esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
## 3 Methods
### Description of the Swarm
#### 3.1.3 Simulator and Environments
The fundamental advantages of this type of generation are that practically all the levels generated are different. This is a very simple strategy to implement, and the results, as will be seen later, are realistic. More specifically, the function used to generate all these environments is presented in Algorithm 1. Initially, function RandomMapWithEmptyCells generates a random map where \(Pe\) of the total cells will be empty. Once the random map has been generated, we apply the rule of the cellular automaton through the procedure ApplyCellularAutomataRule for each map cell. Furthermore, we must adapt the map with JoinCaves so that the different generated areas are connected to each other and a dilatation process with a Disk of size \(D\) to produce more uniform environments. Figure 1 presents a group of maps generated with this algorithm. ```
1:functionGenerateEnvironment(size, \(Pe,D\))
2:\(M\leftarrow\textsc{RandomMapWithEmptyCells}\) (size, \(Pe\))
3:SetBorders(\(M\))
4:for each cell in \(M\)do
5:ApplyCellularAutomataRule(\(M,cell,4,5\))
6:endfor
7:JoinCaves(\(M\))
8:\(M\leftarrow\textsc{Dilation}\) (\(M,\textsc{Disk}(D)\))
9:SetBorders(\(M\))
10:returnM
11:endfunction
12:procedureApplyCellularAutomataRule(\(M,cell,a,b\))
13:\(S\leftarrow\textsc{GetNeighbhorhoodSquare}\) (\(cell\))
14:ifAdjacentWall(\(S\))\(<a\)then
15:\(M[cell]\leftarrow\emptyset\)
16:endif
17:ifAdjacentWall(\(S\))\(>b\)then
18:\(M[cell]\leftarrow\textsc{Wall}\)
19:endif
20:endprocedure
```  
**Algorithm 1** Unestructured Environment Generator  
Finally, once a map has been generated, it is used with our physical simulator to develop the simulation tasks required to thoroughly test our proposed behaviors. In Figure 2, an example of a map and its 3D visualization, together with the first steps of a deployment behavior, are presented. Figure 1: Example of map generation using Algorithm 1. The first parameter is the randomness index required by the automaton \(Pe\) (the higher the index is, the greater the generation of roads). The second parameter indicates the intensity of a morphological expansion applied to the map for smoothing \(D\). The map size is 15 m \(\times\) 15 m.
## 4 Results  
The combined side-by-side mosaic of bi-gratings-1 to -6 enabled monochromatic retroreflection over a broad angular range. As shown in Figure 6, the mosaic sample maintains consistent retroreflection of the target wavelength (785 nm) with significant intensity as the angle of incidence is varied from 5\({}^{\circ}\) to 10\({}^{\circ}\). This confirms the device's ability to achieve efficient retroreflection across multiple angles, validating the performance of the bi-gratings for practical use in narrowband optical applications.  
Samples Bi-grating-7, Bi-grating-8, and Bi-grating-9 were designed to retroreflect distinct wavelengths at specific incidence angles. The results, shown in Figure 7, demonstrate successful multi-wavelength retroreflection. Figure 7a presents the normalized spectral response, while Figure 7b shows the observed response for each sample. The results confirm that each sample retroreflects a unique wavelength: 550 nm (green) at 5\({}^{\circ}\), 590 nm (orange) at 10\({}^{\circ}\), and 650 nm (red) at 15\({}^{\circ}\) incidence. These results indicate precise control over retroreflected wavelengths, each corresponding to the intended angle of incidence.  
The surface plasmon resonance imaging (SPRi) results in Figure 7c further corroborate these observations, showing distinct wavelengths meeting SPR conditions at their respective incidence angles. This confirms that the bi-gratings are functioning as intended, with each bi-grating selectively retroreflecting its assigned wavelength at the specified angle.  
