esa-sceva/satcom-chunk-collection huggingface dataset:
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 8 Simulation Results and Discussion  
### Simulation Setup  
Our experimental evaluations simulate the behavior of a swarm of 30 to 50 low SWaP fixed-wing UAVs (e.g., [40,41]) monitoring a 6 km \(\times\) 6 km area in which fixed communication infrastructure, such as a cellular network, is not available. The aerial BS is located in the bottom center of the map, and the UAVs are launched from the vicinity of the BS. Each UAV is equipped with GPS and has a transmission range of 1 km (for example [12,29,42]). We assume a multihop topology with free space propagation (for example [42]). For simplicity, UAVs are assumed to be a point mass and their mobility is limited to the X-Y plane flying at a constant altitude (typically 200 m to 1 km above the ground [41]). UAVs avoid collisions by altering trajectories [43]. To facilitate the representation of the pheromone map, waypoints, and coverage statistics, the area is divided into grid cells of 100 m \(\times\) 100 m each, in accordance with other literature [3,7,29,44]. A UAV scans the cell in which it currently resides and deposits a repel pheromone of magnitude 1. We used pheromone evaporation rates of \(\lambda\) and diffusion rates of \(\psi\) 0.006 each. Table 1 shows the simulation parameters.  
We evaluated monitoring single- and multi-target settings in various locations; see Figure 7. The UAVs use the BS-CAP mobility model (\(\beta=1.5\)) to calculate their flight trajectories. All UAVs start out from the region near the base station. If a UAV discovers a target, it becomes a \"target UAV\" and continuously monitors the target by circling around it. Meanwhile, other UAVs continue to search for new targets or events. The target UAV establishes a route and transmits data packets continuously to the BS.  
In our simulations, we take the channel rate to be 11 Mbps and the UAV transmission range to 1 km. We consider a line-of-sight communication and ignore channel fading and noise. The packet size and TTL values are 1.5 kB and 3 s, respectively. We consider a distributed TDMA MAC protocol [45] and a large MAC queue size. Packets with the smallest time-to-expiry in the queue are pushed to the head of the line.  
In order to study the network behavior under traffic, we first simulate the UAV swarm for 1000 s, after which time all the targets have been found. Then, we evaluate the throughput, routing, and coverage performance for 2000 s (i.e., from 1000 to 3000 s) as the swarm monitors the targets and continues to search. We report performance metrics averaged over 30 simulation runs.  
Low SWaP UAVs are prone to failure due to mechanical failure or energy depletion. To understand the impact of these issues on our routing scheme, we study the method's performance when a fraction of nodes randomly fail during the simulation time. In particular, we assign a fixed fraction of nodes (20% or 30%) a failure time during the simulation, with the time selected uniformly between 1000 and 3000 s. This simulates nodes failing progressively over the course of the deployment.
## 5 Experiments
### Results Analysis and Validation  
To validate the results of the proposed Spark-based parallel DMAS algorithm, the results are compared in terms of processing speed with both sequential and Python multiprocessing library. These three versions of algorithm were deployed on both a standalone PC and on Eddie and were run for three times, and the average wall time was noted. The computational results for sequential, Pyspark-based, and Python multiprocessing versions on both the standalone PC and on Eddie can be found in Figure 5. It can be observed that the Pyspark version is 34.12 and 43.2 times faster than sequential implementation of DMAS on a standalone PC and on Eddie, respectively. The Pyspark version also outperforms the Python multiprocessing library and is 4.62 times faster on the standalone PC and 6.72 times faster on Eddie. It can be observed that the Python multiprocessing library also improves the speed significantly compared to the sequential version; however, Pyspark achieves better speed due to in-memory processing.  
On the other hand, the cluster mode significantly improves the speed; however, there is a slight difference in performance due to data retrieval from the shared memory.  
A comparison with the MIR algorithm, based on DAS, proposed in our previous work [34], is also performed to further validate these results. The results can be found in Figure 6. It can be observed that the parallel DMAS takes sightly longer due to \(M^{2}\) multiplications. This comparison aims to determine the effectiveness of using the Spark framework for health big data processing such as medical image reconstruction.  
It can also be observed that the Spark MapReduce framework significantly improves the efficiency of these imaging algorithms. One of the downsides of the proposed algorithm is that it retrieves the data into a memory disk on every cycle to calculate pixel value, which results in considerable disk input/output. A potential solution is to copy the input data and algorithm to the computational nodes.  
