esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Geometry of Distributor Plates  
Three distributor plates are employed to experiment with the cold model of a bubbling fluidized bed gasifier, as shown in Figure 2. The first is a perforated distributor plate, commonly used as a conventional plate for fluidized bed gasifiers. The second is a 45\({}^{\circ}\) slotted plate, which is usually recommended for the swirling flows for better lateral mixing of particles. The distributor plate's novel design, which combines a perforated plate and a 45\({}^{\circ}\) slotted plate, is introduced here for the first time to study the fluidization behavior and bubble dynamics within the fluidized bed. In addition, this study is carried out to optimize the existing bubbling fluidized bed gasifier designs by varying the geometric and operational parameters to enhance the overall hydrodynamics and consequently improve the working efficiency of the gasifiers.  
Distributor plates designs and general parameters are elaborated in Figure 2 and Table 2 below, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bed Material** & **Galdert Classification** & **Average Particle** & **Bulk Density** & **Particle Density** & **Void Fraction** \\  & & **Diameter (\(\upmu\)m)** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(\%)** \\ \hline Sand 1 & Type B & 250 & 1234 & 2420 & 51 \\ \hline Sand 2 & Type B & 590 & 1330 & 2510 & 53 \\ \hline Sand 3 & Type B & 850 & 1424 & 2590 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of bed material.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Distributor** & **Diameter of the** & **The Thickness of** & **Diameter of** & **Length of** & **Width of** & **Open Area** & **Number of** \\
**Type** & **Plate, D (m)** & **the Plate, t (m)** & **Holes, d\({}_{0}\) (m)** & **Slot, I (m)** & **Slot, W (m)** & **Ratio \(\gamma\) (\%)** & **Holes/Slots** \\ \hline Perforated & 0.125 & 0.008 & 0.005 & - & - & 14.25 & 89 (holes) \\ \hline
45\({}^{\circ}\) degree & 0.125 & 0.008 & - & 0.03 & 0.0065 & 13.99 & 8 (Slots) \\ \hline Hybrid & 0.125 & 0.008 & 0.005 & 0.03 & 0.0065 & 25.55 & 89 + 8 (holes + slots) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric details of distributor plates.
## 4 Case Study
### Results and Discussion
#### 4.3.3 Prediction Effectiveness of KAN Model  
The effectiveness of the KAN prediction model was evaluated by comparing the predicted values against the actual values using multiple performance metrics. Figure 13 presents a side-by-side comparison of the actual and predicted values over the test set. In this study, we aimed to forecast changes in airport operational resilience with a lead time of 6 h. Visually, the model closely follows the trend and fluctuations in the actual data, with minor deviations in a few points. This indicates that the KAN model effectively captures upward and downward trends.  
In addition to the visual inspection, the quantitative evaluation of the prediction performance is presented in Table 5. The key performance metrics include the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the R-squared value (R\({}^{2}\)).  
Figure 12: Changes in daily operational resilience values of routes in western and remote areas.  
Figure 13: Flight resilience with predicted values using KAN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics of Test Set** & **MSE** & **RMSE** & **MAE** & **MAPE** & **R\({}^{2}\)** \\ \hline Prediction result & \(1.55\times 10^{-5}\) & 0.0039 & 0.0031 & 0.0092\% & 99.9565\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation metrics of prediction accuracy.  
The MSE and RMSE values are relatively low, thus indicating that the predictions deviate minimally from the actual values. The MAE of 0.0031 further supports the model's precision, with a small average prediction error. The MAPE of 2.3% demonstrates that the KAN model's prediction errors are relatively negligible, making it highly effective in real-world applications where percentage accuracy is crucial. Most notably, the R\({}^{2}\) value of 99.806% signifies that the KAN model explains almost all of the variance in the actual data, which points to an excellent model fit.  
These metrics confirm that the KAN model performs exceptionally well, providing accurate and reliable predictions. The minor errors and high correlation between the predicted and actual values make it an excellent candidate for forecasting tasks in similar datasets.
## 4 Evaluation and Analysis
### Experimental Results
#### 4.3.2 Evaluation of Feature Extraction Component
All the comparisons can demonstrate that the utilization of attention-based channel weighting and the similarity matrix weighted by relative value relation can help improve the discriminability of descriptors for positive pairs and reduce the differences of values in the similarity matrix for negative pairs. Moreover, the visual information at the boundaries of each patch is integral for feature extraction. Using overlapping convolution kernels in convolutional neural networks to retain contextual information at the boundaries of feature blocks is more effective than directly segmenting the original image.
