esa-sceva/satcom-chunk-collection huggingface dataset:
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
## 2 Materials and Methods
### Implementation of KANs  
The original KAN was implemented by [PERSON] et al. [1] using a residual activation function \(\varphi(x)\), which is defined as the sum of a base function and a spline function with their corresponding weight matrices \(w_{b}\) and \(w_{s}\):  
\[\varphi(x)=w_{b}b(x)+w_{s}\cdot\text{spline}(x) \tag{6}\]  
where \(b(x)\) denotes the fundamental function, often implemented as SiLU, and \(\text{spline}(x)\) is represented as a linear combination of B-splines. The initial values for each activation function are \(w_{s}=1\) and \(\text{spline}(x)\approx 0\), and \(w_{b}\) is initialized using the Xavier initialization.  
The Efficient KAN approach [27] improves efficiency by using B-spline basis functions \(b(x)\) and linear combinations, reducing memory usage and simplifying calculations. The final output \(Y\) is computed as a sum of a base linear transformation and a spline-adjusted output:  
\[Y=f_{activation}\ (x)\ W_{base}^{T}+B(x)W_{spline} \tag{7}\]  
The authors replaced the incompatible L1 regularization on input samples with L1 regularization on spline weights, \(L_{1}=\lambda_{activation}\sum\left|W_{spline}\right|\), and introduced learnable scaling for activation functions. They also initialized base weights and spline scalers using Kaiming uniform initialization to enhance training stability.  
As described in [28], the Fast KAN approach has enhanced training speed by utilizing Radial Basis Functions (RBFs) to approximate the 3 rd-order B-spline. This method also incorporates layer normalization to ensure that the inputs remain within the RBFs' range. These adjustments simplify the implementation process while preserving accuracy. The RBF is defined as follows:  
\[\varphi(r)=e^{-\varphi r^{2}} \tag{8}\]  
The distance \(r=x-c\) represents the Euclidean distance between the input vector \(x\) and the center vector \(c\), and the parameter \(\varrho>0\) is a sharpness value that determines the width of the Gaussian function. FastKAN utilizes a specific variation of RBFs in which we have the following:  
\[r=\frac{x-c}{h},\quad\varrho=\frac{1}{2} \tag{9}\]  
Thus, the equation becomes the following:  
\[\varphi(r)=\exp\left(-\frac{1}{2}\left(\frac{x-c}{h}\right)^{2}\right) \tag{10}\]  
Finally, the RBF network with \(N\) centers can be expressed as follows:\[\text{RBF}(x)=\sum_{i=1}^{N}w_{i}\varphi(r_{i})=\sum_{i=1}^{N}w_{i}\exp\left(- \frac{1}{2}\left(\frac{x-c_{i}}{h}\right)^{2}\right) \tag{11}\]  
The Faster KAN approach [31] signifies a progression from the Fast KAN approach by integrating Reflectional Switch Activation Functions (RSWAFs). These RSWAFs, modifications of RBFs, provide a computationally straightforward method owing to their uniform grid structure. The RSWAF is formally defined as follows:  
\[\varphi(r)=1-\tanh\left(\frac{x-c}{h}\right) \tag{12}\]  
The RSWAF network with \(N\) centers is then given by:  
\[\text{RSWAF}(x)=\sum_{i=1}^{N}w_{i}\varphi(r_{i})=\sum_{i=1}^{N}w_{i}\left(1- \tanh\left(\frac{x-c_{i}}{h}\right)\right) \tag{13}\]
## 4 Discussion
### GCPs and CPs Distribution  
The precision depends mainly on the quantity and strategy for locating the GCPs. An analysis of the results obtained for the six configurations evaluated revealed the following.  
Configurations 1 and 2 had very few GCPs, increasing the RMSE in the CPs beyond 30 cm. This value is more than 10x the GSD due to the lack of either control points or strips. Small samples of ground control points (GCPs) can reduce statistical significance and negatively affect the accuracy and reliability of the UAV-generated photogrammetric model.  
Configuration 3 has a high RMSE value, similar to the Configuration 1 and 2 values; this occurs by the absence of GCPs in the block edges. This is why GCPs should go along the edges of square and rectangular blocks; in the case of irregular blocks, GCPs should follow a similar distribution [57].  
