esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Dimensional Attention ```
## 5 Result and Discussion
### Comparative Discussion  
The proposed MMCW-BiLSTM model outperforms existing models in terms of volatility of demand, issues with data quality, and difficulty in capturing multi-dimensional interactions between features with the use of advanced techniques purposefully built for forecasting inventory tasks. Using BiLSTM layers in conjunction with both collaborative and multi-dimensional attention mechanisms gives the model the ability to tackle complex temporal dependencies and feature correlations in the dataset. This comprehensively leads to more accurate forecasts, especially when several factors are implicated and influence inventory levels. In addition, Taylor series transformations allow the model to take into account higher-order interactions among the features, which enhances its overall predictive power. The MMCW-BiLSTM model was compared to traditional and state-of-the-art methods like SVM, Bayesian approaches, BP neural networks, and meta-heuristic CNN-LSTM methods. The MMCW-BiLSTM model consistently performed better than these techniques, such that there was an affirmation of the model in accurately forecasting inventory levels. In short, it is the incorporation of superior architecture and exhaustive evaluation procedures that make our MMCW-BiLSTM models perform better than other predictors for inventory forecasting. The results show that the models have the least number of errors, with values of 1.75, 2.89, 6.76, and 2.6 for MAE, MAPE, MSE, and RMSE when using the AV demand forecasting dataset. Likewise, when utilizing the forecast product demand dataset, the model has the lowest error values of 1.97, 3.91, 8.76, and 2.96 for these metrics. Similarly, when utilizing the dairy goods sales dataset, the model has the lowest error values of 2.54, 3.69, 10.39, and 3.22. The comparative discussion table for the AV demand forecasting dataset and forecast for the product demand dataset is shown in Table 14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Computational Time**} \\ \cline{2-4}  & **AV Demand** & **Product Demand** & **Dairy Goods** \\  & **Forecasting Dataset** & **Dataset** & **Sales Dataset** \\ \hline SVM & 20.80 & 20.76 & 20.69 \\ \hline Bayesian approach & 20.80 & 20.81 & 20.78 \\ \hline BP neural network & 20.81 & 20.81 & 20.81 \\ \hline Meta-heuristic method evolves & 20.81 & 20.83 & 20.82 \\ CNN-LSTM & 20.83 & 20.83 & 20.83 \\ \hline Pharmaceutical supply chain & 20.83 & 20.83 & 20.83 \\ model & 20.46 & 20.52 & 20.59 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparative analysis based on computational time.
# An Evasion Attack against Stacked Capsule Autoencoder
## 1 Introduction  
Image recognition is a popular research topic in machine learning, and convolutional neural networks (CNNs) are among the major methods addressing this task. CNNs abstract images into local features through operations, such as convolution and pooling, and the local features are then used as evidence for identification. CNNs are able to meet the requirements of common image recognition tasks, but they cannot handle images after affine transformation well, such as rotation and resizing.  
A capsule network is a type of neural network that is designed to improve the performance of traditional CNNs when handling images after affine transformation. Based on abstraction, the capsule network further analyzes the spatial relationship between features to promote the reliability of the classification. The stacked capsule autoencoder (SCAE) is a state-of-the-art capsule network which has a different structure from the past capsule networks. First, the poses of features and the relationship between features are extracted from the image. Then, they are combined and encoded into capsules. Finally, the predicted result of the image is obtained by inputting the output of the capsules into the classifier. One of the highlights of the SCAE is the unsupervised classification on the capsule network, which uses bipartite graph matching [1] to find the permutation of cluster indices after finding multiple clusters using the k-means approach.  
Recent studies have found that capsule networks also face security threats [2; 3; 4; 5]. However, these studies only focus on capsule networks based on dynamic routing. To thebest of our knowledge, the potential security vulnerabilities of SCAE have not been studied. In this paper, we address this issue. The contributions of the paper can be summarized as follows:  
1. We propose an evasion attack against the SCAE in which the attacker can compute the perturbation based on the output of the capsules in the model to generate adversarial samples that lead to misclassification of the SCAE;
2. The attack achieves a high attack success rate on various datasets, which confirms that the SCAE has a security vulnerability that allows for the generation of adversarial samples without changing the original structure of the image to fool the unsupervised classifier in the SCAE.  
The remainder of this paper is arranged as follows: Section 2 introduces the related works. Section 3 describes the architecture of the SCAE and explains its operations. Section 4 presents our attack method and algorithm in detail. Section 5 describes the experiments and the results. Finally, Section 6 provides a summary and briefly presents direction for future work.