# Efficient Optimization-Based Trajectory Planning for Truck-Trailer Systems
### Selecting Approaches and Solvers for Optimization Problems  
Solving the optimal control problem is a key aspect of trajectory planning. Due to the nonlinear nature of trajectory planning, linearization can be performed at one or multiple operational points, as demonstrated by the authors of [8; 9; 11; 21]. However, if the problem is to be tackled using nonlinear methods, numerical approaches become a reasonable choice, as they can address a broader range of optimization problems. A widely adopted technique is to transform the optimal control problem into a nonlinear programming problem, then leverage a ready-to-use solver.  
Many researchers have utilized MATLAB's built-in functions such as \(fmincon\) (or \(fminunc\)) [22], which are highly efficient solvers incorporating numerous advancements from prior studies such as [23; 24]. These solvers are particularly suitable for simulation-based tasks or early design phases prior to implementation.  
When transitioning to real-time algorithm deployment, additional requirements for solvers must be considered, particularly compatibility with the target operating system (Linux being a preferred choice), availability on Linux platforms, and integration capability with supervised control systems. Such implementations frequently rely on programming languages such as Python, C/C++, Julia, or FORTRAN. For both trajectory planning and broader nonlinear optimization problems, gradient-free solvers are often essential in practice. This requirement significantly narrows the pool of potential solvers, especially when there is a need to ensure smooth transition from offline simulations to real-time applications.  
In our proposed approach, we select the \(fmincon\) solver due to its efficiency and robust performance. Additionally, we tested the SOLNP solver [25], which is gradient-free and supports multiple interfaces, including MATLAB, Python, and C++. The SOLNP solver demonstrated excellent performance and robustness on our benchmarks, which included nonlinear optimization tasks such as trajectory planning. Real-time operation was successfully tested on a Raspberry Pi 4. However, all tests of the SOLNP solver were conducted as part of preparations for future work, and are beyond the scope of this paper.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 3 System Model  
### Fundamental Assumptions  
In order to have a better illustration of our proposed TRP-MC and conduct the simulation conveniently, we present the following assumptions.  
1. All the sensors keep static after deployment, and once their energy is exhausted, they will be invalid.
2. Sensors can adjust their communication distance within communication range and single hop communication are mainly utilized for data uploading.
3. We define the sojourn points (SPs) as the places where the mobile collector stops for data gathering.
4. A static sink is set at the corner of the sensor field, and during each round, the mobile collector will visit the sink once to upload its collected data.
5. A mobile collector which is modified by an intelligent car is employed for data gathering. It travels through the sensor field and only stops at SPs which are elaborately selected for data gathering.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research Regarding Insertion Loss and Isolation of MEMS Switches in Different Bands
#### 3.1.3 MEMS Switches with a Float Metal Layer on the Dielectric Layer in Different Frequency Bands  
Table 3 summarizes the performance parameters of MEMS switches with a float metal layer on the dielectric layer in different frequency bands. This structure is the equivalent of two capacitors in series in the up-state, leading to the reduction of the capacitance, and thereby, a low insertion loss was achieved in the centimeter Q, and V bands. This design could also solve the problem of reducing the capacitance in the down-state due to the microscopic roughness of the surface of the dielectric layer, leading to high isolation in different bands.  
[PERSON] _et al._ (2017); [PERSON] _et al._ (2017) designed low-insertion-loss capacitive shunt MEMS switches using a float metal layer covering the top of the SiO\({}_{2}\) dielectric layer, and the insertion loss was better than 0.11 dB, and the peak isolation was 55 dB at 10 GHz. [PERSON] et al. [PERSON] _et al._ (2017) achieved a high down-state capacitance and a low up-state capacitance using a torsion beam structure, and the switch achieved a very high capacitive ration of 1175. [PERSON] et al. [PERSON] _et al._ (2017) designed a new type of capacitive shunt MEMS switch with float metal layers for the X and K bands and showed a low insertion loss of 0.01-0.11 dB at 1-25 GHz. A low-actuation voltage was achieved in [PERSON] _et al._ (2017); [PERSON] _et al._ (2017); [PERSON] _et al._ (2017) using two actuation electrodes and cantilevers with a low spring constant. The MEMS switch designed by [PERSON] (2016) achieved a low insertion of 0.1-0.19 dB at 30-75 GHz by covering a metal layer on the dielectric layer and a high isolation of 59 dB at 36 GHz through secondary isolation by using a combination of two bridges in series.
