esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 3 Problem Formulation
### Formalization of the Markov Decision Process
\begin{cases}-\big{(}K_{a}cost_{\text{pat}}(t)+K_{\beta}cost_{\text{smo}}(t )+K_{\lambda}cost_{\text{col}}(t)\big{)}&d(t)\geq d_{g},d_{i}(t)\geq d_{s}\\ K_{a}&d(t)\leq d_{g}\\ -K_{b}&d_{i}(t)\leq d_{s}\end{cases}  \tag{14}\]  
where \(K_{a}\) is a positive gain parameter associated with the distance to the target point, \(K_{\beta}\) is a negative gain parameter for action smoothness, \(K_{\lambda}\) is a negative gain parameter associated with the distance to the nearest obstacle, \(K_{a}\) is the reward term for reaching the target point, and \(K_{b}\) is the penalty term for collision with obstacles. When the vehicle is in a normal driving state, i.e., the distance to the target point \(d(t)\) is greater than the preset distance \(d_{g}\) and the distance to all obstacles \(d_{i}(t)\) is greater than the safety distance \(d_{s}\), the reward function is the negative of the weighted sum of the respective costs. This reward function design encourages the agent to choose efficient, safe, and smooth paths by minimizing the path length, path smoothness, and collision risk. When the vehicle successfully reaches the target point, i.e., \(d(t)\leq d_{g}\), a positive reward \(K_{a}\) is provided to encourage the agent to complete the path-planning task. When the vehicle's distance to any obstacle satisfies \(d_{i}(t)\leq d_{s}\), a negative penalty \(-K_{b}\) is imposed to strongly discourage the agent from selecting actions that lead to collisions, improving the safety of the path. Through this reward function design, the agent can progressively optimize its policy during the learning process, enabling efficient, safe, and smooth path planning in complex dynamic environments.
## 3 Phase I : Proposed Precoding-Based Source to Relay Transmission
### Proposed Integer Forcing Precoder
If this additional power is not available at the transmitter, the precoding may fail. We study this case in detail under outage formulation in Section VI. With a finite power penalty, the proposed IFP can convert the signal obtained at the relay into a reliable linear combination of source messages. The decoder at the relay employs the maximum likelihood (ML) decoding to obtain a linear combination of source messages, as explained in the next subsection.
# 108 m Underwater Wireless Optical Communication Using a 490 nm Blue VECSEL and an AOM
## 4 Results and Discussion
### Measured Performance of UWOC System
The spot at a distance of 24 m had a diameter of 1.1 cm, and the spot at a distance of 48 m had a diameter of 2.21 cm. The angle of divergence of the VECSEL in water was calculated to be 0.457 mrad (0.0262\({}^{\circ}\)). At 108 m, the spot diameter is 6.2 cm, but the laser beam was still focused. This demonstrates the ability of the VECSEL to produce high-quality beams with low divergence angles and high brightness. Observing the eye diagrams in Figure 11d-f, the use of signals at varying distances produced clear eye diagrams. Figure 10: Attenuation coefficient of the used 490 nm blue laser. These findings suggest that the VECSEL exhibits good optical signal stability. Due to the negligible attenuation of the clear water, the optical power received by the receiver was still quite high, despite the distance of 108 m. The VECSEL UWOC system maintained a power of \(-\)5.76 dBm at 108 m. The BER performance of a 50 MHz signal with 64 PPM modulation was measured as being at the same distance. The BER at a time-slot frequency of 50 MHz was 2.7 \(\times\) 10\({}^{-5}\). This is the first reported AOM-based UWOC system with a transmission distance over 100 m. Based on the optical power results being under FEC constraints and the calculated underwater optical power attenuation coefficients, we could also estimate the predicted maximum communication distance for 64 PPM at 50 MHz to be about 180 m. The size of the spot at the distance of 108 m and the clarity of the eye diagram also suggested that VECSEL-based and AOM-based systems demonstrate excellent transmission results in underwater communications. The channel can still be extended by increasing the optical power, while the small divergence angle of VECSELs and the use of acousto-optic modulation ensure high-quality signal transmission over long distances.