## 2 Materials and Methods
### Microdsimetric Measurements Using TEPC  
Microdsimetric investigations were performed using a tissue-equivalent proportional counter (TEPC) of cylindrical shape, which was designed and constructed at LNL. The sensitive volume of the TEPC consists of a cavity featuring equal diameter and height of 1.3 cm. The anode is made of a 100 \(\upmu\)m gold-plated tungsten wire and is surrounded by a helix, made of the same wire, with a diameter of 6 mm. This helix confines the electronic avalanche in proximity to the anode. The cathode wall has a thickness of 1 mm and is made of A-150 tissue-equivalent plastic. It is designed as two half-cylindrical shells that  
Figure 1: **Top panel**: top view drawing of the moderator. Heavy water (light blue), graphite (grey), and bismuth (dark grey). The white region is the beam line that enters the moderator. **Bottom panel**: a picture of the irradiation set-up with the BSA for thermal neutrons and the TEPC in front of the bismuth window.  
can be removed and replaced without difficulty (Figure 2). This feature allows the counter to be supplied with cathode walls doped with different concentrations of boron. For the current measurements, five different concentrations of boron were used, using five pairs of half-shells. The boron concentrations utilized were 0, 10, 25, 70, and 100 ppm of \({}^{10}\)B. The TEPC is enclosed within an aluminum cap with a thickness of 0.2 mm. The total counter thickness is 0.21 g/cm\({}^{2}\). The anode, helix, and cathode electrodes can be independently biased to improve gas gain and energy resolution. Additional information on the operation of this type of TEPC can be found in reference [18].  
Pure propane gas was used to fill the cylindrical cavity of the TEPC. The gas pressure was set at 31.4 hPa, which provided a tissue-equivalent diameter of 1 \(\upmu\)m [21]. To ensure stable gas gain, the gas continuously flowed at a rate of 0.1 cc/min STP. The cathode bias voltage was set at \(-\)210 V, the helix at \(-\)200 V, and the anode at +340 V. Five independent measurements were conducted using the same TEPC. The first measurement involved undoped cathode walls, and the subsequent measurements used the other four sets of cathode walls loaded with different concentrations of \({}^{10}\)B. The detector was placed in the same irradiation position in front of the bismuth window (Figure 1), and \(10^{6}\) counts were recorded for each measurement.
## 4 Redundant Transmission Control Clustering Approach
### Initialization Phase  
In our designed model, the sensing area is partitioned into the regions and then each of the regions is partitioned into cubes. The length of the region is taken as \(R\) while the length of the cube is taken as \(r\) for further calculations. Here, we take each cube as a cluster and we adjusted \(r\) according to the communication range of FNs. The FNs are expressed through their location \(L(i,j,k)\) and their cluster number \(N(x,y,z)\). Whereas \(x\), \(y\), and \(z\) are computed using the following equations:  
\[x =\frac{r-i}{r}\mod r+i \tag{9}\] \[y =\frac{r-j}{r}\mod r+j\] (10) \[z =\frac{r-|k|}{r}\mod r+|k| \tag{11}\]  
when the network configuration is complete, each RH transmits the initialization message to their corresponding clusters which contains RH location information for future correspondences. On receiving this initialization message, each of the FN computes its distance from the RH \(d_{i,RH}\) and other FNs \(d_{i,j}\) through this equation:  
\[d_{i,j}=\sqrt{(i_{1}-i_{2})^{2}+(j_{1}-j_{2})^{2}+(k_{1}-k_{2})^{2}}. \tag{12}\]  
where \((i_{1},i_{2})\), \((j_{1},j_{2})\) and \((k_{1},k_{2})\) are points between FN and RH.
## 2 Materials and Methods
### Data Analysis  
The mean and the standard deviation of the air velocity in streamwise and vertical directions at each measurement position were provided by BSA Flow Software (Dantec Dynamics, Skovlunde, Denmark). Air velocity and air turbulence characteristics were processed from 40,000 samples at each position to ensure statistically reliable results.  
The velocity magnitude calculated from two-dimensional velocity components was defined as:  
\[V_{2D}=\sqrt{U^{2}+W^{2}} \tag{1}\]  
where \(V_{2D}\) is the velocity magnitude, m\(\cdot\)s\({}^{-1}\); \(U\) and \(W\) are mean air velocities in streamwise and vertical directions, respectively, m\(\cdot\)s\({}^{-1}\).  