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 3 Gibbs Sampling for MIMO Detection
### Performance of Mixed GS with Multiple Restarts  
The performance of the described mixed Gibbs sampling with restarts will be evaluated here. Setting the number of dimensions \(n=20\), the maximum number of restarts being \(R=15\), the maximum number of iterations \(t_{\max}=160\) and \(c_{\min}=10\), the results are depicted in Figure 2. The initial vector for the first restart was the output of the MMSE filter and for the remaining restarts a random initial vector was used. As can be inferred, the stalling problem was efficiently fixed with the use of MGS-MR. Further, and as was expected, the attained diversity by MGS-MR is similar to the one obtained by the LRA method. Moreover, the BER gain attained by GS-MR over D-ELR-SLB-SV-SIC-MMSE is not negligible. In particular, the randomized algorithm requires around less 3 dB than the studied LR algorithm to achieve a BER of \(10^{-3}\).  
Figure 2: BER performance for both conventional Gibbs sampling and mixed Gibbs sampling with multiple restarts detection algorithms for \(M=N=10\) and a 4-QAM constellation.  
Increasing dimensions to \(n=32\), using a 16-QAM constellation and setting \(t_{\max}=512\), the results are shown in Figure 3. As was expected, in the low SNR regime, the performance is near ML and better than the one achieved by the LRA method. However, for SNR \(\geq 20\) dB, the MCMC detector suffers a degradation in performance. This phenomenon is related with the rather low convergence rate (mixing time) of the underlying Markov chains, since \(a^{2}=1\). Particularly, this choice for \(\alpha\) will cause the Markov chain to take a long time to reach its stationary distribution [22]. To solve this problem, three different approaches are possible:  
1. Try and optimize the mixing ratio \(q_{\infty}\) as a function of the SNR.
2. Find the dependence of \(\alpha\) with the value of the SNR.
3. Increase the maximum number of iterations \(t_{\max}\) and use different, better initial vectors \(\mathbf{x}^{(0)}\), at the cost of increased complexity.  
Option 2 from the above will be evaluated next.
# Laser Cavitation Peening: A Review
## 2 Peening Using a Pulsed Laser
### Laser cavitation Peening System  
In order to illustrate laser cavitation peening systems, Figure 5 shows a schematic diagram of a laser cavitation peening test section. A target was placed in a water-filled glass chamber. A pulsed laser from a laser source was expanded using a concave lens, then focused on the target surface using a convex lens(es) to avoid damage to the glass of the chamber. The standoff distances in air \(s_{a}\) and water \(s_{w}\) were optimized by measuring the peening intensity with \(s_{a}\) and \(s_{w}\). During conventional submerged laser peening, the second harmonic of an Nd:YAG laser, i.e., a wavelength of 532 nm, is used in order to avoid absorption in the water [5]. However, from the point of view of the generation of cavitation bubbles, the fundamental harmonic, i.e., 1064 nm, is more beneficial, since the heat is more concentrated; researchers who have investigated cavitation bubbles using pulsed lasers have used Nd:YAG lasers [38]. A total of 40% of the pulse energy at 1064 nm is lost to obtain 532 nm during wavelength conversion. It was reported that the laser cavitation induced by a 1064 nm pulsed laser introduced compressive residual stress into hard metals such as alloy tool steel, when the standoff distance in water was optimized [39]. Recently, a portable submerged laser peening system using a pulsed laser with a wavelength of 1064 nm was developed [40]. In order to measure the pressure and/or impact caused by the laser ablation and laser cavitation collapse, a submerged shockwave sensor, a hydrophone, and a PVDF sensor were used. As the cavitation impact energy can be obtained using  
Figure 3: Fundamental process: (**a**) laser peening without coating (LPwC); (**b**) laser peening with coating (sacrificial overlay) [5].  
an AE sensor and/or a laser Doppler vibrometer [41], these can be useful for measuring and/or monitoring the peening intensity of laser cavitation peening. During submerged laser peening, peening with and without a sacrificial overlay can be carried out. In both cases, as a number of small particles are produced during laser ablation, feeding with water is required to remove contamination. We discuss water quality later in this paper. Thus, the fundamental harmonic, i.e., a wavelength of 1064 nm, was used to generate laser cavitation during the additional experiment.  
Figure 4: Images of laser peening: (**a**) laser peening underwater (laser cavitation peening); (**b**) laser peening with a water film. Reprinted from [6] with permission from Elsevier, License Number 5532490457730.  
Figure 5: Schematic diagram of test section of laser cavitation peening system.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 3 Corrosion Prediction Model
### Knowledge-Based Model  
The knowledge-based model involves utilizing artificial intelligence techniques to anticipate the advancement of corrosion based on images and videos. The learning process is adopted by the algorithms used for the corrosion images. The knowledge of the algorithm is continuously updated throughout the learning process. The learning process can be categorized into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a learning process used by labeled corrosion images. These images are labeled manually before being trained by the algorithm. Unsupervised learning is a learning process used by unlabeled corrosion images. The algorithm will train itself to explore the unlabeled images and predict the corroded area. Reinforcement learning is a learning process that uses trial and error in an interactive corrosion environment. The algorithm will create a sequence of decisions, based on the learning process, by providing penalties or rewards for each solution concerning corrosion detection. The feedback of each action will enable the algorithm to provide optimal results. Generally, the knowledge-basedmodel will complement the other computer vision and image processing corrosion detection approaches due to its high efficiency, low level of human error, and improved workflows.  