## 3 Results and Discussion
### Effects of Shift in Agitation Speed on Production of CMCase  
The effects of the shift in the agitation speed of bioreactors on the cell growth of _E. coli_ KACC 91335P and its production of CMCase were also examined in 7 L bioreactors. The optimal agitation speed of a 7 L bioreactor for the cell growth of _E. coli_ KACC 91335P was 500 rpm, whereas that for the production of CMCase was 416 rpm (17). The agitation speeds of the 7 L bioreactors for experiments were (1) constant at 500 rpm, (2) a shift in the agitation speed from 500 to 416 rpm at 24 h, (3) a shift in the agitation speed from 500 to 416 rpm at 36 h, (4) a shift in the agitation speed from 500 to 416 rpm at 48 h and (5) constant at 416 rpm. The aeration rate of the 7 L bioreactors was fixed at 0.95 vvm, which was the optimal aeration rate for the production of CMCase by _E. coli_ KACC 91335P (16). As shown in Figure 2, the maximal cell growth of _E. coli_ KACC 91335P was obtained at a constant  
Figure 1: Effects of shift in aeration rate on (**a**) cell growth and (**b**) production of carboxymethylcellulose by _E. coli_ KACC 91335P (\(\blacktriangledown\), constant at 0.50 vvm; \(\blacksquare\), shift in aeration rate from 0.50 to 0.95 vvm at 24 h; \(\blacktriangle\), shift in aeration rate from 0.50 to 0.95 vvm at 48 h; and \(\bigcirc\), constant at 0.95 vvm).  
agitation speed of 500 rpm, whereas the highest production of CMCase was obtained with a shift in the agitation speed from 500 to 416 rpm after 36 h, at which the production of CMCase was 874.3 U/mL.
## 4 Experiments
### Influence of Parameters  
Several important parameters will influence the final clustering performance of LSH-GEFR.  
When building the bilayer graph, the threshold \(\delta_{e}\) is used to identify whether two events are related. Therefore, \(\delta_{e}\) determines how many events are connected with a given event \(e_{i}\). Figure 10 shows the event clustering performance when \(\delta_{e}\) changes in a step of 0.05 from 0 to 1. As we can see, when \(\delta_{e}\) is equal to 0.80, the clustering achieves the best performance in terms of the silhouette coefficient evaluation metric. The reason for this is that when \(\delta_{e}\) approaches 0, the event \(e_{i}\) becomes connected with too many other events, resulting in the extended event features containing too many noise features in the environmental feature. On the other hand, when \(\delta_{e}\) approaches 1, the event \(e_{i}\) is influenced by only one or two events, resulting in \(e_{i}\) losing its valuable environmental features. The other relevancy thresholds are set similarly to \(\delta_{e}\). The parameters in the interval of 0 to 1 are tested with a step increase of 0.05, and we choose the best parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **1 (\%)** & **2 (\%)** & **3 (\%)** & **4 (\%)** & **5 (\%)** & **6 (\%)** & **7 (\%)** & **M** & **SD** \\ \hline LDA + TO & 9.33 & 11.69 & 12.32 & 24.47 & 19.19 & 15.28 & 7.72 & 4.41 & 1.43 \\ Story Forest & 7.68 & 10.71 & 11.27 & 19.68 & 20.08 & 17.15 & 13.43 & 4.02 & 1.77 \\ EventKG & 3.12 & 6.67 & 13.54 & 27.33 & 27.38 & 14.57 & 7.39 & 4.01 & 1.72 \\ EventNET & 4.33 & 6.67 & 10.05 & 20.31 & 21.64 & 23.12 & 13.88 & 4.74 & 1.79 \\ LSH-GEFR & 3.35 & 3.51 & 8.23 & 16.41 & 23.65 & 27.59 & 17.26 & 4.88 & 1.65 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Readability score percentages for different methods.  
Figure 10: Event clustering performance with different \(\delta_{e}\).  