Configuration 4 achieved the best RMSE\({}_{\text{xyz}}\) CPs mean value. The authors of [43] analysed different distributions of GCPs in a corridor-shaped photogrammetric block. Several combinations of GCPs were used, but the best results were obtained by locating the GCPs on both sides of the road and one GCP for each edge of the block. This distribution is similar to this study, as it obtained an RMSE\({}_{\text{xy}}\) = 3.1 cm and an RMSE\({}_{\text{z}}\) = 8.1 m, while our results left nine points as CPs (Table 6) were RMSE\({}_{\text{xy}}\) = 4.5 cm and RMSE\({}_{\text{z}}\) = 6.7 cm. The main difference between [43] and this study is the addition of oblique perimeter images, which improved the RMSE\({}_{\text{z}}\). Additionally, [43] achieved horizontal and vertical accuracies of approximately 3.5 x the GSD. In this study, the planimetric accuracy was similar (1.7 x the GSD), but the vertical accuracy was better (2.5x the GSD).  
Although Configurations 5 and 6 had a higher GCP, the RMSE results did not decrease for Configuration 3, which essentially agrees with [69], which used six to seven GCPs well-distributed throughout the study area. The results were similar to those obtained using 15 GCPs. The same occurs in [70], in which an optimal GCP density was reached, and there was no further decrease in the horizontal and vertical errors.  
According to the analysis, Configuration 4 presents the best results of the RMSE CPs. Therefore, it is essential to clarify that the GCPs must be well-distributed to ensure the cross strips cover them and can be captured by the camera at different directions, angles, and heights to correct errors of inclination and distortion.
# From Climate Conditions to the Numerical Slope Stability Analysis of Surface Coal Mines
###### Abstract
The stability of these areas is essential, especially during the current period of decarbonization and movement towards the post-coal and post-lignite era. Areas that hosted coal and lignite mines are now being reclaimed, e.g., by installing renewable energy systems, and new energy principles relating to energy production, harvesting, and storage are being set. The appropriate exploitation of these areas is directly related to their safety and stability in sustainably contributing to the new era. The proposed framework is formulated to be used in various conditions. An in-depth climatic analysis appropriately defines the parameters and specifies the range of rainfall intensity for stability purposes. Initially, the baseline rainfall is established from reported climatic conditions for the last thirty years and extreme events of the last ten years. Furthermore, climatic projections are used to anticipate trends and possible changes in the baseline, past, and current rainfall conditions. Finally, a typical slope representing a mine excavation is simulated within the anticipated climatic conditions. The finite element method (FEM) is employed, and a fully coupled flow-deformation analysis is conducted that allows for the simultaneous evaluation of the unsaturated transient seepage flow and the mechanical response of the soil. A parametric analysis is conducted for the critical geotechnical parameters. Greek lignite areas are used as a case example; however, the methodology and insights apply to several similar surface slopes. The numerical model (geometry, properties) can be implemented similarly in many countries where similar conditions are found. The results of this work can be used as a guideline for similar situations.
# Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway
## 2 Collision Warning Algorithm for Small Fishing Vessels  
The aim of this study is to verify the operation of a millimeter-wave communication-based collision warning algorithm for small fishing vessels and to validate its feasibility by applying it to a real ship test in a real sea area. This algorithm was developed in one of our previous research works [23]. Therefore, it is necessary to provide a brief introduction to the previously developed collision warning algorithm. The developed algorithm differs from others in its use of the potential assessment of risk (PARK) model, as summarized in Section 2.1; the algorithm is described in Section 2.2.
# On Isospectral Composite Beams
## 3 Examples  
As an application of the above results, we determine examples of composite beams that are isospectral to the uniform composite beam with \(\widehat{A}(z)=1\), \(\widehat{\mu}(z)=\pi^{2}\), \(E=1\), \(\gamma=1\) and \(L=1\) under supported-end conditions. A direct calculation shows that \(\widehat{a}(x)=1\), \(\widehat{k}(x)=\pi^{2}\), \(y_{m}(x)=\sqrt{2}\sin(m\pi x)\), \(\widehat{\lambda}_{m}^{V}=(1+m^{2})\pi^{2}\), \(m=1,2,...\).  
The isospectral composite beam has a shearing stiffness coefficient \(k(x)\) given by (39). The isospectral coefficients shown in Figure 2 have been derived for \(m=1\) and \(c=-0.1\), \(c=-0.2\), and \(c=-0.3\). The initial uniform beam corresponds to \(c=0\). Similarly, isospectral coefficients for \(m=2\), with \(c=-0.05,\ c=-0.1\), and \(\ c=-0.2\) and for \(m=5\) with \(c=-0.08,\ c=-0.1\), and \(\ c=-0.12\) are shown in Figures 3 and 4, respectively.  