Figure 4: Experimental results for (**a**) lamb brain phantom using (**b**) DMAS and (**c**) DAS algorithm. It can be noted that DMAS produce accurate results compared to DAS.
# Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter
## 1 Introduction
The Split Hopkinson Pressure Bar (SHPB) is a standard laboratory experiment where the tensile strength of geologic materials, measured at high rates of strain, is obtained. It is worth noting that the simulation of the SHPB experiment is very demanding for the numerical method being used, mainly in the post-peak portion of the stress-time curve. The correct representation of damage after the material has reached its peak poses many numerical challenges, for example with the proper accounting of the energy being dissipated. In this work, it will be demonstrated that via this assimilation process, the Combined Finite Discrete Element Method is able to simulate the post-peak behavior of granite at high strain rates. Furthermore, the calibrated properties obtained from this assimilation exercise can be used to populate more complicated models, such as when trying to simulate underground mining explosions, or dynamic earthquake rupture problems. This paper presents the procedure to obtain such calibrated strength parameter sets, but the application to other types of problems is outside its scope. This work presents the implementation of the ensemble Kalman filter to estimate key model parameters of a dynamic Combined Finite Discrete Element model, which simulates the evolution of fractures and cracks in different geomaterials. Specifically, the ensemble Kalman filter (EnKF) is applied to the Hybrid Optimization Software Suite (HOSS) to better simulate a set of laboratory experiments. The main objective is to estimate a set of key strength parameters for the model which have the most influence on the material response to the imparted load. For this, the FDEM numerical results are contrasted against observations obtained for a SHPB experiment on granite samples. The paper is organized as follows: Section 2 provides a brief introduction of the HOSS model and the parameters to be estimated; Section 3 presents the SHPB experiment description, as well as the HOSS model setup to simulate an SHPB experiment; Section 4 provides a brief overview of the ensemble Kalman filter and the assimilation algorithm,and the results of the assimilation are presented in Section 5; and Section 6 provides the conclusions and discussion of the results.
## 2 Principles and Methodologies
### OSP Methods
#### 2.3.1 Intelligence Algorithms  
The artificial hummingbird algorithm (AHA) is a novel metaheuristic optimization algorithm proposed by [PERSON] et al. in 2021 [49]. At present, the commonly used methods for initializing populations are tent chaotic mapping and logistic chaotic mapping. The uneven distribution of traditional logistic chaotic mapping has an impact on the convergence speed and accuracy of the algorithm. Although tent mapping has a more uniform distribution, it has unstable periods and is prone to getting stuck in fixed points. Circle mapping is relatively stable and has a distribution uniformity comparable to tent. Specific evidence can be found in reference [50]. This paper utilizes an improved circle chaotic mapping for initializing the population to enhance the global search capability of the initial population. The improved circle mapping addresses the issues of uneven distribution and unstable periodicity that exist in traditional logistic and tent chaotic mapping. The specific formula is as follows:  
\[X_{i+1}=\text{mod}\left[3.14X_{i}+0.6-\left(\frac{0.65}{3.14\pi}\right)\sin(3.14 \pi X_{i}),1\right] \tag{14}\]  
where \(X_{i+1}\) represents the \((i+1)\)-th position, the \(X_{i}\) position is known, the \((i+1)\)-th position is obtained by the remainder of its operation, and mod is the remainder function.  
Next, during the foraging process, _Levy_ flight is incorporated to expand the search space [51], guiding foraging or territory foraging and updating the food source positions of the artificial hummingbirds to avoid premature convergence. The position update formula is given by:  
\[x_{i}(t+1)=\begin{cases}x_{i}(t)+\alpha\oplus Levy(\lambda)&f(x_{i}(t))\leq f (v_{i}(t+1))\\ v_{i}(t+1)&f(x_{i}(t))>f(v_{i}(t+1))\end{cases} \tag{15}\]  
where \(Levy(\lambda)\) represents the random search path, which satisfies:  
\[Levy\,\sim u=t^{-\lambda},\,1<\lambda\leq 3 \tag{16}\]  
The step length follows a _Levy_ distribution, and the step length \(s\) is calculated as:  
\[s=\frac{\mu}{|v|^{1/\beta}} \tag{17}\]  
where \(\mu\) and \(v\) follow a normal distribution, defined as:  
\[\mu\,\sim N(0,\sigma_{\mu}^{2}),v\,\sim N(0,\sigma_{v}^{2})\]  
\[\sigma_{\mu}=\frac{(1+\beta)(\sin\frac{\pi\beta}{2})}{\frac{1+\beta}{2}\beta^{ 2}\frac{\beta-1}{2}},\sigma_{v}=1 \tag{18}\]  
where \(\beta\) is typically a constant, often set to 1.5.  