The fusion threshold for life stories is determined after experiments. Figure 11 shows the life story fusion performance when the threshold changes in a step of 0.01 from 0.70 to 1. When the threshold is 0.88, the fusion achieves the best performance in terms of the evaluation metric of the silhouette coefficient.  
The density radius \(eps\) of DBSCAN is an important parameter in the DBSCAN algorithm and represents the neighborhood radius when density is defined. Figure 12 shows the event clustering performance when \(eps\) changes in a step of 0.05 from 0 to 1. When \(eps\) is 0.70, the clustering achieves the best performance in terms of the evaluation metric of the silhouette coefficient.  
Figure 11: Life stories fusion performance with different thresholds.  
Figure 12: Event clustering performance with different \(eps\).
## 2 Theory  
When a defect on the surface of a light element is illuminated, it displays various reflection characteristics based on the angles of incidence, zenith, and azimuth. This characteristic can be described using the BRDF [20], which is defined as the ratio of the irradiance \(dL(\theta_{i},\varphi_{i};\theta_{s},\varphi_{s})\) exiting the defect surface in direction \((\theta_{s},\varphi_{s})\) to the irradiance \(dE(\theta_{i},\varphi_{i})\) incident upon the defect surface in direction \((\theta_{i},\varphi_{i})\), i.e.,  
\[BRDF(\theta_{i},\varphi_{i};\theta_{s},\varphi_{s})=\frac{dL(\theta_{i}, \varphi_{i};\theta_{s},\varphi_{s})}{dE(\theta_{i},\varphi_{i})} \tag{1}\]  
where the incident angle is represented by \(\theta_{i}\), the scattering angle by \(\theta_{s}\), the incident azimuth by \(\varphi_{i}\), and the scattering azimuth by \(\varphi_{s}\). A schematic of the BRDF scattering geometry is shown in Figure 1[14].  
The Rayleigh-Rice theory [21] generalized the first-order vector perturbation theory. It is a scattering model based on physical optics and applies to surface scattering cases where the magnitude of the variation in the surface height profile is less than the order of the wavelength of the visible light. The Rayleigh-Rice theory deconstructs the surface profile microrough topography into numerous sinusoidal gratings with different magnitudes, spatial periods, and orientations. It then considers the scattered field of the surface as the height of each of these sinusoidal grating components. To determine the intensity of the scattered field, it is necessary to know the height of each sinusoidal grating component of the surface height profile. The power spectral density \(S_{PSD}(f_{x},f_{y})\) is used, which represents the square of the average height of the surface sinusoidal grating components with spatial frequency \(f_{x},f_{y}\), and is defined as follows:  
\[S_{PSD}\big{(}f_{x},f_{y}\big{)}=\frac{1}{A}\Big{|}\iint_{A}z(x,y)e^{i2\pi(x /f_{x}+yf_{y})}dxdy\Big{|}^{2} \tag{2}\]  
where \(A\) represents the area of the scatterer, while \(z(x,y)\) is the surface height profile function of the scatterer, which represents the deviation of the heights of the points on its surface from the average surface height. Thus, \(S_{PSD}\big{(}f_{x},f_{y}\big{)}\) contains the complete dependency of the scattered field on the surface topography.