It can be seen from these figures that when \(m\) is taken to be large, then the stiffness coefficient \(k(x)\) depart significantly from that of the uniform beam \(\widehat{k}(x)=\pi^{2}\); that is, the change becomes more sensitive to changes in \(c\).  
Figure 4: Isospectral stiffness coefficient \(k(x)\) as in (39), for \(m=5\) and \(c=-0.08,-0.1,\) and \(-0.12\).  
Figure 3: Isospectral stiffness coefficient \(k(x)\) as in (39), for \(m=2\) and \(c=-0.05,-0.1,\) and \(-0.2\).  
Figure 2: Isospectral stiffness coefficient \(k(x)\) as in (39) for \(m=1\) and \(c=-0.1,-0.2,\) and \(-0.3\).  
The isospectral equivalence has been verified by finite element (FE) analysis. The numerical procedure herein adopted is based on a standard FE model of the problem (33) and (34) with a uniform mesh and continuous piecewise linear displacement shape functions \(\varphi_{j}=\varphi_{j}(x)\), where \(j=1,2\). The local matrix entries of the stiffness \(k_{\ell}^{ij}\) and mass matrix \(m_{\ell}^{ij}\) are evaluated by the formulas  
\[m_{\ell}^{ij}=\int_{x_{\ell}}^{x_{\ell+1}}\widehat{A}\varphi_{i}\varphi_{j}dx,\quad k_{\ell}^{ij}=\int_{x_{\ell}}^{x_{\ell+1}}(\widehat{A}\varphi_{i}^{ \prime}\varphi_{j}^{\prime}+k\varphi_{i}\varphi_{j})dx, \tag{40}\]  
where \(i,j=1,2\), and are evaluated in exact form. A model with \(N=200\) equally spaced finite elements was built by assembling the local matrices to form the global matrices and properly assigning the Dirichlet boundary conditions. For \(N=200\), the first ten eigenvalues for the cases \(c=-0.1\) and \(m=5\) are given in Table 1 and are compared with the exact values corresponding to the uniform beam. It is seen that the constructed beams are isospectral to the original uniform beam within the limits of computing accuracy in such an FE approximation.
## 2 Related Work
### Graph-Based Attack Identification Methods  
Data interactions in the network can be represented as graphs. Therefore, there are many studies based on graphs to identify attacks. [PERSON] et al. [15] built a graph structure based on the original packets and analyzed the degree distribution through large deviation theory to achieve the detection of Bot. [PERSON] et al. [9] constructed a graph structure based on the packets and achieved the identification of the Bot by the indegree and outdegree features. [PERSON] et al. [16] built a graph named DNG for DNS and found that the DNG enabled a differentiated description of DNS query failure instances. [PERSON] et al. [17] proposed a distributed method for DDoS attacks using monitors at the active network nodes. This method created simulated datasets of normal and abnormal flows, extracted content features, and used the graph neural network for learning. [PERSON] et al. [18] used a collaborative environment of intelligent agents to detect attacks. Each agent deployed a hierarchical graph-based learning network to effectively monitor the entire infrastructure and counter the propagation of attacks.  
Anyway, [PERSON] et al. [19] constructed a behavior profile file of malware for advanced persistent threats and used a graph network to analyze the behavior profile to identify attacks. [PERSON] et al. [20] pointed out that the existing DDoS detection works to face the problem of non-negligible time delay and the poor generality of detection models for attacks with different types and rates. Therefore, they established for the first time a directed graph kernel to measure the divergence features between the current graph and the normalized graph and constructed a dynamic threshold and a freezing mechanism to display changes in the standard traffic. Then, [PERSON] et al. [21] raised the issue that the existing studies are insensitive to a small number of attacks. Therefore, they proposed the THREATRACE, a real-time detection system that detected host threats at the system entity level without prior knowledge of attack patterns. The THREATRACE uses an inductive graph neural network to learn the role of each entity in a graph. [PERSON] et al. [22] proposed a temporal graph-based method. The continuous-time dynamic graphs were constructed from the log records based on the interactions between users and entities. The results show that this method possesses better detection performance than state-of-the-art methods in both direct push learning and inductive learning.  
The identification methods based on the graph can provide a more comprehensive description of the traffic composition of an attack behavior from a multi-flow perspective. Moreover, these methods can reflect the changes in an attack behavior over time, which are more adaptable to identifying attacks in dynamic networks.
# Research on Key Technologies of Data Processing Mechanisms in Ternary Optical Computer
## 3 Computation-Data Files
### TOC Data Processing Method  
Based on the hardware structure and computational characteristics of the TOC, a data processing method for the TOC is proposed, utilizing the computation-data file. The flowchart is depicted in Figure 3.  