Based on the above description, the flowchart of IAHA is illustrated in Figure 1.  
In this paper, intelligent algorithms such as AHA [49], IAHA, the particle swarm optimization algorithm (PSO) [52], the improved particle swarm optimization algorithm (IPSO) [53], the sparrow search algorithm (SSA) [54], and the improved sparrow search algorithm (ISSAMS) [50] are used for OSP on the arch dam. By comparing and analyzing  
Figure 1: Flow chart of IAHA.  
the performance of various algorithms and the effectiveness of OSP, the most suitable intelligent algorithm for the fitness function used in this paper is selected.
## 2 System Model  
The proposed model is valid for a dispersive, stationary, memoryless, and ergodic channel with independent and identically distributed (i.i.d.) intensity fading statistics. In that case, the electrical signal, received, \(r\), can be expressed as follows [20; 32; 33]:  
\[r=xs+\widetilde{n}=x\eta l+\widetilde{n}, \tag{1}\]  
where \(x\) modulates the signal bit, as \"0\" or \"1\", \(s\) denotes the instantaneous beam intensity, \(\eta\) is the effective photo-current conversion ratio of the receiver, \(I\) represents the normalized signal irradiance, and \(\widetilde{n}\) is the additive white Gaussian noise (AWGN), of zero mean value and variance equal to \(N_{0}/2\), with \(N_{0}\) being the noise power density [34].
## 2 Materials and Methods
### Test Methods  
#### 2.4.1 WRC Tests  
After soil sample preparation, the compacted clay samples were placed in aluminum boxes. The opening percentage of the aluminum box was greater than 85%. The opening percentage is the ratio of hole area to the total lid area. Studies [47] show that PAM solutions can maintain at least half their original viscosity for more than 8 years at 100 \({}^{\circ}\)C and for approximately 2 years at 120 \({}^{\circ}\)C. Its backbone can remain stable at high temperatures. So, the soil samples were dried in an oven with a temperature of 100 \({}^{\circ}\)C in order to save time. The quality changes in the samples were recorded every 3.5 h, and Equations (1) and (2) were used to calculate the moisture content and water loss rates after drying.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Testing Parameters** & **Values** \\ \hline Polymer type & Zwitterionic polyacrylamide (ZP) \\ Molecular weight & 12 million \\ Admixture amount (\%) & 0, 0.2, 0.4, 0.6, 0.8, 1.0 \\ Drying time (h) & 0, 3.5, 7, 10.5, 14, 17.5 \\ Testing program & Moisture content, water loss, gas permeability, gas diffusion, liquid limit \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing program and parameters of this study.  
The water loss rates of the clay samples after drying were calculated as follows:  
\[W_{\mathrm{s}}=\frac{\mathrm{m}_{0}\mathrm{-}\mathrm{m}}{\mathrm{m}_{\mathrm{s}} \times 25.6\%}\mathrm{\times}100\% \tag{1}\]  
where w is the moisture content; m\({}_{\mathrm{s}}\) is the sum of the mass of the clay and polyacrylamide (g); m\({}_{0}\) is the mass of a compacted clay sample before being baked (g); m\({}_{\mathrm{t}}\) is the mass of a compacted clay sample after drying for time t (g), and W\({}_{\mathrm{S}}\) is the water loss rate (%).
## 3 Results and Discussion
### Limitation and Improvements  
This article employs ANSYS for fluid-solid coupling analysis, utilizing both VOF and FEM models. While ANSYS is robust software, it does have its limitations, such as demanding large computational resources and cumbersome grid generation. Consequently, to enhance computational efficiency, some simplifications are applied to the model in this study. The VOF model may face challenges with numerical dissipation and uncertainties in interface thickness and shape. The simplified FEM model may also bring inaccurate results.  