# Cyber-Attack Scoring Model Based on the Offensive Cybersecurity Framework
## 4 Cyber-Attacks Evaluation
### Scoring
#### 4.4.1 Cyber-Attack Scoring Result with Cyber Kill Chain
Through these methods, we calculated the scoring result of cyber-attacks with Cyber Kill Chain as listed in Table 9. The result using Cyber Kill Chain shows Poweliks (0.3714), Rozena (0.3429), Duqu 2.0 (0.3429), Kovter (0.3429), Petya (0.4286), Sorebrect (0.2857), WannaCry (0.3714), Magniber (0.2857), Emotet (0.3429), and GandCrab (0.3429) for fileless cyber-attacks. For APT cyber-attacks, it shows APT1 (0.4000), Emissary Panda (0.4857), APT29 (0.4286), SectorJ04 (0.4571), Lazarus Group (0.5143), APT38 (0.4286), Chafer (0.4000), MuddyWater (0.4286). The average score for fileless and APT cyber-attacks is 0.3459, 0.4318, respectively. This shows that APT cyber-attacks use more Cyber Kill Chain techniques than fileless cyber-attacks. Figure 5: Cyber-attack Score example for Lazarus Group. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Cyber-Attacks**} & \multirow{2}{*}{**UOCSM**} & \multicolumn{6}{c}{**Lockheed Martin Cyber Kill Chain Phase**} & \multirow{2}{*}{**Total**} \\ \cline{3-3} \cline{5-10}  & & & **R** & **W** & **D** & **E** & **I** & **C** & **A** \\ \hline
1 & Poweliks & 260.0 & 20 & 40 & 40 & 20 & 40 & 60 & 40 & 0.3714 \\
2 & Rozena & 240.0 & 20 & 40 & 20 & 60 & 40 & 40 & 20 & 0.3429 \\
3 & Duqu 2.0 & 240.0 & - & 40 & 40 & 40 & 40 & 40 & 0.3429 \\
4 & Kovter & 240.0 & 20 & 40 & 40 & 40 & 40 & 20 & 40 & 0.3429 \\
5 & Petya & 300.0 & 20 & 60 & 40 & 60 & 60 & - & 60 & 0.4286 \\
6 & Sorebrect & 200.0 & - & 40 & 20 & 40 & - & 40 & 60 & 0.2857 \\
7 & WannaCry & 260.0 & - & 40 & 40 & 40 & 40 & 40 & 60 & 0.3714 \\
8 & Magniber & 200.0 & - & 40 & 20 & 40 & 40 & - & 60 & 0.2857 \\
9 & Emotet & 240.0 & - & 40 & 40 & 40 & 40 & 40 & 40 & 0.3429 \\
10 & GandCrab & 240.0 & - & 40 & 20 & 40 & 40 & 40 & 60 & 0.3429 \\ \hline
11 & APT1 & 280.0 & 20 & 60 & 40 & 40 & 60 & - & 60 & 0.4000 \\
12 & Emissary & 340.0 & 40 & 40 & 60 & 40 & 60 & 60 & 40 & 0.4857 \\
13 & APT29 & 300.0 & 40 & 20 & 40 & 40 & 60 & 60 & 40 & 0.4286 \\
14 & SectorJ04 & 320.0 & 20 & 40 & 40 & 40 & 60 & 60 & 60 & 0.4571 \\
15 & Lazarus Group & 360.0 & 40 & 60 & 60 & 40 & 60 & 60 & 40 & 0.5143 \\
16 & APT38 & 300.0 & 40 & 40 & 60 & 40 & 40 & 40 & 40 & 0.4286 \\
17 & Chafer & 280.0 & 40 & 40 & 40 & 40 & 40 & 40 & 40 & 0.4000 \\
18 & MuddyWater & 300.0 & 20 & 40 & 40 & 40 & 60 & 60 & 40 & 0.4286 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Scoring result of cyber-attacks using Cyber Kill Chain.
## 4 Experiment Verification
### Performance Degradation Test Data Collection
#### 4.1.2 HI of Intake Filter  
The degradation mechanism of the intake filter was analyzed as follows: Under the action of the pressure difference between the front and rear end of the intake filter, the mixture flows through the filter medium of the intake filter, and the solid particles or liquid in the mixture are retained. The filtered air is passed through the pores of the filter medium, and thus the impurities such as sand and dust mixed in the air are separated. Impurity particles such as sand and dust are captured by the cartridge fibers and accumulate on the surface of the cartridge. With the accumulation of impurity particles, the gaps between the cartridge fibers are gradually blocked, the effective circulation area of the cartridge becomes smaller, the resistance becomes larger, and the intake filter gradually degrades. The resistance of the intake filter is generally expressed in terms of the pressure loss, and the resistance is related to the speed of air flow and the effective circulation area of the cartridge, expressed as:  
\[\Delta p=P_{in}-P_{out}=b(\frac{m_{air}}{S})^{n} \tag{20}\]  
where \(\Delta p\) is pressure loss of the intake filter, \(P_{in}\) is inlet pressure, \(P_{out}\) is outlet pressure, \(S\) is the effective flow area, \(m_{air}\) is air mass flow rate, and \(b\) and \(n\) are constants.  