Figure 5: Experimental setup for the monochromatic retroreflection measurements. The setup includes a horizontally polarize and collimated white light source illuminating the samples through a semi-transparent mirror, with retroreflected light collected in reflection mode using a spectrometer.  
Figure 6: Monochromatic retroreflection performance of samples Bi-grating-1 to Bi-grating-6. (**a**) 3D graph illustrates the retroreflection of the target wavelength (785 nm) across a range of incidence angles (5\({}^{\circ}\) to 10\({}^{\circ}\)). (**b**) A 2D graph showing the spectra at key retroreflection angles (5\({}^{\circ}\) to 10\({}^{\circ}\)), with each spectrum vertically offset by 25 a.u. for better visualization.
# Porosity Elimination in Modified Direct Laser Joining of Ti6 Al4V and Thermoplastics Composites
## 1 Introduction  
Hybrid component manufacturing for lightweight constructions has attracted much attention due to the increasing demands in the automotive [1], aerospace [2] and biomedical industries [3]. Hybrid components consisting of plastic and metal can reduce product weight, tailor product properties and enhance the flexibility in product design. Short glass fiber reinforced polyamide-66 is a promising candidate for lightweight construction applications due to the required lightweight and the expected thermomechanical properties [4; 5]. Comparing to conventional joining methods such as adhesive bonding [6] and mechanical fastening [7], the laser joining process shows more flexibility without weight addition or contamination for environment [8]. A variety of laser-assisted joining methods have been developed for bonding plastic to metals. [PERSON] produced joints of steel and glass fiber reinforced polyamide (PA6-GF30) by a laser-assisted metal and plastic joining (LAMP) process, and obtained a shear strength of less than 30 MPa [9]. Based on laser surface texturing and laser irradiation joining processes, [PERSON] proposed a thermal joining method for bonding thermoplastics (PA6, PA66-GF30) to aluminum; the fracture strength of the produced Al-PA66-GF30 joints reached around 20 MPa [10].  
However, laser joining process induced micro-pores inside plastic are a major concern. It has been reported that the bubbles caused by gas in plastic result in the formation of micro-pores aftersolidification, which will have a negative effect on joint strength [11; 12]. [PERSON] [13] revealed that the laser-induced shrinkage porosity in the joint of carbon fiber reinforced polymer (CFRP) and steel could be suppressed by laser fabricating protrusions on the steel surface to change the heat conduction path during the LAMP process. [PERSON] developed an ultrasonic-aid laser joining (U-LAMP) method for direct bonding of polyethylene terephthalate (PET) and titanium, and most of the laser-induced bubbles in joints were effectively eliminated. The obtained joint strength was improved, i.e., was four times greater than that prepared by conventional LAMP method, due to the reduction of the number of bubbles in the joints [14].  
In this work, the shear strength of the Ti6 Al4V-PA66-GF30 joints produced by the conventional LAMP process was examined carefully. It was confirmed that the joint strength was limited by the micro-pores near the melted zone boundary of PA66-GF30. To eliminate the micro-pores to a large extent, a simple direct laser joining method has been proposed in which the clamping configuration is optimized. Results show that only very few small micro-pores are generated in the joints produced by the modified laser joining method, and that the fracture strength of the joints is significantly increased from 13.8 MPa to 41.5 MPa, while the highest value we found in the literature was only around 20 MPa. Moreover, failure mechanisms of the produced Ti6 Al4V-PA66-GF30 joints have been investigated.