Simultaneously, the subzero temperatures associated with LNG often induce freezing in the outer layers of the tank, thereby altering the dynamic characteristics of the soil layers, and further complicating the computational challenges. Consequently, with respect to future research, it is imperative to account for the influence of temperature fields and groundwater seepage fields to allow for a thermal-fluid-structure coupling analysis.
## 2 Related Works
### Enhancement of Under Water Images  
In the realm of traditional methods, [PERSON] et al. [28] devised a piecewise linear function for histogram equalization, resulting in images with more details in the RGB channels. The CLAHE color model [29] employed a mixed contrast adaptive histogram equalization approach in both RGB and HSV color spaces, effectively enhancing underwater image quality. However, this method sometimes led to over-enhancement or under-enhancement issues. [PERSON] et al. [30] introduced a novel underwater image enhancement algorithm based on adaptive feedback and the Retinex algorithm, enhancing color saturation, richness, local contrast, and clarity. Nevertheless, it suffered from the problem of post-enhancement blurring. [PERSON] et al. [31] proposed using the attenuation differences between three color channels underwater to estimate scene depth and subsequently perform image enhancement. [PERSON] et al. [32] introduced an approach based on observing the absorption rates of the R channel in a significant number of underwater images to restore a high-quality image using the underwater dark channel prior (UDCP). However, this method often requires a substantial number of physical parameters and underwater optical characteristics, making its generalizability limited.  
[PERSON] et al. [33] introduced an end-to-end underwater image enhancement strategy, achieving favorable results by optimizing a combination of MSE and SSIM loss functions. [PERSON] et al. [34] devised a multi-term loss function and proposed a weakly supervised color transfer method to rectify color, attaining state-of-the-art (SOTA) performance. Their approach successfully addressed the weakening of model generalization caused by the disregard of wavelength-dependent attenuation or assumptions about specific spectral profiles by most algorithms and models. [PERSON] [35] employed Cycle-GAN as an image degradation process to train a paired dataset and utilized the Pix2 Pix model to enhance underwater image quality. [PERSON] et al.'s MLFeGAN [36] utilized global features to enhance local features at each scale for color correction and image detail preservation. While MLFeGAN exhibited some enhancement in synthesized underwater images, its effectiveness was limited when dealing with severely degraded images. [PERSON] et al. [37] proposed a light model named Shallow-UWNet for underwater image enhancement, which achieved enhancement performance comparable to the then SOTA model while employing fewer parameters.
# Challenges of Hydrodynamic Cavitation of Organic Wastes
## 2 Materials and Methods
### Analytical Methods  
In the present study, to estimate the changes that occurred within HC, the characteristics of substrates before and after cavitation was also analysed. In all cases, the analogous parameters were examined: total chemical oxygen demand (COD), soluble chemical oxygen demand (SCOD), biochemical oxygen demand (BOD\({}_{5}\)), total solids (TS), volatile solids (VS), volatile fatty acids (VFA), alkalinity (ALK), as well as pH. Moreover, the composition of the applied materials was determined after each delivery. In this case, total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH\({}_{4}\)+-N), as well as ortho-phosphate phosphorus (PO\({}_{4}\)\({}^{3-}\)-P) were additionally estimated. The SCOD, VFA, ALK, NH\({}_{4}\)\({}^{+}\)-N and PO\({}_{4}\)\({}^{3-}\)-P were analysed in the supernatant obtained by filtering the sample through a 0.45 \(\mu\)L pore-size filter. Most of the analyses were performed using a Hach Lange UV-VIS DR 3900 (Hach, Loveland, CO, USA) and standard cuvette tests appropriate for the given parameter. In turn, the TS and VS contents were determined using the Standard Methods for the Examination of Water and Wastewater [23] (APHA, 2005). The pH values were measured with an electronic pH meter CP-501 (ELMETRON, Poland). TOC was determined using a Shimadzu TOC-5050A total organic carbon analyser. Additionally, in LB the following fibre fractions were analysed: cellulose (C), hemicellulose (HC), lignin (L), pectic polysaccharides, phenolic compounds, proteins and monosaccharides (analysed totally, marked as PPPM), monosaccharides (MS), and proteins (P). They were established by means of a modified [PERSON] method [24]. This method involves the application of two detergents: neutral (sodium dodecyl sulfate, EDTA, pH 7.0) and acidic (cetyltrimethyl ammonium bromide in 1 N H\({}_{2}\)SO\({}_{4}\)).  
