esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 2 Background  
Assume a scenario where an Industrial WSN (IWSN) establishes communication in a feedback loop that compensates for a control variable in a system. The system defines the update time that the control loop must fulfill in order to keep the system stable. Hence, a WSN must ensure that the control variable is delivered within a minimum latency according to these requirements.  
Replicating packets is a technique to ensure higher reliability. This means that each packet is duplicated and sent along two disjoint paths. At the destination, only the first packet is kept, and the copied packets are discarded as they arrive. Packet replication creates redundant data traffic, in order to avoid a single point of failure. In short, this increases reliability with the drawback of increased traffic and energy consumption as more packets traverse the network.
## 6 Conclusions
\begin{tabular}{l l l} \(A\) & Area & m\({}^{2}\) \\ \(A_{\text{b}}\) & Area of heat sink base & m\({}^{2}\) \\ \(C_{\text{P}}\) & Specific heat at constant pressure & J kg\({}^{-1}\) K\({}^{-1}\) \\ \(D\) & Inlet jet hole diameter & m \\ \(\mathbf{F}\) & Volume force & N m\({}^{-3}\) \\ \(g\) & Gravity, 9.8 m s\({}^{-2}\) & m s\({}^{-2}\) \\ \(h\) & Convective heat transfer coefficient & W m\({}^{-2}\) K\({}^{-1}\) \\ \(h_{\text{m}}\) & Modified convective heat transfer coefficient & W m\({}^{-2}\) K\({}^{-1}\) \\ \(\overline{h}\) & Average heat transfer coefficient & W m\({}^{-2}\) K\({}^{-1}\) \\ \(\mathbf{I}\) & Identity matrix & – \\ \(I_{\text{f}}\) & LED forward current & mA \\ \(k\) & Thermal conductivity & W m\({}^{-1}\) K\({}^{-1}\) \\ \(k\) & Turbulent kinetic energy & m\({}^{-2}\) s\({}^{-2}\) \\ \(L_{\text{c}}\) & Characteristic length & M \\ \(Nu\) & Nusselt number & Dimensionless \\ \(p\) & Pressure & Pa \\ \(r_{\text{bp}}\) & Rotation axis base point (present study keeps it zero, constants) & \(-\) \\ \(Q\) & Heat generation per volume, heat dissipation rate & W m\({}^{-3}\) \\ \(q^{\prime\prime}\) & Heat flux & W m\({}^{-2}\) \\ \(Re\) & Reynolds number & Dimensionless \\ \(R\) & Thermal resistance & K W\({}^{-1}\) \\ \(R_{\text{b+jet}}\) & Thermal resistance of heatsink base to inlet air jet temperature & K W\({}^{-1}\) \\ \(R_{\text{f+jet}}\) & Thermal resistance of junction to inlet air jet temperature & K W\({}^{-1}\) \\ \(R_{\text{f+sp}}\) & Thermal resistance of junction to solder point & K W\({}^{-1}\) \\ \(R_{\text{sp-hs}}\) & Thermal resistance of solder point to heatsink & K W\({}^{-1}\) \\ \(R_{\text{sp-b}}\) & Thermal resistance of solder point to heatsink base & K W\({}^{-1}\) \\ \(R_{\text{sp-jet}}\) & Thermal resistance of solder point to inlet air jet temperature & K W\({}^{-1}\) \\ \(R_{\text{sp-trim}}\) & Thermal resistance of solder point to trim temperature & K W\({}^{-1}\) \\ \(T\) & Celsius temperature & \({}^{\circ}\)C \\ \(T\) & Kelvin temperature, \({}^{\circ}\)C + 273.15 \({}^{\circ}\)C & K \\ \(T_{\text{a}}\) & Ambient temperature & \({}^{\circ}\)C \\ \(T_{\text{b}}\) & Heat sink base temperature & \({}^{\circ}\)C \\ \(T_{\text{h}}\) & Housing temperature & \({}^{\circ}\)C \\ \(T_{\text{hs}}\) & Heatsink temperature & \({}^{\circ}\)C \\ \(T_{\text{j}}\) & Junction temperature & \({}^{\circ}\)C \\ \(T_{\text{jet}}\) & Inlet air jet temperature & \({}^{\circ}\)C \\ \(T_{\text{sp}}\) & Solder point temperature & \({}^{\circ}\)C \\ \(T_{\text{trim}}\) & Trim temperature under housing & \({}^{\circ}\)C \\ \(t\) & Time & s \\ \(t^{\text{s}}\) & Frozen time & s \\ \(u\) & Velocity & m s\({}^{-1}\) \\ \(\overline{u}\) & Averaged velocity & m s\({}^{-1}\) \\ \(V_{\text{f}}\) & LED forward voltage & V \\ \(w\) & Angular velocity & rad s\({}^{-1}\) \\ \end{tabular}
# Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection
## 2 Related Work
Another unsupervised approach is the Deep Structured Energy Based Models (DSEBM) algorithm [19], presented by [PERSON] et al., in whose proposed work Energy Based Model EBMs are extended with deep structured EBMs called DSEBMs, in which an energy function is encoded through a deep neural network. The DSEBM algorithm generalizes classical EBMs by building a new architecture applicable to a broader spectrum of applications, such as anomaly detection. [PERSON] et al. created the Memory-Augmented Deep Autoencoder [20] algorithm, which combines a deep autoencoder neural network with a memory module to mitigate a problem encountered using a simple deep autoencoder network. The authors state that due to the high generalization ability of the deep autoencoder network, sometimes even outliers are reconstructed correctly in the decoding phase, causing a low reconstruction error failing to detect outliers. In [20], the Memory-Augmented Deep Autoencoder algorithm is used to detect cyber attacks present in the KDDCUP99 dataset. This paper mainly focuses on an unsupervised approach; however, some interesting techniques use semi-supervised approaches. [PERSON] et al. [21] propose a semi-supervised architecture oriented to intrusion detection in the IoT context, named SS-Deep-ID. The experiments carried out show that SS-Deep-ID obtains better precision, recall, F1-score, and accuracy results than the other supervised and semi-supervised algorithms analyzed. In [22], [PERSON] et al. propose, always within the IoT and Intrusion Detection domain, the first semi-supervised algorithm; i.e., semisupervised hierarchical stacking temporal convolutional network (HS-TCN). The primary focus of this work is to perform the labeling of a large amount of data with a small amount of labeled data. Other interesting approaches are those provided by transfer learning. Still in the area of Intrusion Detection aimed at IoT, [PERSON] et al. [23] propose the MMD-AE technique, which uses two autoencoder-type networks and the maximum mean discrepancy metric, aiming at minimizing the distance between multiple hidden layers between the two networks. [PERSON] et al. [24], on the other hand, propose an approach to the problem based on transforming network data into grayscale images, to which transfer learning is added to increase its adaptivity and overall efficiency. The results show that the approach performs better than the most common algorithms, also showing higher robustness and generalization capability.
## 4 Numerical Simulations
### Case Study 1  
In this group, wind disturbances along \(Ox_{I}\) and \(Oy_{I}\) and the uncertainty of \(d_{s}\) regeared as unmodeled dynamics are considered. The unmodeled dynamics \(d_{s}\) would affect the flight stability of the FWUAV since it can cause unstable poles.  
The simulation results are illustrated below.  
Figures 2 and 3 clearly prove that the proposed approach is superior to the NDI approach. The absolute maximum following errors of the proposed scheme along \(Ox_{I}\) and \(Oy_{I}\) are only 0.02 m and 2 m, respectively, compared with the ones based on the NDI approach, which are up to 0.12 m and 12 m along \(Ox_{I}\) and \(Oy_{I}\), respectively. The path-following errors of the proposed approach are much smaller than those of the NDI. Additionally, the two figures also indicate that the NDI approach has a trend of divergence after 45 s when the unmodeled dynamics are encountered, since the NDI approach does not have any anti-disturbance mechanisms. However, the situations are different in the proposed scheme due to the existence of the DE, which has a strong disturbance estimation capability and superb estimation accuracy.  
Figure 4 shows that, when dealing with wind disturbances, the controllers of the proposed approach generate more effective control inputs than those of the NDI approach to guarantee path-following precision. However, to deal with the unknown unmodeled dynamics, too-frequent deflection for the rudder occurs in the NDI-based closed-loop system, which places a heavy burden on the actuator of the FWUAV. The reason for this  
Figure 2: Path—following effect.  
is that the closed-loop system based on the proposed control scheme has a disturbance rejection mechanism that enables the FWUAV's strong robustness. Figure 5 shows the accurate estimation capability of the DE, which is the fundamental reason for the proposed control approach being superior to the conventional NDI approach.  