## 2 Target Structure and Numerical Modeling
### Finite Element Model Development  
Finite element modeling was performed using the general-purpose finite element program LS-DYNA [24]. For the deck model, 8-node solid elements, 4-node shell elements, and 2-node beam elements are used for the concrete slab, steel girder, and reinforcing bars, respectively. The deck model consists of 288,332 nodes and 198,550 elements, and the edge lengths range from 0.05 to 0.15 m. The concrete slab of the model is divided into 5 layers of elements. Stay cables are modeled as solid round bar shapes with 8-node solid elements. The numerical models of the cable #37, 53, and 68 consist of 87,362, 39,668, 73,205 nodes and 77,686, 35,316, 65,232 elements, respectively. The overall edge length ranges from 0.10 to 0.50 m, and the finer mesh with 0.02 to 0.05 m of edge length is generated at the region of the application of the blast load. In the pylon model, there is a concrete part and a reinforcing bar part. The concrete part and the reinforcing bar parts consist of 8-node solid elements and 2-node beam elements, respectively, and the pylon model has 1,185,084 nodes and 917,438 elements. The section submitted to the blast load was modeled with smaller element sizes of 0.05 to 0.20 m. The remaining sections were configured using element sizes of approximately 0.5 m. For the rebars, the specifications in Table 2 were applied. The composite conditions for the concrete and rebars were implemented using the *CONTRAINED_LAGRANGE_IN_SOLID command. The contact condition between the steel girders was applied considering the case of a large, load-induced deformation, using the *AUTOMATIC_SINGLE_SURFACE command. Figures 3-5 illustrate the finite element models of the superstructure, cables, and pylon.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Part** & \multicolumn{2}{c}{**Thickness (mm)**} \\ \hline Edge Girder & Upper flange & 50 \\  & Lower flange & 50 \\  & Web & 22 \\ \hline Center Stringer & Upper flange & 30 \\  & Lower flange & 20 \\  & Web & 12 \\ \hline Crossbeam & Upper flange & 30 \\  & Lower flange & 30 \\  & Web & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Steel girder details.  
Figure 3: Deck model: (**a**) slab, (**b**) steel girder, and (**c**) rebar.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Part** & \multicolumn{2}{c}{**Vertical Rebar**} & **Lateral Rebar** \\  & **Diameter and Spacing (mm)** & **Diameter and Spacing (mm)** \\ \hline Deck & H19@150 & H16@150 \\ pylon (CB1) & H35@125 & H22@100, H19@250 \\ pylon (CB2, CB3) & H29@125 & H22@100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rebar details.
## 2 Methods
### Feature Extraction and Normalization  
In this article, we find it reasonable to consider the technique of cepstral analysis for feature extraction because it allows us to separate excitation from the vocal tract. Thus, we adopt Fourier cepstra at each frame that forms the input sequence \(X=[X_{1},\ldots,X_{n},\ldots,X_{N}]\) of the Seq2 Seq model, where \(N\) defines the frame number of the ES signal.  
The real Fourier cepstra of the esophageal and target speech are obtained by computing the inverse Fourier transform (IFFT) of the logarithm of the magnitude short-time spectra. The mathematical formula for the extraction of acoustic features is given by the following equation:  
\[C[n]=IFFT(Log|FFT(x[n]*H[n])|) \tag{1}\]  
where \(H(n)\) is a normalized Hamming window [31] of length equal to 512 in this work.  
For each time frame, the linguistic contents are encoded into a set of coefficients: vocal tract cepstral vector as \([vt_{0}\ldots v_{32}]\), cepstral excitation as \([ex_{33}\ldots ex_{256}]\), and phase coefficients as \([ph_{0}\ldots ph_{256}]\).  
In the normalization step, we normalize the acoustic features in order to obtain a standard normal distribution and to control the amplitude of the gradients during training. For each component \(x_{i,n}\) of index \(i\) at frame \(n\), we subtract the mean and divide the result by the standard deviation according to Formula (2).  
\[x^{\prime}_{i,n}=\frac{(x_{i,n}-\mu_{i})}{\sigma_{i}} \tag{2}\]  
where \(\mu_{i}\) and \(\sigma_{i}\) are, respectively, the mean and the standard deviation (stdev) of the \(i\)th component in all frames of the training sample. \(x^{\prime}_{i,n}\) represents the normalized vector.
