esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 4 Model Realization and Result Analysis  
### Experimental Parameter Optimization and Training Process  
#### 4.1.1 Parameter Calibration  
Model hyperparameters were optimized as follows:  
* Learning rate: Initial 0.001 with decay factor 0.1 per 800 iterations (selected from {0.01, 0.001, 0.0001} via grid search)
* Regularization: L2 factor 0.0001 optimized via Bayesian search (range: 0.00001 to 0.001)
* LSTM units: 6 units validated through 5-fold cross-validation.
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Ant. Type** & **Ant. Size mm\({}^{2}\)/mm\({}^{3}\)** & \
\begin{tabular}{c} **Bands** \\ **GHz** \\ \end{tabular} & **NB IoT7** & **\%\(\eta\)** & **Planar?** &
\begin{tabular}{c} **sub-GHz** \\ **Bands?** \\ \end{tabular} & **Gain (dBi)** \\ \hline
[2] & monopole & \(0.34\lambda_{g}\times 0.23\lambda_{g}\) & \(2.2,3.5,5.5,8\) & No & 54-86.3 & Yes & 0 & 6.9 \\
[3] & monopole & \(0.34\lambda_{g}\times 0.23\lambda_{g}\) & \(3.1\)–\(8.5\) & No & - & Yes & 0 & \(-0.78\) \\
[4] & monopole & \(0.18\lambda_{g}\times 0.36\lambda_{g}\) & \(0.915\), \(2.45\) & Yes & \(93,95\) & Yes & 1 & \(1.9,3.85\) \\
[5] & monopole & \(0.41\lambda_{g}\times 0.58\lambda_{g}\) & \(2.4\) & Yes & \(79\) & Yes & 0 & \(-0.256\) \\
[6] & patch & \(0.15\lambda_{g}\times 0.61\lambda_{g}\) & \(0.82\) & No & - & No & 1 & \(-2.5\) \\
[15] & monopole & \(0.50\lambda_{g}\times 0.19\lambda_{g}\) & \(0.9,1.8,2,3.2,6\) & Yes & \(80\)–\(90\) & Yes & 1 & 3–4 \\
[16] & monopole & \(0.15\lambda_{g}\times 0.16\lambda_{g}\) & \(0.85,2.1\) & No & \(40\)–\(67.2\) & Yes & 1 & 1–32 \\
[17] & monopole & \(0.14\lambda_{g}\times 0.14\lambda_{g}\) & \(0.85,2.4\) & No & \(70,90\) & Yes & 1 & - \\
[18] & PIFA & \(0.23\lambda_{g}\times 0.05\lambda_{g}\) & \(0.829,1.95\) & No & \(60\)–\(73\) & No & 1 & - \\
[19] & PIFA & \(0.044\lambda_{g}\times 0.16\lambda_{g}\) & \(0.836\), \(2.2,3.6\) & No & \(48\)–\(83\) & No & 1 & 0.5–5.2 \\
[20] & slot & \(0.035\lambda_{g}\times 0.035\lambda_{g}\) & \(1.32\)–\(14.9,17.5\)–\(82\) & Yes & \(45\)–\(81\) & Yes & 0 & \(0.5-4.5\) \\
[21] & PIFA & \(0.15\lambda_{g}\times 0.12\lambda_{g}\) & \(0.22,0.80,4.7,4.96\) & No & \(60\)–\(85\) & No & 2 & - \\
[22] & patch & \(0.75\lambda_{g}\times 0.75\lambda_{g}\) & \(3.01,3.83,4.83,5.76\) & No & \(42\)–\(60\) & Yes & 0 & \(1.43\)–\(3.06\) \\
[23] & PIFA & \(1.55\lambda_{g}\times 1.55\lambda_{g}\) & \(0.41\)–\(0.91,2.1\)–\(3.5\) & No & \(10\)–\(50\) & No & 1 & \(-6.1,4.9\) \\
[32] & slot & \(1.58\lambda_{g}\times 1.94\lambda_{g}\) & \(1.82\)–\(1.93,2.1\) & No & \(85\)–\(89\) & No & 0 & \(3.2\)–\(6.3\) \\
[33] & slot & \(1.2\lambda_{g}\times 1.33\lambda_{g}\) & \(5\)–\(6\) & No & - & Yes & 0 & \(1.36\)–\(1.87\) \\
[34] & slot & \(0.65\lambda_{g}\times 0.36\lambda_{g}\) & \(3.0\) & No & - & Yes & 0 & \(-0.44\)–\(2.97\) \\
[35] & slot & \(0.55\lambda_{g}\times 0.16\lambda_{g}\) & \(5.73\)–\(5.97\) & No & \(60\) & No & 0 & \(1.28\) \\
[36] & slot & \(0.014\lambda_{g}\times 0.0425\lambda_{g}\) & \(2.45\)–\(3\) & No & - & No & 0 & \(-25\) \\ \hline Prop. & slot & \(0.18\lambda_{g}\times 0.15\lambda_{g}\) & \(0.78\lambda_{g}\)–\(1.04\) & Yes & \(54\)–\(67\) & Yes & \(9\) & \(0.86\)–\(1.8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Proposed IoT antenna versus related works.