Figures 6-8 show the attitude control performance of the FWUAV during flight. In the flight control field, attitude maintenance capability plays a decisive role and determines path-following performance. In Figures 7 and 8, the NDI approach-based path-following control system is incapable of steering the yaw movement when unmodeled dynamics are encountered. It can be seen in Figure 7 that the yaw angle of the NDI approach has frequent fluctuations. In Figure 8, the FWUAV has large changing rates, which reach up to 30 degrees (should be around zero) for the yaw movement. A poor attitude control capability would cause large path-following errors for the FWUAV, which results in unsatisfying control performance in path-following missions. The situations are quite different in the proposed approach, as shown in Figure 6, due to the disturbance rejection function because the unmodeled dynamics can be estimated and compensated successfully.
## 5 Evaluation of Major Design Codes Based on Results of FE Analysis
### Influence of Stiffness Ratio  
We find from Figures 14-16 that the stiffness ratio significantly affects the normalized critical axial force. The critical axial forces according to the Chinese and European codes are generally overestimated when the stiffness ratio is small, regardless of the rise-to-span ratio, arch rib spacing, and \(\gamma\)-value, even exceeding the normalized yield force, while it is relatively close to the results given by FE analysis when the stiffness ratio is large. The flexural stiffness of the arch rib and lateral bracing is considered simultaneously in the  
Figure 16: Accuracy Evaluation of Multinational Codes on Influence of Arch Rib Spacing.  
formulae given in the Chinese code and Eurocode for calculating the critical axial force as the Euler critical force. However, the positive effect of lateral bracing is overestimated. On the other hand, the influence of lateral bracing is not considered in the Japanese code provisions, and the critical axial force according to FE analysis decreases with increasing stiffness ratio. Therefore, the critical axial force obtained using the Japanese code is conservative when the stiffness ratio is small and overestimated when it is large, respectively. The normalized critical axial forces calculated by the Japanese code coincide with FE analysis results. Therefore, in general, the results given by the Japanese code are relatively accurate.
## 3 Processing Method
### Evaluation Metric  
Several evaluation metrics such as accuracy, precision, F-score, etc. have been used to evaluate the performance of the model. The accuracy depicts the ratio of the correctly classified samples to the total number of samples. Precision helps to evaluate how well the model classifies the relevant class with respect to the total number of predicted instances of that class. A similar metric called \"specificity\" is also used.  
\[\text{{Precision }}(P)=\text{{True Positive}}/(\text{{True Positive}}+\text{{False Positives}}) \tag{1}\]  
\[\text{{Specificity}}=\text{{True Negative}}/(\text{{True Negative}}+\text{{ False Positives}}) \tag{2}\]  
Recall (also known as sensitivity) denotes the fraction of correctly predicted relevant instances (class y = 1) in the actual number of relevant instances. This gives us a measure of how accurately the model predicts with respect to the actual instances of that class.  
\[\text{{Recall }}(R)=\text{{True Positive}}/(\text{{True Positive}}+\text{{False Negatives}}) \tag{3}\]  
These two terms, i.e., precision and recall, give a good estimate of the model. The ideal value for both is close to 1. To combine and evaluate these together, we use the metric F-score or F-measure. A high F-score is an indication of a good model.  
\[F-\text{{Score}}=\frac{2.P.R}{(P+R)} \tag{4}\]  
The area under curve (AUC) of the receiver operating characteristics (ROC) curve is a statistical value that denotes the ability of a classifier to perform as its discrimination threshold (probability) is varied. An ideal AUC value is close to 1.  
The Jaccard Index or IoU (Intersection over Union) is a metric is used to measure the similarities or overlap between predicted and actual sample sets A and B, respectively. It is defined as the cardinality or size of the intersection divided by the size of the union of the two sets. A value close to 1 is considered to be ideal, i.e., when both the sets are equal.  
\[J\Pi=\frac{|A\cap\ B|}{|A\cup\ B|} \tag{5}\]
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 6 GNSS Meta-Signal RTK Performance Results
### Non-Nominal Conditions: Constrained Satellite Visibility (Bad GDOP) Scenario
This result is shown in Figure 13, where the most remarkable point was that the convergence of the MLE to \(\text{CRB}_{real/integer}\) was driven by the time-delay estimation precision. A lower \(\text{CRB}_{real}\) (i.e., better time-delay estimation precision) implied a faster convergence to \(\text{CRB}_{real/integer}\) (i.e., the threshold region at a lower SNR). In this case, the threshold was in the range \(\text{SNR}_{\text{out}}=[28-36]\) dB, the best case obtained with E5A + E6 and the worst one by Galileo E5, which provided the best performance under nominal conditions. Figure 12: Non-nominal bad GDOP scenario RTK RMSE (m) and the corresponding CRBs (m) for the different GNSS meta-signals: (**a**) GPS L2C + L5Q, (**b**) Galileo E5B + E6B, (**c**) BeiDou B2 AQ + B3Q, (**d**) Galileo E5A + E6B, (**e**) BeiDou B2 + B3Q and (**f**) Galileo E5 + E6B. Taking into account these convergence results, the performance ones in Figure 12 and the success rate in Figure 11, further confirmed that the best choices were the Galileo E5 + E6 and BeiDou B2 + B3 combinations.