## 4 ASK Modulation and Demodulation
### ASK Modulation for Cochlear Implants  
The electronic system of the modulator and demodulator is replaced by a NAND gate, diode and RC with CMOS transistor to control the carrier signal for cochlear prostheses [34]. This system used five inverters in order to satisfy the constraint of integration and to generate an oscillation operated carrier frequency of 20 MHz. Recently, [PERSON] _et al_. [35] proposed a monostable circuit for low-power CMOS ASK system with clock and data recovery for cochlear implants integrated under 0.18 \(\upmu\)m CMOS technology. Pulse width modulation (PWM) signals with delay-locked loops (DLL) are used to recover and resynchronize the clock and data of the system [36]. However, the expense of increased area and overhead power are problems in this system.
# Structural Evaluation of Cable Bolts under Static Loading
## 2 Literature Review
### Testing of Ground Support Systems
#### 2.3.4 Numerical Analysis  
Numerical analysis has been utilised in studying and developing rock burst support systems since the 1970s. In 1972, the first finite different method model was developed to simulate the elastic pulse propagation problem in the Split Hopkinson pressure bar technique [39]. In the same year, [PERSON] (1972) [40] utilised finite element analysis to study and model pillar bursts, allowing him to predict probable rock burst locations. In 1979, the first boundary element method was carried out to propose the complete plane strain concept and used to study pillar bursts [41]. Since the introduction of multiple different numerical methods, numerical approaches have been classified into three approaches, namely continuum, discontinuum and the hybrid approach [42]. Due to the complexity regarding rock burst phenomena, [PERSON] et al. (2021) [8] stressed the importance of selecting the correct numerical analysis methodology to achieve accurate results, where the specific engineering problem should define the numerical method selected. Thus, [PERSON] et al. (2021) [8] summarised the strengths and weaknesses of each numerical methodology, which has been adapted below in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Numerical Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Continuum Method (BEM)} & Capability of 3D modelling & \multirow{4}{*}{Normally elastic analysis only} \\  & Allow the rapid assessment of & \\  & stress concentration, & \\  & deformation and designs & \\ \hline \multirow{4}{*}{Continuum Method (FEM, FDM)} & Capability of 3D modelling & \multirow{4}{*}{Input limitations, e.g., some critical} \\  & Easily handle material & \\  & heterogeneity and geometric & Interfaces can model simple structures, \\  & nonlinearity & but are not suitable for highly jointed \\  & Simulate complex behaviour & block media \\  & of rock/rock masses with & Rich experience in using numerical \\  & various constitutive models & analysis is essential \\  & Allows dynamic loading & Calculation time increases \\  & & exponentially with number of elements \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of numerical methods in modelling rock burst mechanism.  
Since the introduction of these analysis types, numerical analysis has become extremely popular due to its advantages of being cost-efficient and flexible, as it does not require specific testing facilities to carry out. Furthermore, as it is not a destructive form of testing, unlike blast, drop and the double shear tests, it is a safe methodology that can be easily repeated. However, numerical analysis results are only valid if verified using analytical and experimental data. Furthermore, similar to laboratory tests, numerical analysis is forced to make assumptions to produce accurate results, and thus it struggles to account for the in situ stresses of rock and the exact loading pattern of rock burst loading [8].