From the above theoretical analysis of the intake filter degradation mechanism, it is clear that the degradation degree of the intake filter can be assessed by measuring the input and output pressures and calculating the pressure loss, i.e., the health indicator of the intake filter can be expressed as  
\[HI_{jq}=\Delta p \tag{21}\]  
For the raw input and output data of the intake filter obtained in Section 4.1.1, after five-point sliding average filtering of the raw data, the health indicator curve for the intake filter during performance degradation was calculated according to Equations (20) and (21), as shown in Figure 6.  
As can be seen in Figure 6, as the performance degradation time increased, the health indicator (HI) of the intake filter fluctuated but showed an overall trend of gradually becoming larger, which can reflect the performance degradation phenomenon of the intake filter. Specifically, \(HI_{jq}\) remained relatively flat in the beginning phase and started to increase with time. The monotonicity of the inlet filter HI was 0.13, the correlation was 0.9, and the robustness was 0.4, which further proves that the HI of the intake filter has a high lifetime correlation.  
Figure 6: Health indicator curve of intake filter.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 1 Introduction
### Related Works
Similarly, paper [52] presents the results of the FI, CI and ABG models in an indoor environment for LOS and NLOS situations. In addition, it proposes a new model that improves the fit of the FI, CI and ABG models. Likewise, papers [53; 54; 55; 56] present another approach to path loss model design based on the use of machine learning methods. The results of these works have been grouped in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Modulation/Transmission Technology** & **Frequency** & **Model** & **n (Slope)** \\ \hline
[16] & BPSK & 868 MHz & CI & NSC 1.98, WSC 1.44 \\ \hline
[26] & LoRa & 868 MHz & CI & LOS 2.5–2.75 \\ \hline
[28] & LoRa & 920 MHz & CI & LOS 2.19, NLOS 3.87 \\ \hline
[29] & LoRa & 915 MHz & CI & LOS 3.03, NLOS 4.81 \\ \hline
[31] & Zigbee & 2.4 GHz & 802.15.4A & LOS 1.63, NLOS 3.07 \\ \hline
[35] & VNA & 26 GHz & CI & Corridor: LOS 1.61, NLOS 3.80 \\ \hline
[35] & VNA & 26 GHz & CI & Stairwell: LOS 1.68, NLOS 4.18 \\ \hline
[35] & VNA & 38 GHz & CI & Corridor: LOS 1.69, NLOS 3.54 \\ \hline
[35] & VNA & 38 GHz & CI & Stairwell: LOS 2.76, NLOS 3.97 \\ \hline
[36] & mBECS & 28 GHz & CI & LOS 2.16, NLOS 3.81 \\ \hline
[39] & CW & 40 GHz & CI & LOS 1.8, NLOS 2.9 \\ \hline
[39] & CW & 40 GHz & FI & LOS 1.8, NLOS 2.9 \\ \hline
[40] & VSG & 3.5 GHz & FI & LOS 1.55, NLOS 2.96 \\ \hline
[42] & V2X & 5.89 GHz & CI & LOS 1.917 \\ \hline
[43] & CW & 1300 MHz & CI & LOS 1.5–1.8, NLOS 2.4–2.8 \\ \hline
[44] & CW & 1300 MHz & CI & LOS 1.79, NLOS 2.81 \\ \hline
[45] & - & 1300 MHz & CI & LOS 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Path loss measured in different scenarios.