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
###### Abstract  
Surface modification treatments able to confer antistain/antibacterial properties to natural or synthetic materials are receiving increasing attention among scientists. Ion beam co-sputtering (IBS) of zinc oxide (ZnO) and poly-tetrafluoroethylene (PTFE) targets allows for the preparation of novel multifunctional coatings composed of antimicrobial ZnO nanoparticles (NPs) finely dispersed in an antistain PTFE polymeric matrix. Remarkably, IBS has been proved to be successful in the controlled deposition of thin nanocoatings as an alternative to wet methods. Moreover, tuning IBS deposition parameters allows for the control of ZnONP loadings, thus modulating the antibacterial/antistain coating's final properties. All the deposited coatings were fully characterized by X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and transmission electron microscopy (TEM) in order to obtain information on the materials' surface composition, with deep insight into the nanocoatings' morphology as a function of the ZnONP loadings. An analysis of high-resolution XP spectra evidenced a high degree of polymer defluorination along with the formation of inorganic fluorides at increasing ZnO volume ratios. Hence, post-deposition treatments for fluorides removal, performed directly in the deposition chamber, were successfully developed and optimized. In this way, a complete stoichiometry for inorganic nanophases was obtained, allowing for the conversion of fluorides into ZnO.  
ZnO; nano antimicrobials; ion beam sputtering; fluoropolymer; X-ray photoelectron spectroscopy; atomic force microscopy +
Footnote †: journal: Applied Physics Letters
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 3 Methodology for the Elevation Angle Characterization  
In this section, we describe the procedure to characterize the elevation angle behavior through a random variable in a manageable analytical expression. Also, we include a procedure to obtain useful expressions of the elevation angle, such as measures of central tendency and dispersion.  
The elevation angle characterization for different LEO orbit's configuration will help to predict which orbits are more convenient for a particular ES location. In addition, the measures of central tendency and dispersion will help to choose suitable orbits based on the expected elevation angle, and to calculate the link attenuation based on the distribution of the elevation angle, and on its expected value and variance.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 3 Materials and Methods
### Positional Encoding  
Positional encoding, by embedding positional information, enhances the model's capability to understand the order of elements within a sequence and their contextual relationships. In this paper, we innovatively applied the position encoding scheme from the LoFTR [26] to our framework. This method employs sine and cosine functions to assign unique positional identifiers to each two-dimensional point, thereby enabling the transformed features to perceive their relative positions within the global context.  
The specific formula is defined as follows:  
\[PE_{(x,y),i}=\begin{cases}\sin(x/10000^{i/d_{model}})&\text{if $i$ mod $4=0$}\\ \cos(x/10000^{i/d_{model}})&\text{if $i$ mod $4=1$}\\ \sin(y/10000^{i/d_{model}})&\text{if $i$ mod $4=2$}\\ \cos(y/10000^{i/d_{model}})&\text{if $i$ mod $4=3$}\end{cases} \tag{1}\]  
where \(x\) and \(y\) respectively represent the horizontal and vertical coordinates of the point on the two-dimensional image, \(i\) represents the index of the feature length, and \(d_{model}\) represents the length of the feature.
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 1 Introduction
### Existing Problems and Solutions  
#### 2.3.1 Existing Problem  
The difficulty of shield tunnel construction in this project is the original bridge foundation piles. Generally, in the past, there are two preferred methods to solve this problem. The most economical and reasonable method was to modify the route line to avoid the piles obstacles. And the other solution is to demolish the original bridge structure when the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Name** & **Unit Weight (kN/m3)** & **Cohesion (kPa)** & **Friction (\({}^{\circ}\))** & **Compressive Modulus (MPa)** \\ \hline Miscellaneous fill & 16.5 & 10.5 & 22.2 & 5.0 \\ silty clay & 18.8 & 15.6 & 18.9 & 15.6 \\ cobblestone soil & 20.6 & 60.0 & 25.0 & 52.6 \\ Weathered rock layer & 21.4 & 150.0 & 30.2 & 180.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of soil strata.  