## 4 Validation of the Proposed Approach
### Example 3  
In this example, we investigate a two-dimensional inverse heat conduction problem in a peanut-shaped simply connected domain, as depicted in Figure 14. To evaluate the accuracy of the MSMM, we compare the computed results of the proposed method and those of the MFS used with the ECSHA [10]. The two-dimensional object boundary is described by the following equation:  
\[\Gamma=\Big{\{}(x,y)\Big{|}x=\rho cos\theta,y=\rho sin\theta\Big{\}},\quad\rho= \sqrt{cos(2\theta)+\sqrt{1.1-sin^{2}(2\theta)}},\quad 0\leq\theta\leq 2\pi \tag{22}\]  
where the radial coordinate is denoted by \(\rho\) and the angular coordinate is denoted by \(\theta\), respectively. The analytical solution is given by:  
\[T=e^{x}cosy. \tag{23}\]  
In this study, the parameter \(N_{q}\) denotes the number of terms of the non-singular basis functions and is set as 10. Figure 14 shows the distribution of the collocation points on the domain boundary. In total, 100 boundary and source points are uniformly placed on the domain boundary. Two scenarios are considered in this example. Figure 15a depicts the temperature distribution of the exact solution. For the first scenario, the boundary data are imposed on the \(\theta\) values in the range of \(0\leq\theta\leq 3/2\pi\) with noise levels of \(s=0\) and \(s=5\). We collocate 200 inner points within the domain boundary. The temperature distributions are illustrated in Figure 15c,e. The MFS has been used to obtain the numerical results, as depicted in Figure 15b,d. For the second scenario, the boundary data are imposed on the \(\theta\) values in the range of \(0\leq\theta\leq\pi\) with noise levels of \(s=0\) and \(s=5\). We collocate 200 points inside the domain to obtain the temperature field of the problem, as shown in Figure 15g,i. The MFS has been also used to obtain the numerical results, as depicted in Figure 15f,h. The MAE by assigning different levels of noise for example 3 is depicted in Table 1. To show the accuracy of the MSMM, we consider that the boundary conditions are polluted by random noise where the noise level is \(s=5\). It is found that the numerical solution of the temperature field can be approximated in a stable way, and the accuracy of the numerical solution decreases with the increasing noise level. The computed results demonstrate that the accuracy of the proposed method can reach to the order of \(10^{-2}\) while \(s=5\). The accuracy level of the MSMM is discovered to be higher than that of the MFS used with the ECSHA when \(s=5\).
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 4 Experiments and Analysis
### Ablation Study
#### 4.6.1 Ablation of the Structure  
In order to verify the effectiveness of the double-branch design, the double-branch structure is split, the single-branch model is used for training, and the data are compared with the double-branch training results. The experimental results are shown in Table 3. The experimental results show that the PSNR of DPDD dataset and RealDOF dataset are reduced by 0.4 dB and 0.44 dB by removing the CNN branch and keeping only the Mamba branch. In contrast, when only the CNN branch is retained for full-channel data processing, the PSNR and SSIM values on both datasets are significantly reduced. This shows that a single branch cannot fully meet the needs of defocus deblurring task by relying only on local or global feature capture, and the joint modeling of local and global features has important practical value for effectively restoring defocus images. Therefore, the parallel fusion feature extraction structure not only better preserves and restores image details but also achieves higher-quality deblurring results, effectively validating the effectiveness of the dual-branch design.
# One-Dimensional Nanostructure Engineering of Conducting Polymers for Thermoelectric Applications
## 3 Strategies to form 1D Conducting Polymer Nanocomposite Thermoelectric Materials
### Incorporation of 1D Conducting Polymer into Polymer Matrix by Physical Mixing  
Besides organic-inorganic hybrid materials, 1D nanostructured conducting polymers can also be incorporated into polymer matrix to generate polymer/polymer composites. Recently, [PERSON] et al. integrated the in situ polymerized PEDOT nanowires into PSS-doped and tosylate-doped PEDOT hosts via physical mixing to afford a novel free-standing composite thermoelectric films [104]. As revealed in the AFM image, the PEDOT nanowires are homogenously dispersed in the polymer matrix (Figure 15a). The best thermoelectric performance was achieved with addition of only 0.2 wt% PEDOT nanowires. The power factor of the hybrids with tosylate-doped PEDOT host can reach a high of 446.6 uW m\({}^{-1}\) K\({}^{-2}\) and the corresponding figure of merit can reach 0.44 at room temperature. Furthermore, these free-standing PEDOT nanowire/PEDOT composite films were employed as p-type legs for fabrication of a thermoelectric power generator, which delivered a normalized power output of \(\sim\)0.5 mW m\({}^{-2}\) at \(\Delta\)T = 10.1 K. These promising outcomes demonstrate the effectiveness of this new strategy for the design and preparation of high performance organic thermoelectric materials.  