Turbulence intensity and turbulent kinetic energy of the airflow were calculated by Equations (2) and (3), respectively.  
\[TI=\sqrt{0.5\big{(}\sigma_{U}^{2}+\sigma_{W}^{2}\big{)}}/V_{2D}\times 100\% \tag{2}\]  
\[TKE=0.5\big{(}\sigma_{U}^{2}+\sigma_{W}^{2}\big{)} \tag{3}\]  
where \(TI\) is the turbulence intensity, \(\%\); \(TKE\) is the turbulent kinetic energy, m\({}^{2}\cdot\)s\({}^{-2}\); \(V_{2D}\) is the velocity magnitude, m\(\cdot\)s\({}^{-1}\); \(\sigma_{U}\) and \(\sigma_{W}\) are standard deviations of the instantaneous air velocity in streamwise and vertical directions, respectively, m\(\cdot\)s\({}^{-1}\).  
It is noted that in this study \(V_{2D}\), \(TI\), and \(TKE\) were calculated based on a two-component velocity analysis.
## 3 Results and Discussion  
### Bulk Density, Open Porosity, and Natural Moisture Content  
The bulk density of R1 blocks is lower than that of R2 blocks, on average 8%. The former are in the 1650-1800 kg/m\({}^{3}\) range, while the latter are in the 1800-1950 kg/m\({}^{3}\) range, as shown in Figure 6. Note the presence of two possible outliers around 2000 kg/m\({}^{3}\).  
In general, fine-grained soils allow for a closer arrangement of particles when subjected to the same degree of compression. This greater compaction capacity is evident when examining the thickness of R1 blocks (77 \(\pm\) 2.25 mm), which is systematically lower than that of R2 blocks (82 \(\pm\) 3.36 mm). On the other hand, a more packed structure has reduced void spaces, resulting in a denser configuration [36; 37]. The results of this study do not support this theory despite the fact that soil R1 has a heavier, fine-grained fraction than soil R2. Possibly, in the soil under investigation, the removal of particles larger than 2 mm resulted in the removal of a group of coarser, heavier particles and the reconfiguration of the granule-porous structure [38].  
Open porosity was estimated at 36% for R1 blocks and 32% for R2 blocks. Therefore, the less dense blocks are also more porous (this result was further confirmed by the results of water absorption and electrical resistivity presented later).  
The natural moisture content was different at 28 and 180 days. At 28 days, both mixtures showed an average moisture content of 1.40%, with a high coefficient of variations (45%). At 180 days, typical values were 0.80% for R1 CEBs and 0.60% for R2 CEBs. The latter values fall within the common range of humidity (0.50 to 5% [39]). The higher moisture content detected in R1 blocks was expected, as finer particles tend to retain water more easily than coarser ones [40].  
Figure 6: Differences in thickness and bulk density due to particle size effect.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 3 Basic Discretisation
### Time Discretisation
#### 3.4.2 Second-Order Time Integration  
First-order accurate methods are easier to construct than the majority of second-order methods, which may be produced by relying on Butcher tableaus. These tableaus may become quite complicated when the scheme has to also be \(l_{0}\)-stable.  
In this section, we recall a second-order \(l_{0}\)-stable (SOI\({}_{0}\)) scheme, first presented in [21], that does not rely on Butcher tableaus and is relatively easy to compute. In the following, we want to give a brief explanation of this method.  
We aim to solve an equation like (19) or (28), and these equations look like  
\[\dot{\mathbf{x}}(t)=A\mathbf{x}(t) \tag{37}\]  
The solution of this ODE is \(\mathbf{x}(t)=\exp(tA)\), and for a time step further, we can write  
\[\mathbf{x}(t+\tau)=\exp((t+\tau)A)=\exp(\tau A)\mathbf{x}(t) \tag{38}\]  
However, calculating \(\exp(\tau A)\) is a complicated task. To simplify it, we make use of approximations such as  
\[R(\tau A)\approx\exp(\tau A) \tag{39}\]  
A simple choice of \(R(\tau A)\) would be \(R(\tau A)=(I-a\tau A)^{-1}(I+(1-a)\tau A)\), which leads to well-known numerical schemes just by changing \(a\). Choosing \(a=0\), we obtain the explicit Euler method. For \(a=1\), we obtain the IE method, as it is presented earlier in this paper. The CN scheme can be produced by setting \(a=0.5\).  