Figure 1: Elevation view of tunnel crossing viaduct foundation piles.  
shield tunnel crossed through and rebuild the bridge after the shield tunnel construction was finished. However, both the traditional methods seem to be unacceptable because of the following reasons: (1) the construction shield tunnel is located in the central city due to the limited building land and resources in the urban area, which caused problem in metro route line planning, so it is impossible to modify the metro line to detour the original bridge foundation piles. (2) the traditional demolishing and rebuilding plan cost lots of money and time, therefore this method is also undesirable.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
###### Abstract  
Quadcopter unmanned aerial vehicles continue to play important roles in several applications and the improvement of their control performance has been explored in a great number of studies. In this paper, we present an altitude control algorithm for quadcopters that consists of a combination of nonlinear and linear controllers. The smooth transition between the nonlinear and linear modes are guaranteed through controller gains that are obtained based on mathematical analysis. The proposed controller takes advantage and addresses some known shortcomings of the conventional proportional-integral-derivative control method. The algorithm is simple to implement, and we prove its stability through the Lyapunov theory. By prescribing certain flight conditions, we use numerical simulations to compare the control performance of our control method to that of a conventional proportional-derivative-integral approach. Furthermore, we use a DJI-F450 drone equipped with a laser ranging sensor as the experimental quadcopter platform to evaluate the performance of our new controller in real flight conditions. Numerical simulation and experimental results demonstrate the effectiveness of the proposed algorithm.  
Keywords:quadcopter; unmanned aerial vehicle; altitude control; position control; PID control +
Footnote †: journal: Applied
## 2 Materials and Methods
### Cell Culture and MTT Assay  
The fibroblast cell line (L929, ATCC CCL-1, The Bioresource Collection and Research Center, Hsinchu, Taiwan) was used in this study. The L929 cells were plated in 3 sets of triplicates for each investigated sample, in a 24-well culture plate (1 \(\times\) 10\({}^{4}\) cells per well density) with Dulbecco's modified Eagle minimum essential medium (DMEM, Gibco, Waltham, MA, USA), supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycinycin, and then incubated in a humidified 5% CO\({}_{2}\) incubator at 37 \({}^{\circ}\)C for 24 h. The tested GRGD peptides, over a range of concentrations (0% or control, 1%, 2%, and 5%), were added into the L929 cell culture wells and were incubated for an additional 24 h. After 24 h of incubation, 50 mL of 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazololium bromide (MTT, Sigma, Taipei, Taiwan) solution was added into each culture well and the culture plate was incubated for 4 h in a humidified atmosphere to form the formazan solvent precipitates. The precipitated formazan was solubilized in 150 mL of dimethyl sulfoxide (Sigma, Taipei, Taiwan), and absorbance was read at 595 nm using an Epoch microplate reader (BioTek Instruments Inc., Winooski, VT, USA). A short-term culturing experiment was used to investigate its acute cytotoxicity response according to ISO 10993-5 specification.
# Modeling and Predicting Human Actions in Soccer Using Tensor-SOM
While current methods like Self-Organizing Maps (SOM) and Tensor-SOM are powerful in analyzing relational data and team dynamics, there remains the question of whether interpolation-based or approximation-based neural network approaches might provide more interpretable and physically consistent frameworks for robot behavior modeling. Recent studies have explored the potential of interpolation and approximation methods, which are capable of maintaining consistency with physical constraints and enhancing the interpretability of learned behaviors. For example, methods based on approximation models have been shown to offer valuable insights in scenarios where dynamic adjustments are needed, and they may also improve the overall understanding of agent interactions in constrained environments. These approaches could be particularly beneficial in environments such as robot soccer, where motion constraints and the need for real-time decision-making are central to effective coordination and behavior generation. Several recent studies have examined these ideas. For instance, [14] have proposed an approximation-based framework for decision-making under motion constraints, while [15] explored the use of interpolation methods for improved behavior prediction and decision consistency in dynamic environments. Incorporating such methods into multi-agent systems could enhance both the efficiency and interpretability of the robots' decision-making processes. In this study, we primarily focus on the Tensor-SOM model for behavior decision-making, but future work will consider the integration of approximation-based methods to further improve the system's interpretability and physical consistency in dynamic, real-world scenarios. The main contributions of this paper are as follows:  
* We propose a behavior decision system for autonomous agents using SOM. * We introduce Tensor-SOM to analyze relational data and interpret team dynamics. * We evaluate the system's effectiveness in the context of RoboCup MSL through experiments. The remainder of this paper is structured as follows: Section 2 describes the design of agent behavior decision algorithms. Section 3 introduces SOM as a behavior decision-making method. Section 4 presents the Tensor-SOM model and its properties. Section 5 evaluates performance in terms of data completion and interpolation. Section 6 outlines the learning of input-output relationships. Section 7 reports experimental results and analysis. Section 8 concludes the study and discusses future work.