Figure 14: (**a**) Schematic of the synthesis procedure of PPy/rGO hybrid film. (**b**) Cross-sectional SEM image of the PPy/rGO hybrid film with a mass ratio of PPy:GO 1:0.6. (**c**) Schematic illustration of the preparation process for the PPy-nanowire/SWCNT nanocomposite free-standing films by solution mixing and subsequent vacuum filtration. (**d**) SEM image of the S/S-PPy nanowire/SWCNT composite films with SWCNT:PPy-nanowire mass ratio of 60 wt%. The arrows in blue point to the PPy nanowires, while the red arrows point toward the SWCNTs. Readapted with permission from Elsevier Ltd. [102; 103].
# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
## 2 Methodology
### Outer Loop: Nonlinear Vehicle Model  
Figure 3 shows the illustration of the 7-degrees-of-freedom (DOF) nonlinear vehicle system model that is utilized in this study. The vehicle model consists of 3-DOF to represent the vehicle in the lateral, longitudinal and yaw motion with 1-DOF contributing to the rotational motion of each wheel.  
Based on Figure 3, the governing equations of the model are expressed as follows [26]:  
\[m\Big{(}\dot{v}_{y}+v_{x}\dot{\varphi}\Big{)}=\Big{(}F_{yFL}+F_{yFR}\Big{)}cos \delta+(F_{xFL}+F_{xFR})sin\delta+F_{yRL}+F_{yRR} \tag{1}\]  
\[m\Big{(}\dot{v}_{x}-v_{y}\dot{\varphi}\Big{)}=(F_{xFL}+F_{xFR})cos\delta-(F_{yFL} +F_{yFR})sin\delta+F_{xRL}+F_{xRR} \tag{2}\]  
\[L_{x}\ddot{\varphi}=L_{x}\Big{(}(F_{yFL}+F_{yFR})cos\delta+(F_{xFL}+F_{xRR})sin \delta\Big{)}-L_{x}\Big{(}F_{yRL}+F_{yRR}\Big{)}+\frac{T}{2}((-F_{xFL}+F_{xFR}) cos\delta+\big{(}F_{yFL}-F_{yFR}\big{)}sin\delta-F_{xRL}+F_{xRR} \tag{3}\]  
where \(v_{y}\) and \(v_{x}\) are the lateral and longitudinal velocities and \(\varphi\) is the yaw rate. \(F_{xi}\) and \(F_{y}\) (\(i=FR,FL,RR,RL\)) are the wheel's longitudinal and lateral forces.  
The vehicle model consisted of quasi-static load transfer to generate normal force generations. The normal load equations are given by:  
\[F_{xFL,FR}=mg\frac{L_{R}}{2L}-\frac{ma_{x}h}{2L}\mp\frac{ma_{y}h}{2T} \tag{4}\]  
\[F_{zRL,RR}=mg\frac{l_{f}}{2L}+\frac{ma_{x}h}{2L}\mp\frac{ma_{y}h}{2T} \tag{5}\]  
where \(g\) is the gravitational acceleration, \(h\) is the centre of gravity height, \(a_{x}\) is the longitudinal acceleration, \(a_{y}\) is the lateral acceleration and \(T\) is the track width.  
The vehicle system used nonlinear wheel model whereas the equations can be referred in [27]. Table 1 tabulates the vehicle model parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Symbol** & **Definition** & **Value** & **Unit** \\ \hline \(v_{y_{y}}\) & \(v_{x}\) & Lateral and longitudinal velocities & 30 & km/h \\ \(T\) & Track width & 1.53 & m \\ \(L_{F}\), \(L_{R}\) & Front and rear wheel distance to COG & 1.26 \& 1.9 & m \\ \(m\) & Vehicle mass & 2023 & kg \\ \(l_{z}\) & Yaw inertia & 6286 & kg\(\cdot\)m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: Parameter of vehicle model.  
Figure 3: Vehicle model.