###### Abstract  
Many people admire the Japanese language and culture, but mastering the language's writing system, particularly handwritten kanji, presents a significant challenge. Furthermore, translating historical manuscripts containing archaic or rare kanji requires specialized expertise. To address this, we designed a new model for handwritten kanji recognition based on the concept of cross-language transfer learning using a Preact ResNet-18 architecture. The model was pretrained in a Chinese dataset and subsequently fine-tuned in a Japanese dataset. We also adapted and evaluated two fine-tuning strategies: unfreezing only the last layer and unfreezing all the layers during fine-tuning. During the implementation of our training algorithms, we trained a model with the CASIA-HWDB dataset with handwritten Chinese characters and used its weights to initialize models that were fine-tuned with a Kuzushiji-Kanji dataset that consists of Japanese handwritten kanji. We investigated the effectiveness of the developed model when solving a multiclass classification task for three subsets with the one hundred fifty, two hundred, and three hundred most-sampled classes and showed an improvement in the recognition accuracy and an enhancement in a number of recognizable kanji with the proposed model compared to those of the existing methods. Our best model achieved 97.94% accuracy for 150 kanji, exceeding the previous SOTA result by 1.51%, while our best model for 300 kanji achieved 97.62% accuracy (exceeding the 150-kanji SOTA accuracy by 1.19% while doubling the class count). This confirms the effectiveness of our proposed model and establishes new benchmarks in handwritten kanji recognition, both in terms of accuracy and the number of recognizable kanji.  
deep learning; multiclass classification; language recognition; ResNet; Preact ResNet-18; computer vision; OCR; transfer learning; cross-language transfer learning; kanji  
Preact ResNet-18 Architecture for Improving Kanji  
Recognition Accuracy and Enhancing a Number of Recognizable Kanji. _Sci._**2025**, 15, 4894.  
10.390/app15094894
## 4 Conclusions  
In conclusion, we targeted the intracellular accumulation of (p)ppGpp, a bacterial stringent-response-signaling molecule involved in the insurgence of persisters, a form of phenotypic AMR, and in bacterial virulence. New chemical entities with antimicrobial activity targeting non-essential pathways, such as (p)ppGpp signaling, are urgently needed to fight and prevent antimicrobial resistance.  
We performed an extensive structure-based _in silico_ fragment screening on the synthetase site of the bifunctional enzyme Rel\({}_{Seq}\), selecting three main chemotypes. Protein-fragment interaction experiments evidenced several low mM affinity binders. In particular,the aminobenzic acid scaffold showed a marked synthetase domain selectivity and was therefore selected for the rational design of enzyme inhibitors that will be described in due course. Potent and selective Rel inhibitors will enable to shed light on the role of (p)ppGpp signaling in persisters' formation and pave the way to low-selective-pressure antimicrobial therapeutic approaches.  
The following are available online at [[https://www.mdpi.com/article/10_3390/molecules27103097/s1](https://www.mdpi.com/article/10_3390/molecules27103097/s1)]([https://www.mdpi.com/article/10_3390/molecules27103097/s1](https://www.mdpi.com/article/10_3390/molecules27103097/s1)), Figure S1: Superposition of X-ray vs. Hip312 best pose GDP, Figure S2: Superposition of X-ray vs. His312 best pose GDP, Figure S3: Best poses of the three singletons selected from the His312 grid, Figure S4: Tyr308-fragment centroids distances monitored during MD simulations. Figure S5: STD-NMR of **B1** with Rel\({}_{Seq}\) (1-385), Figure S6: STD-NMR of **A2** with Rel\({}_{Seq}\) (1-385), Table S1: Databases used for the virtual screening campaign, Table S2: 3D Datasets used in the VS workflow and enrichment factors calculated for each chemotype, Table S3: Fragment datasets obtained by using PubChem database, Table S4: Docking results for the fragment sets into HIP312 and HIS312 models.  
Conceptualization, S.S.; methodology, S.S., L.S. and M.C.; validation, S.S., M.C. and L.S.; investigation, C.C., L.S., M.M. and F.V.; writing--original draft preparation, S.S., C.C., M.C., L.S. and F.V.; writing--review and editing, S.S. and M.C.; visualization, C.C. and F.V.; supervision, S.S.; project administration, S.S. and M.C.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (ERACHRON project, grant agreement no. 758108). The APC was funded by the ERC (grant no. 758108).  
Not applicable.  
The data presented in this study are available upon request to the corresponding author.  
The authors would like to thank [PERSON] for the administrative support.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.