\({}^{-10}\). Table 4 shows the parameters after optimization, and the system reliability of the transposition system can be increased from 0.983795 to 0.999998.  
According to the optimization model, the indices of the reliability-based sensitivity can be decreased, and the reliability index can be increased. The effects of the positions' parameters can be reduced, which is beneficial for system performance. Considering the same standard deviation of each parameter, the reliability of the transposition system can also be enhanced in tool holders with the recommended values.
## 2 Characteristics of Designed Silica Microsphere WGMR for Kerr-OFC
A polarization controller (PC2) is placed before the intensity Mach-Zehnder modulator (MZM, Photline (Paris, France), MX-LN40) to reduce the polarization-dependent loss. The high-speed NRZ signal is generated offline using a 2\({}^{15}-\)1 long pseudorandom binary sequence (PRBS). The signal is up-sampled and filtered using a root-raised-cosine (RRC) filter having a roll-off factor of 1. At the next pre-processing stage, frequency domain pre-equalization up to 30 GHz is used to compensate for amplitude-frequency distortions and limited bandwidths (BWs) of a 65 GSa/s electrical arbitrary waveform generator (EAWG, Keysight M9502A, 25 GHz). Please observe (Figure 6) the response of an end-to-end system calibration for the optical back-to-back (OB2B) configuration. Figure 5: Experimental demonstration of the IM/DD optical interconnect relying on the in-house-built silica microsphere WGMR-based Kerr-OFC as a light source generating 400 GHz spaced optical carriers that are used for up to 50 Gbps/\(\lambda\) NRZ-OOK modulated signal transmission over a 2 km long SMF link. The high-speed NRZ encoded signal is loaded to the EAWG, pre-equalized, and transmitted. The generated output electrical signals are amplified by an electrical amplifier (EA-1, 38 GHz, and 29 dB gain) and fed into the S\({}_{1}\)(t) input of the MZM with a null chirp factor and having 40 GHz 3 dB bandwidth, 20 dB extinction ratio, and 9 dB insertion loss. To ensure the best possible BER performance, we adjusted a bias voltage Vb\({}_{1}\) of the MZM to 3.8 V. After MZM, the modulated optical signal is transmitted over a 2 km long SMF link and passed through a variable optical attenuator (VOA) located before a photoreceiver for the power control. The photoreceiver module (PIN, Lab Buddy, DSC10H-39) consists of high optical power 50 GHz InGaAs photodiode with a 3 dB bandwidth of 50 GHz, a sensitivity of +4 dBm at BER of \(10^{-12}\), and responsivity of 0.5 A/W. An optical coupler with a 10/90 coupling ratio and a power meter were used before PIN to monitor the optical power level at the receiver. After optical-to-electrical conversion, the electrical signal is amplified by an electrical amplifier (EA-2, 25 GHz, 16 dB gain). Thereafter, the signal is captured by a digital storage oscilloscope (DSO, Keysight DSAZ334A, 80 GSa/s, 33 GHz). The sampled signal is processed offline using digital signal processing (DSP) consisting of a low-pass filter (LPF), clock recovery, resampling, and linear post-equalization based on a symbol-spaced adaptive decision-feedback equalizer (DFE) and bit error rate (BER) counter. Please note that BER values are obtained using bit-by-bit comparison between the transmitted and received bit sequences.