Based on the data processing method flowchart, the following processing scheme is implemented:  
1. Users' computational rules and large amounts of raw data are organized into the computation-data file, which is specifically formatted and constructed based on the operational characteristics of the ternary optical computer.  
Figure 3: Flowchart of TOC data processing method.  
Figure 2: Schematic diagram of the computation-data file format.  
2. Specialized software assists users in creating the computation-data file. As depicted in Figure 4, this software provides a straightforward interface, requiring users to input only their computational requirements and original operands. The software then generates the corresponding computation-data file in line with the users' intentions, operating on an auxiliary PC.
3. The computation-data file is transmitted to the TOC by extending high-level language instructions, allowing users to employ the TOC through applications written in high-level languages.
# Light-Responsive Polymeric Micellar Nanoparticles with Enhanced Formulation Stability
## 3 Results  
### Synthesis of the Light-Sensitive Block Copolymers and Formulation of the Micallar Nanoparticles  
The synthetic route of the amphiphilic block copolymers, PEG-PLL-DNQ and PEG-PLL-Oct (Figure 1), is shown in Figure S1. PEG-PLL (\(M_{\text{n}}\) \(\sim\) 7400, 5k PEG conjugated with 10 lysine units) was simply coupled to DNQ-sulfonyl chloride (sc-DNQ) under a base condition to afford PEG-PLL-DNQ in 80% yield. The successful synthesis of PEG-PLL-DNQ was characterized by \({}^{1}\)H NMR spectroscopy. As shown in Figure S2, the characteristic resonance signals of DNQ at 8.70, 8.47, 7.70, 7.60, and 5.29 ppm were observed in the spectrum of PEG-PLL-DNQ in DMSO-\(d_{6}\), which is evidence of the successful conjugation of DNQ groups on PEG-PLL. Octanoyl chloride (c-Oct) without aromatic groups was also conjugated to PEG-PLL in the same manner to form PEG-PLL-Oct that only provides hydrophobic interactions in the core of the micelle without \(\pi\)-\(\pi\) stacking interactions.  
As previously reported, AB-type amphiphilic block copolymers composed of hydrophilic and hydrophobic components can self-assemble into micelles, where the hydrophobic core of the micelle can encapsulate hydrophobic drugs which will otherwise precipitate in aqueous solutions. In this study, we synthesized a new block copolymer, PEG-PLL-DNQ, containing hydrophobic light-sensitive DNQs which could generate additional \(\pi\)-\(\pi\) stacking interactions between the neighboring DNQs on the polymer backbone inside of the micelle core. Furthermore, these DNQs could also interact with DTX, which contains two aromatic phenyl groups that can form another \(\pi\)-\(\pi\) stacking in addition to hydrophobic interactions (Figure S1). These interactions will contribute to the higher formulation stability of the micelle as well as its increased loading capacity (LC) with aromatic drugs.  
The DTX-loaded polymeric micellar nanoparticle (**DTX-NP\({}_{\text{DNQ}}\)**) was prepared by the thin-film hydration method. Dynamic light scattering (DLS) results showed that PEG-PLL-DNQ formed micelles with a volume average hydrodynamic diameter of 143 nm and TEM image revealed that **DTX-NP\({}_{\text{DNQ}}\)** were formed with a spherical morphology (Figure 2). For controlled experiments, **DTX-NP\({}_{\text{Oct}}\)** was successfully formulated with 105 nm in diameter (Figure S3).  
Figure 1: Structure of PEG-PLL-DNQ and PEG-PLL-Oct.  
Figure 2: (**a**) DLS size distribution of **DTX-NP\({}_{\text{DNQ}}\):** (**b**) TEM image of **DTX-NP\({}_{\text{DNQ}}\)** (scale bar = 500 nm).