To obtain a \(l_{0}\)-stable second-order method, we need another \(R(\tau A)\). [PERSON] et al. presented the following equation  
\[R(\tau A)=(I-r_{1}\tau A)^{-1}(I-r_{2}\tau A)^{-1}(I+(1-a)\tau A) \tag{40}\]  
as such a suitable choice. With \(r_{1,2}=\frac{1}{2}\Big{(}a\mp\sqrt{a^{2}-4a+2}\Big{)}\), we obtain a method of second-order, and by choosing \(a=2-\sqrt{2}-\varepsilon\), with \(\varepsilon\) being an arbitrarily small positive number, we obtain a \(l_{0}\)-stable method which relies only on real-valued arithmetic.  
Putting these considerations on our Equation (28) leads to the following numerical scheme:  
\[\mathbf{p}_{r}(t_{l})=(I_{2r}-r_{1}\tau H_{r})^{-1}(I_{2r}-r_{2}\tau H_{r})^{ -1}(I_{2r}+(1-a)\tau H_{r})\mathbf{p}_{r}(t_{l-1}) \tag{41}\]
## 3 Results
### Case Study A
#### 3.1.3 Results  
Figure 4a shows a 25-year distribution of the calculated LCOE values to the applied scenarios. The demographical curvatures can be seen in Figure 4b with the following expanded emphasis on LCOE as shown in Figure 4c. Figure 4d illustrates the generational capacity LCOE ranges grouped into the correlated scenarios.  
The total values for the LCOE's at the given generational capacities with their correlated scenarios across the different operational years were calculated. The average LCOE depreciation per year from twenty-five years of operation is shown in Table 9. This showcases the effect of removing a year of operational lifespan effects LCOE evaluations at an expected generational capacity.  
From a logarithmic generational capacity scale, the LCOE drops at a considerate rate before starting to normalize at capacities over one hundred megawatts of capacity. Percentage depreciation from that point forward is approximately 4.7% per every added 100 MW. This drops further to less than 1% when considering over a thousand megawatts. The depreciation of LCOE is clearly visibly in Figure 4c. The operational length of an offshore windfarm has a significant effect at low generational capacities as seen in Table 9, which depreciates as the generational capacity is increased. After 1000 MW, the depreciation difference is almost stabilized.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scenario/Capacity (MW)** & **10** & **100** & **1000** & **10,000** \\ \hline V Low & 11.86 & 1.87 & 0.87 & 0.77 \\ Low & 17.79 & 2.81 & 1.31 & 1.16 \\ Mid & 23.72 & 3.74 & 1.74 & 1.54 \\ High & 29.65 & 4.68 & 2.18 & 1.93 \\ V High & 35.58 & 5.61 & 2.61 & 2.31 \\ \hline \end{tabular}
\end{table}
Table 9: LCOE Increase per Year.
## 7 Conclusions  
The development of SMPNCs has emerged as a groundbreaking class of smart materials, overcoming the limitations of traditional SMPs. By integrating functional nanofillers into SMP matrices offering enhanced thermal and electrical properties while maintaining mechanical integrity. This review underscores the diverse advancements achieved through the integration of metallic nanoparticles, carbon-based materials, and cellulose nanostructures, enabling SMPNCs to respond to diverse stimuli such as magnetic fields, light, electricity, water/solvents, and pH. These multi-responsive capabilities and inherent advantages, such as higher stiffness, strength, and biodegradability, position them as prime candidates for diverse applications, including smart medical devices, soft robotics, engineering structures, energy devices, electronics, and textiles. Specific examples, such as Fe\({}_{3}\)O\({}_{4}\) NPs for magnetic actuation, gold and silver NPs for precise photothermal control, and carbon-based fillers for enhanced electrical conductivity, underscore the versatility and performance of SMPNCs in meeting the demands of advanced engineering and technology applications.  
SMPNCs are traditionally categorized based on thermal-induced phase changes, response mechanisms, and the type of external stimuli triggering shape-memory actuation. However, this review introduces a novel categorization of SMPNCs based on the types of reinforced nanostructures incorporated into polymer matrices, offering a unique perspective on their design and functionality. Despite the challenges associated with incorporating nanostructures, such as aggregation and surface modification effects, innovative techniques have been developed to achieve better dispersion and interfacial interactions within polymer matrices to explore enhanced mechanical and thermal properties. Unlike traditional SMPs that rely solely on thermal activation, SMPNCs' ability to respond to a broad range of stimuli further expands their potential for next-generation applications. Therefore, future research must focus on achieving an optimal balance between nanofillers and polymer matrices, developing advanced actuation mechanisms, and enhancing remote control capabilities of shape-memory effects. By addressing these challenges and expanding their application potential, SMPNCs have the capacity to redefine smart material technology, offering sustainable, adaptive, and dynamic solutions for a wide array of industries.  