Several studies [15; 16] have used the knowledge-based model for automated sewer inspections. The images and videos were captured with closed-circuit television (CCTV). The CCTV was installed in sewers, and corrosion assessments were conducted from a remote place. The data from the CCTV were analyzed in order to detect the corroded area. However, not all images or videos were clear enough for corrosion detection. Consequently, pre-processing techniques were applied, such as the support vector machine (SVM) [17] for the pixel classification of corroded pipes. CNN [18; 19] can also be used for corrosion detection. This method pre-processed 47,000 sewer pipeline images and managed to classify six types of defects. This method achieved 96.33% accuracy for defective sewer pipelines. However, the classification of imbalanced CCTV data can occur in long-distance sewer pipelines. Defective classifications in a 24.7 km sewer pipeline were investigated as only a 64.8% accuracy was obtained from the CCTV with regard to corrosion detection. However, this method did not use pre-processing for imaging input enhancement. This proved that pre-processing is an important step in computer vision and image processing-based corrosion detection. Similar knowledge-based approaches [20; 21; 22] also used a combination of CCTV and machine learning in their methodology for corrosion detection in pipelines.
## 4 Discussion
### Unconfined Compressive Strength  
Moisture loss causes the water film around the particles to become thinner, leading to greater cohesion and increased soil strength.  
From Figure 13 we obtained a coefficient of determination (R\({}^{2}\)) of 0.8721 for the silt sample, thus providing a model that fits the laboratory data well and provides Equation (5).  
\[y=-0.00008872x^{2}+0.004353x+0.1039 \tag{5}\]  
where \(x\) is time (days).  
Figure 12: Correlation equation based on test results and elapsed time for the saturation of (**a**) silt; (**b**) organic soil.  
Figure 13: Correlation equation based on test results and elapsed time for unconfined compressive strength-silt.  
As mentioned above, the relationship between moisture loss due to sample storage and UCS is not linear. As the storage time increased, the MC decreased, and its resistance started to reach a maximum value and then decreased. The particles became more brittle because the sample was too dry, thus reducing the cohesion.  
Similarly, for the organic soil sample (Figure 14), the data were adjusted to a mathematical model using linear regression, resulting in a concave curve with a coefficient of determination (R\({}^{2}\)) of 0.8935, as shown in Equation (6).  
\[y=-0.00005675x^{2}+0.003711x+0.04116 \tag{6}\]  
where \(x\) is time (days).  
Figure 14 shows that the UCS initially increased with storage period, leading to moisture loss; however, by day 28, its critical point was reached, where the resistance began to decrease again. This could also be caused by the organic matter and minerals present in the soil.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 5 Discussion
### Use of eNIDs at the Server Side
The Client, C, looks up the FQDN for the Server, S. The DNS server, D, serves the domain for S. D generates a new eNID, e1, for access to S from any new client request, i.e., every client will see a different eNID (and therefore different IPv6 addresss) for S in the DNS response from D. D notifies S and the Firewall, F, of the new eNID value, e1. F will then only allow a TCP connection with an IPv6 destination address that includes the value e1 in the lower 64 bits. Even without encryption to protect the DNS server response, this can provide protection against traffic-based DoS attacks from off-path attackers. Note that this mechanism would not be used alone—other functions will be required for a complete DoS-protection facility at the server. This perturbs forged connection requests by making the attack more resource intensive and harder to execute for an attacker. Now, instead of just generating forged packets from any point on the network, at the very least, an attacker would need to use a valid source address in the outgoing packet to be able to receive the DNS response to then make a correct TCP connection request. Also, even if the DNS request and the response are not protected using encryption, this mechanism helps to prevent _off-path_ forged traffic attacks, as the address to use for the server changes for every client. This mechanism would not be used by itself, but would added to the toolbox of possible mechanisms to be used together--for example, it could be used with suitably adapted traffic packet filtering [53] or rate limiting techniques at the firewall. It is best suited to an enterprise scenario or another scenario where F, S, and D are within a trusted administrative domain, as it is clear that F, S, and D must work together and have trusted communication for this system to work securely. Finally, if eNIDs are used at both server and client, then for an attacker inspecting source and destination address values in IP packets, each flow looks like it is from a different client and to a different server.