## 2 Background
[PERSON] et al. postulated that the amount of information could be expressed in terms of the amount of eliminated uncertainty [25; 26]. According to information theory, information entropy is considered from the statistical property of the whole information source. For a particular information source, information entropy is only one. Suppose the random event set is \(\{X_{i},i=1,2,\cdots,N\}\), the probability of its occurrence is \(p_{i}\), and the condition \(\sum\limits_{i=1}^{N}p_{i}=1,0\leq p_{i}\leq 1,i=1,2,\cdots,N\) is satisfied: then define information entropy \(H(X)=-\sum\limits_{i=1}^{N}p_{i}\log p_{i}\). An SAR image is composed of pixels. Pixels of different intensities occupy different areas in the image, making the image show different shapes, and different shape image areas contain different information. Because the image distribution has block structures, there is a position correlation between each pixel. Therefore, on the basis of the one-dimensional entropy, the image entropy, which can reflect the spatial property of gray-scale distribution, is introduced to form the two-dimensional entropy, considering the two-dimensional spatial property of the SAR image. Assuming an SAR image is \(I_{1}\times I_{2}\) size, and the image data have a non-negative value that is \(f(i,j)\geq 0\), the image entropy \(E(f)\) is defined as  
\[p_{ij}=f(i,j)/\sum\limits_{i=1}^{I_{1}}\sum\limits_{j=1}^{I_{2}}f(i,j)\,,E(f) =-\sum\limits_{i=1}^{I_{1}}\sum\limits_{j=1}^{I_{2}}p_{ij}\log p_{ij} \tag{7}\]  
where \(f(i,j)\) is the gray-scale value of the pixel whose coordinate position is \((i,j)\) in the image, and \(p_{ij}\) is the probability of pixel gray-scale value \(f(i,j)\) appearing in the image. The entropy rate superpixel method is an over-segmentation technique that can improve the local consistency of pixel labels. Among the existing superpixel algorithms, the entropy rate method can not only obtain superpixels of adaptive size from the global perspective, but also has the adaptive ability in the heterogeneous region and the ability to preserve local details for optical images [27; 28]. For optical images, the distance between pairwise neighboring pixels can be used to generate superpixels by the entropy rate method. Definition of pixel similarity between the two in terms of distance is as follows:  
\[d(x,y)=\left\|x-y\right\|_{2}=\sqrt{(x-y)^{T}(x-y)} \tag{8}\]  
where \(x,y\) are two pixel values in the image, and the similarity between the two pixels is calculated by Formula (8). An image is mapped to a graph G = (V, E) with vertices denoting the pixels and the edge weights denoting the pairwise similarities given in the form of a similarity matrix. The entropy rate of the random walk on the constructed graph is a criterion to obtain compact and homogeneous clusters, and a balancing function encourages clusters with similar sizes. However, the original entropy rate method is not completely suitable for SAR images due to the fact that speckle noise will severely affect the distance directly calculated between individual pixels, so an accurate and robust distance measure between neighboring pixels should be derived.
## 3 Antimicrobial Peptides (AMPs)
### Mechanisms of Action  
#### 3.1.1 Membrane-Targeting AMPs  
A commonly known antibacterial mechanism of AMPs is the non-receptor mediated membrane-lytic activity that relies on two features: net positive charge to ensure the interaction with negatively charged compounds on the bacterial envelop (e.g., lipopolysaccharideaccharides (LPS) of Gram-negative bacteria, teichoic acids of Gram-positive bacteria, and phospholipids of the bacterial membrane), and amphipathic propensity, which allow AMPs to fold into both hydrophobic and hydrophilic structures [122]. After attaching to the bacterial cytoplasmic membrane, a wide range of AMPs insert into the bilayers and subsequently cause membrane structural disruption  
Figure 5: Model of the vegetative cycle and sporulation, germination, and outgrowth of _B. cereus_. Under severe stress conditions, _B. cereus_ can initiate a sporulation process and form spores. Under favorable conditions, the spore can germinate and grow out to vegetative cells [56].  
and permeabilization. In vitro studies with artificial membranes have led to three proposed models [123; 124; 125], namely (1) the toroidal-pore (Figure 6A) that is a continuously toroidal channel formed through AMPs-induced bending of the bilayers and proposed for PGLa and magainins, (2) the carpet model (Figure 6B) where AMPs induce membrane disintegration by various transient holes like a carpet, used by AMPs like caerin 1.1 and cecropins, (3) the barrel-stave pore (Figure 6C), which is a highly ordered cylindrical water pore surrounded by peptides and is linked to hydrophobic peptides such as alamethicin.  