[PERSON], [PERSON], [PERSON] and [PERSON] discussed, commented on, and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was generously supported by the Air Force Office of Scientific Research (AFOSR FA9550-23-1-0581; 23 RT0567) and the Robert A. Welch Foundation (Grant Nos. E-1320 and V-E-0001).  
We thank [PERSON] for his valuable suggestions on the preparation of the manuscript and [PERSON] for her assistance in the revision process.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Comparison of Simulation Performance Across Climate Zones
#### 3.5.1 Evapotranspiration (ET)  
The model's simulation of ET exhibits a pronounced, climate-dependent error structure. A general tendency to overestimate annual ET is evident, with the largest mean positive biases occurring in the Polar and Tropical regions. Conversely, the Arid zone displays the smallest mean deviation.  
Of particular significance is the model's apparent incapacity to capture the inter-annual variability of ET in mid-latitude climates. In both the Temperate and Continental zones, the correlation between simulated and observed annual ET is statistically insignificant. This finding suggests a fundamental deficiency in the model's ability to represent the drivers of hydrological dynamics in these regions, limiting its utility for climate sensitivity and trend analyses. The underlying cause may be linked to an inadequate parameterization of the processes critical to these climates, such as snowmelt hydrology, soil freeze-thaw dynamics, and vegetation phenological controls on canopy conductance [39; 40].  
The distribution of simulation biases further elucidates performance heterogeneity (Figure 13a). In the Tropical zone, a notable divergence exists between the mean bias, which indicates a strong overestimation, and the median bias, which suggests an underestimation of approximately 300 mm yr\({}^{-1}\). This implies that the arithmetic mean is highly skewed by extreme positive errors at a subset of sites, while the model's typical behavior is to underestimate ET. The broad interquartile ranges observed in the Temperate and Continental zones signify substantial simulation uncertainty and low model robustness across the sites within these regions.
## 8 Conclusions  
EBCA is a subject worth investigating due to its wide range of applications. The calibration technology presented in this paper makes it possible to assemble new EBCA which can be used by other researchers. Moreover, the testbed released as a part of presented research supports researchers in the development and testing of novel algorithms intended for obtaining disparity maps with the use of EBCA.  
The most significant contribution presented in this paper is the calibration method which reduces the value of BMP metrics by over 34%. Another high value observation is the relation between number of cameras in EBCA and the quality of results when the EEMM merging method is used. Plans for future work include mounting EBCA on a robotic arm and performing hand-eye calibration.  
Conceptualization, A.L.K.; methodology, A.L.K.; software, A.L.K.; validation, A.L.K.; formal analysis, A.L.K. and B.B.; investigation, A.L.K.; resources, A.L.K. and B.B.; data curation, A.L.K.; writing--original draft preparation, A.L.K. and B.B.; writing--review and editing, A.L.K. and B.B.; visualization, A.L.K.; supervision, A.L.K.; project administration, A.L.K.; funding acquisition, A.L.K.; Overall A.L.K. 90%, B.B. 10%. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part under ministry subsidy for research for Gdansk University of Technology, Poland.  
Not applicable.  
The testbed and test data sets presented in this study are openly available at [[https://github.com/alkaczma/ebca](https://github.com/alkaczma/ebca)]([https://github.com/alkaczma/ebca](https://github.com/alkaczma/ebca)) (accessed on 9 August 2021).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
EBCA Equal Baseline Camera Array  
EEMM Exceptions Excluding Merging Method  
LIDAR Light Detection and Ranging  
TOF Time-of-flight camera  
ELAS Efficient Large-scale Stereo Matching  
Stereo5 GBM Stereo Semi-Global Block Matching  
GC Expansion Graph Cut with Expansion Moves  
BMP percentage of bad matching pixels  
BMB percentage of bad matching pixels in background  
COV coverage  
SIFT scale-invariant feature transform  
SURF speeded up robust features  
UUV unmanned underwater vehicle  
CNN convolutional neural network  
MA matching area  
MR margin area