## 3 Feasibility of Applying CCIS in Different SAC-OCDMA Codes  
In SAC systems, utilizing a novel technology with a reliable quality of service (QoS) and fair economic feasibility is an attractive idea. Additionally, in recent years, attention has been paid to extended bandwidth requirements to fulfil all bandwidth-starving applications. As a future technology, CCIS design implementation could be considered feasible. Low-priced broadband sources that are incoherent (i.e., ASE and LEDs) with the incorporation of simple optical filters for wavelength assignment make them applicable for SAC [24; 25]. Similarly, the power output of a CW laser does not change over an averaged period, and large frequency power variations with minimum effect in any class of applications tend to make it an efficient source but a costly option [23]. Considering all the benefits of LED and laser sources, the CCIS design is proposed, which trades off all the advantages of incoherent and coherent sources. Further, to reliably design OCDMA networks, particularly for long-haul communication where non-linearities (XPM, FWM) and dispersion exist, CCIS design feasibility is checked, which is obtained by simultaneously combining both incoherent and coherent sources.  
Table 1 summarizes the feasibility of applying CCIS in SAC for different family codes having different values of cross-correlation (CC) such as Hadamard [8], multi-service (MS) [10], random diagonal (RD) [13], FRS [7], diagonal Eigen value unity (DEU) [9],  
Figure 3: Block diagram of the CCIS network.  
enhanced multi-diagonal (EMD) [18], modified quadratic congruence (MQC) [4], modified frequency hopping (MFH) [4], and zero vectors combinatorial (ZVC) [22].
# Analysis on Influences of Squeeze Film Damper on Vibrations of Rotor System in Aeroengine
###### Abstract  
Squeeze film damper (SFD) is widely used in the vibration suppression of aeroengine rotor systems, but will cause complex motions of the rotor system under specific operating conditions. In this paper, a lumped-mass dynamic model of the high-pressure rotor system in an aeroengine is established, and the nonlinear stiffness and damping formula of SFD are introduced into the above model. The vibration responses of the rotor system under different rotating speeds and with different unbalances are investigated numerically, and the influence of SFD on the rotor system vibration and the change of suppression ability are compared and analyzed. The results show that in the case of high speed, together with a small unbalance, the rotor system will perform a complex vibration or a bistable vibration due to SFD. If the unbalance is properly increased under the same case of high speed, the vibration of the rotor becomes single-harmonic and the bistable vibration disappears. The research results can provide a helpful reference for analyzing complex vibration mechanisms of the rotor system with SFD and achieving an effective vibration suppression through unbalance regulation.  
squeeze film damper (SFD); rotor system; unbalanced vibration; bistable vibration +
Footnote †: journal: Applied  
of Rotor System in Aeroengine
# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 3 The Proposed Method  
In this research, a deep ResNet framework for feature learning and fault diagnosis of rolling bearing is proposed. The structure of the network composed of an input layer, a convolution layer, a max-pooling layer, eight residual blocks, then the following are a GAP and a softmax output layer. The RESNET is the most widely used Convolutional Neural Network (CNN) and has become one of the hotspots in terms of fault diagnosis. However, due to large number of parameters in the full connection layers, the convergence speed is slow during network training. This paper proposed an improved RESNET algorithm for intelligent fault diagnosis of rolling bearings. The complete network structure of the presented ResNet is demonstrated in Figure 4. This method improved the RESNET structure and introduced GAP technology [36] to replace the connection layer part, reducing the amount of training parameters and testing time of the model. The proposed method does not need to perform any manual feature extraction and feature transformation operations for the original data during the entire fault diagnosis process. It only needs to input the original fault data of the rolling bearing into the improved RESNET model, and the fault diagnosis results are automatically output. The \"end-to-end\" algorithm structure has better operability and versatility.  
As demonstrated in Figure 4, the input of this network is the one-dimensional time-domain signal of the rolling bearing fault signal, and the probability distribution of each failure type is the output of the network.  
Residual block is consisted of two convolutional operations, ReLU activation functions, batch normalizations (BNs), and one identity shortcut, as shown in Figure 3b. The parameters of the convolution layer in the residual block are shown in Table 1.  
The size of convolution kernel in the residual block is all 1 \(\times\) 3, the quantity of convolution kernels is 1, and the main difference lies in the stride.