## 3 Experimental Regime
### Adhesion Testing Method Preparation
Using the ISO standard evaluation method alone was difficult to comprehensively determine the adhesion strength of the spot-bonded tile as the difference in adhesion strength varied to a significant degree, depending on which part of the adhesion attachment piece (pull-off plate) was attached to the specimen. In real sites, this type of defect could be common with the glob-type application for spot-bonding and could result in fractures or cracks occurring on the tile if left unattended. Subsequently, a specially manufactured metal disc with a size of 250 \(\times\) 400 mm was used, and a tension jig was installed in the center of the tile as shown in Figure 9 (shown in a,b), at a speed of 250 \(\pm\) 50 N/s using a standard UTM specification testing apparatus. As can be seen in Figure 9c, the result of the fracturing of time was expected to having been caused due to a bending phenomenon, in which the concentrated stress of the tension pulled the attachment on the specimen body (concrete plate). The cavities under the edge sections, due to the lack of adhesive dispersion, allowed the tile to crack before a proper measurement of the entire tile's adhesive strength could be taken. Figure 8: Tile adhesion state after ISO standard adhesion testing. Figure 9: Entire tile-pull-off testing attempt: (**a**) testing plan (unit: mm); (**b**) testing site; (**c**) testing results (fracture at the edge sections of the tiles). Accordingly, a new evaluation method was derived to determine the comprehensive adhesion strength of the total surface area of the tile by sectioning the entire tile into individual pieces, whereby the adhesion strength testing (pull-off testing) was conducted, where applicable, and the adhesion strength concentration areas could be quantified and mapped. The new evaluation method derived, based on the results of the ISO standard evaluation method, consisted of sectioning the entire surface of the tile. As shown in Figure 10, a 250 \(\times\) 400 mm ceramic tile (compliant to the specifications in Table 3, designed for the new evaluation) was used, and the entire surface of the tile was sectioned into 50 \(\times\) 50 mm pieces (total of 40 pieces), where 8 pieces were lined up along the width and 5 pieces along the length of the tile. This process was deemed necessary for the evaluation to be able to (1) objectively and completely compare and assess the degree of adhesive dispersion ratio after pressing between the specimens of different ARs (to be able to determine how many pieces would end up with results similar to the one found in Figure 8, number 4 tile piece), and (2) if the sectioned pieces were in accordance with the specifications of the ISO evaluation standard. The process was repeated for tiles where the cementitious adhesive application surface ratio differed by the following intervals: 60 \(\pm\) 2%, 80 \(\pm\) 2%, and 100% surface area.
## 2 Materials and Methods
### Selection of the Hardware Platforms and Algorithms for the Performance Tests  
For the performance testing, two platforms were chosen.  
1. The most popular in the open STEM-like hardware projects is Pi Pico by Raspberry (Figure 17), based on an RP2040 processor [40], see Figure 18. Core type: ARM Cortex-M0+, whose calculation performance is roughly 130 DMIPS.
2. The CDHM platform \"Falco SBC 1.0\" (see Figure 19), which was developed by [PERSON] [41] for the Ph.D. thesis, will be used for the upcoming \"KhAI-1 spacecraft\" 3U CubeSat. The platform is based on the Microchip (Atmel) ATSAMV71Q21 [42; 43] and shown in Figure 20. The core type is ARM Cortex-M7 and it's expected calculation performance is roughly 600 DMIPS.  
Figure 16: A WASM3 remote container control at the CDHM (START/STOP commands).  
Figure 17: The COTS Raspberry Pi Pico Dev. kit.  
Figure 15: A WASM3 container upload to the CDHM.  
At the same time, typical algorithms for the embedded software were used for the benchmarking, namely:  
* Fast Fourier transform (FFT);
* bubble sort;
* The CRC-16 checksum calculation algorithm.  
Figure 19: The Falco SBC/CDHM component.  
Figure 20: The Falco SBC/CDHM microprocessor ATSAMV71Q21.  
Figure 18: The Pi Pico processor RP2040.  
For the selected algorithms, the following algorithm's complexity in \"O-notation\" is expected:  
The main reason for measuring performance here is to see how much overhead the two layers of abstraction (WASM3 and its middleware) bring to the proposed solution. To determine the exact overhead, the same selected algorithms (see Table 1) were run on a bare metal implementation, i.e., what vendors offer as low-level API + FreeRTOS and on the code written on C/C++ and further WASM3 compiled, where the containers execution engine is also running on FreeRTOS. The following pre-requisites were used for the performance measurements:  
1. For ensuring repeatability and to be able to properly calculate the S, SD, and AVG times of the computation, 1000 measurements are planned for each experiment;
2. For each algorithm, 3 different sizes of data will be used. This will be mainly used to prove that the implementation is carried out in a proper manner and O-complexity is followed;
3. All measurements will be rounded to two digits after the comma;
4. For the CRC-16 algorithm, the sets of 100, 1000, and 10,000, 32-bit signed integers will be used;
5. For the bubble sort algorithm, the sets of 100, 500, and 1000, 32-bit signed integers will be used bubble sort sets with the worst possible condition are used--the data vector to be sorted was filled with numbers placed in a back-sorted order.
6. For the Fast Fourier Transform algorithm, the sets of 128, 256, and 512 samples will be used.