Recently, it was found that SAAP-148, an artificial AMP derived from LL-37, strongly disturbs the hydrophobic core, which can lead to membrane thinning and rapid permeabilization [126] (Figure 6D). It was proven that amphipathic cationic AMPs (thromboidin-1 derived peptides TC19 and TC 84, and the designed AMP BP2) can induce extensive fluidic domains in the _B. subtilis_ membrane [127] (Figure 6E), and, in addition, can cause an irregular or shrunken inner membrane of geminated spores [128] (Figure 6F). Other mechanisms have also been proposed mainly for small, unstructured, or compact peptides that cannot directly span the membrane to form pores, such as molecular electroporation, sinking raft, interfacial activity, and lipocentric pore formation models [129]. AMPs-induced membrane perturbation results in membrane depolarization, cellular contents leakage, and intracellular disruption, which further leads to cell death [123].
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Development in Smart Clothes Sensor Materials  
Researchers are working towards developing wearable sensing devices with flexibility, stretchability and physical robustness. The development of new materials and fabrication strategies are critical factors for ensuring the compatibility of mechanical and functional properties [111]. Figure 15 provides an overview of the sensor materials used for smart clothing.  
Figure 14: **(a)** The schematic of interaction of oligomer chains with \(\mu\)Ni particles. **(b)** Optical image of scar healing for the damaged sample (adapted from [5]).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensing Device** & **Sensing Mechanism/Characteristics** & **Sensing Parameters** & **Reference** \\ \hline Piezoresistive (Carbon nanotube, LM35) & Changes in microstructure of sensing & & \\  & films generates electrical resistance & & Motion detection, measuring \\  & & after applying strain while structure & temperature, pH, humidity, etc. & [107; 108] \\  & of the device is deformed. & & \\ \hline Capacitive (Planar interdigitated capacitor (IDC)) & Applied strain changes distance & & \\  & between two electrodes and thus & & \\  & capacitance is changed. & & \\ \hline Piezoelectric (as ZnO, GaN and PZT) & Electromechanical interaction in & & \\  & noncentrosymmetric crystal structure & & \\  & materials generate electric charges by & & \\  & applying external mechanical stimuli. & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different physiological parameters and their sensing mechanisms.
## 4 Methodology
### Data Acquisition
#### 4.2.3 Air Parameters  
The air parameters were extracted from SSP-L2 satellite images that comprise six pollutants: CO, \(NO_{2}\), \(O_{3}\), \(SO_{2}\), HCHO and \(CH_{4}\), shown in Figure 5. The \(NO_{2}\) concentrations are extracted using band 4 of the TROPOMI L2's UV, UV-VIS spectrometer [50]. Band 3 of the UV-VIS spectrometer is used to derive the \(HCHO\)[51], \(O_{3}\)[52] and \(SO_{2}\)[53] concentrations. Band 7 of the SWIR spectrometer is used to measure \(CH_{4}\) and \(CO\) concentrations [54].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Adapted Equations** & **Reference** & **RMSE** \\ \hline Tur & 35.121 \(-\) 14.489 (R(R3)/(R4)) \(-\) 0.911 (R8a) & [46] & 7.65 NTU \\ pH & 8.790 \(+\) 0.141 (R11) \(-\) 0.228 (R3/R4) & [47] & 3.36 \\ EC & 422.034 \(-\) 1080.365 (R11) & [47] & 228.7 mS/cm \\ chl-s & 54.658 \(+\) 520.451 (R2) \(-\) 1221.89 (R3) \(+\) 611.115 & [48] & 10.15 mg/L \\  & (R4) \(-\) 198.199 (R8a) & [48] & 10.811 \(-\) 0.682 (R1)/(R8a)) \(-\) 0.002 & [47] & 2.82 mg/L \\ DO & ((R2)/(R8a) \(+\) (B2)) & [47] & 111.92 mg/L \\ TDS & 120.750 \(+\) 264.752 (R8a/R1) & [47] & 111.92 mg/L \\ SDD & 0.2 \(+\) 1.41 (R2/R4) & [49] & 0.22 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adapted equations for water quality parameters.  
Figure 4: Hydrological and Topographical Parameters for the Rawal watershed.  
Figure 5: Air parameters for the Rawal watershed.