BI was also investigated and was established as the BOD\({}_{5}\)/COD ratio. The cavitation number was calculated using the Equation (1):  
\[\text{Cv}=(\text{p2-pv})/(0.5\text{v}_{\text{o}}{}^{2}), \tag{1}\]  
where p2 is the fully recovered downstream pressure, pv is the vapor pressure of the liquid, \(\varrho\) is the liquid density, and v\({}_{\text{o}}\) is the orifice velocity. The applied orifice plate was characterized by a total area of holes of 7.0686 mm\({}^{2}\). The piezoelectric pressure gauges (Keller  
Figure 2: Scheme of laboratory installation 1—circulation tank, 2—pump, 3—electromagnetic flow meter, 4—nanometer, 5—cavitation reactor, 6—valve, 7—piezoelectric pressure gauges, 8—computer unit.  
PR-33X, resolution of 1 mbar) were used to measure the fully recovered down-stream pressure (p2). This sensor was located in the expansion zone of the cavitation inducer.
## 4 Results and Discussion
### Ablation Study Results of Transformer Models
#### 4.1.3 Ablation Study of DeBERTa  
The analysis of different architectural components of the DeBERTa model is given in Tables 6 and 7. Based on the results given in Tables 6 and 7, the feedforward network emerges as the most critical component in the DeBERTa architecture for FND, with its removal causing the highest average performance drop of 0.0453 and a standard deviation of 0.0565 in the datasets. In contrast, the relative positional encoding appears to be the least impactful, with an average performance gain of 0.0075 upon its removal, suggesting that DeBERTa's core mechanisms are robust even without this feature. Evaluating dataset-specific outcomes, the best-performing configuration for both the FakeNewsNet and ISOT datasets is the unmodified baseline model, achieving accuracies of 0.5250 and 0.9997, respectively. However, in the LIAR dataset, the highest accuracy is observed when the dropout is removed (0.6059), indicating that regularization may slightly hinder performance under certain data conditions. In general, the average baseline precision for all datasets is 0.7001 with a standard deviation of 0.2128, reflecting strong performance and moderate variability, while highlighting the central importance of the feedforward layer in the DeBERTa architecture.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Dataset** & **Configuration** & **Test Accuracy** & **Test F1** \\ \hline \multirow{8}{*}{FakeNewsNet} & baseline & 0.5250 & 0.4326 \\  & no\_disentangled\_attention & 0.5000 & 0.3589 \\  & no\_enhanced\_mask\_decoder & 0.5000 & 0.4588 \\  & no\_relative\_position & 0.5250 & 0.4012 \\  & no\_feedforward & 0.4000 & 0.3308 \\  & no\_residual\_connections & 0.4750 & 0.3059 \\  & no\_layer\_normalization & 0.5250 & 0.3615 \\  & no\_dropout & 0.5000 & 0.4844 \\ \hline \hline \end{tabular}
\end{table}
Table 6: DeBERTa Ablation Study: Performance Across All Datasets.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Dataset** & **Configuration** & **Test Accuracy** & **Test F1** \\ \hline \multirow{8}{*}{LIAR} & baseline & 0.5924 & 0.5826 \\  & no\_positional\_encoding & 0.5892 & 0.5737 \\  & no\_attention & 0.5585 & 0.4002 \\  & no\_feedforward & 0.5887 & 0.5900 \\  & no\_residual\_connections & 0.5553 & 0.5537 \\  & no\_layer\_normalization & 0.5585 & 0.4002 \\  & no\_dropout & 0.5887 & 0.5858 \\  & no\_gelu\_activation & 0.5945 & 0.5600 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Component Removed** & **Avg Impact** & **Std Dev** & **Count** \\ \hline no\_attention & 0.1688 & 0.2151 & 3 \\ no\_residual\_connections & 0.0124 & 0.0175 & 3 \\ no\_layer\_normalization & 0.0030 & 0.0241 & 3 \\ no\_gelu\_activation & 0.0014 & 0.0036 & 3 \\ no\_feedforward & \(-\)0.0154 & 0.0245 & 3 \\ no\_dropout & \(-\)0.0154 & 0.0245 & 3 \\ no\_positional\_encoding & \(-\)0.0240 & 0.0361 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Component Importance Summary Across All Datasets (RoBERTa).