esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Discussion of the Results  
In general, the proposed CFE encoder is able to achieve very promising results, near those that are in the state of the art. The evolution of the CFE models in Figure 3 shows a behavior that is very similar to the other systems used for comparison. In any case, these tests should be considered to be preliminary results, needing further experiments and improvements to achieve its full potential. For example, new adaptations could be studied for the decoder network, which was not the purpose of the present work.  
These are the main findings of the experiments:  
* The proposed CFE models are not able to overcome the results of the state-of-the-art methods used for comparison, as it can be seen in Table 2, although they are very close in many cases. These differences between methods have been confirmed by the approximate randomization tests, indicating that the differences of the predictors are statistically significant. However, it must be observed that these alternative methods are specifically designed for each problem, while the proposed method has shown to be generic, being able to work with text, audio and images, with minimal adaptations for each problem.
* In all the experiments, the number of iterations of the learning process was fixed for each problem (as indicated in Table 2). However, it has to be considered that the average time per iteration is not the same for all the methods. In fact, the proposed CFE encoder is approximately 1.7 times faster than the other alternatives. Thus, for a fixed learning time, the proposed solution could overcome the other methods in some cases. This can be observed in the validation measures (ACC and PER). For example, using the same learning time in the LaTeX decompilation task, CFE achieves an ACC of 96.5%, while Im2 Text achieves 96.1%. In other words, Im2 Text method needs around  
Figure 3: Evolution of the training errors (cross entropy) of the different models compared, for the three problems of interest. (**a**) Text translation. (**b**) LaTeX decompilation. (**c**) Audio transcription.  
70% more time to achieve its optimum result. A special case is the Transformer method for the problem of bilingual translation, whose average time per iteration is 4 times greater than the time of CFE; so, for the same training time, the performance achieved by CFE would be higher.
* It was observed that the proposed CFE encoder makes a better usage of the attention mechanisms [8]. The attention matrices obtained by CFE are _sharper_ than those obtained for the other methods, i.e., they present a bigger different between the elements of interest and those that are not interesting for the decoder. This effect can be observed in the attention matrices shown in Figure 4 for the bilingual translation problem. This is a very positive aspect, since it indicates that future improvements of the proposed method could benefit more from the attention mechanisms.
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 4 Covalent Organic Frameworks (COFs)
### COFs as SPME Coatings for Environmental Applications
#### 4.3.5 Extraction of per- and Polyfluorinated Alkyl Substances
The highest values obtained for the perfluoroalkylsulfonic acids were ascribed to the hydrogen bonding between the oxygen atoms of the guest molecules and the protonated nitrogens within the COF. LOQs in the 0.06\(-\)3 ng/L range were obtained, together with recoveries in the 84\(-\)113% range, and RSDs below 12%, allowing for the detection of C\({}_{8}-\)C\({}_{18}\) PFASs in the environmental sample with concentrations in the 5.4-38.6 ng/L range.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Analysis of Big Data  
Big data implies extraordinarily big, fast-moving, and diverse data collections that are difficult to handle or analyze using conventional data processing techniques. A framework known as BlockIoTIntelligence has utilized blockchain for big data analysis, as blockchains provide a decentralized approach for secure big data analysis with the help of artificial intelligence [374]. Furthermore, privacy-preserving Distributed Federated Learning by employing Blockchain (DFL-B) for preserving the integrity of the machine learning models and, thus, preventing model poisoning attacks has been proposed for secure massive data evaluation in networks generating massive data [375].
## 2 Materials and Methods
### Study Design  
A total of 256 resin composite discs (7 mm diameter and 2 mm thickness) were prepared and identified as substrate materials, onto which the repair procedures were performed. The discs were divided into 32 groups (n = 8 discs/group) according to the three main experimental factors in this study: factor 1: material, 2 groups (\(X\)-tra fil and Admira Fusion X-tra); factor 2: surface treatment, 8 groups (oxygen inhibition, matrix, Futurabond M+, Silane/Futurabond M+, Admira Bond, Silane/Admira Bond, ceramic repair system and Silane/Cimara bond); and factor 3: storage time, 2 groups (24 h and 6 months).
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
###### Abstract  
The passive detection of low-altitude signal sources is studied using an improved cross-correlation method in the time-frequency domain. A matching template is designed for signal cross-correlation, and a cross-correlation threshold is used to determine whether a signal source is present or not. An improved cross-correlation method is also proposed to estimate the direction of arrival and communication frequency of a signal source. Furthermore, the distance and signal-to-noise ratio are estimated using an energy detector. Outdoor data from a bridge in the Jimo District, Qingdao, and indoor data from a research laboratory are used for performance evaluation. The results obtained show that the proposed method can provide better passive detection of low-altitude signal sources compared to several well-known algorithms in the literature. In addition, this method is more suitable for long-distance detection.  
Keywords:signal detection; low-altitude signal source; unmanned aerial vehicle (UAV); cross-correlation; time-frequency analysis +
Footnote †: journal: Applied Sciences
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
## 2 EUV-Induced Plasma
### Electron Impact Ionization  
As mentioned earlier, the free electrons have an energy up to 76.6 eV after the first ionization events have occurred, depending of the production process. Upon collision, these electrons may ionize the H\({}_{2}\) background gas even further by electron impact ionization. Comparable to photoionization (Equations (6)-(8)), the three electron impact ionization processes playing a role in hydrogen are single ionization (see Equation (10)), dissociative ionization (see Equation (11)), and double ionization (Equation (12)).  
\[\mathrm{H}_{2}+\mathrm{e}\rightarrow\mathrm{2e}+\mathrm{H}_{2}^{+} \tag{10}\]  
\[\mathrm{H}_{2}+\mathrm{e}\rightarrow\mathrm{2e}+\mathrm{H}_{2}^{+*} \rightarrow\mathrm{2e}+\mathrm{H}^{(*)}+\mathrm{H}^{+} \tag{11}\]  
\[\mathrm{H}_{2}+\mathrm{e}\rightarrow\mathrm{3e}+\mathrm{H}_{2}^{2+} \rightarrow\mathrm{3e}+\mathrm{2H}^{+} \tag{12}\]  
Figure 9: Cross sections for single, dissociative and double photoionization of molecular hydrogen as a function of the photon energy. Figure produced using data from [53; 54; 16; 55].  
From a plasma dynamic point of view, the electrons dominate this phase, since the inertia of the much heavier ions prevents them from moving over distances that are comparable to that of the free electrons on these short time scales. The presence of a surface in contact with plasma leads to the creation of space charge regions, which are--via the Poisson equation--accompanied by strong electric fields [60]. Directly after the creation of EUV-induced plasmas (i.e., in this phase), the process towards the creation of this space charge is twofold. First, a part of the fast and highly mobile photo-electrons escape towards nearby walls [11]. The resulting charge imbalance and the associated potential well traps the remaining electrons. During this phase, the plasma potential as compared to the plasma facing surfaces can be over 50 V [21; 61].
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 2 Background
### Domain Analysis
#### 2.2.1 Incoming Dock  
The inflow of the warehouse starts at the incoming dock, and the inventory management process is usually initiated at this stage. The goods arriving here are entered into the system, either by manual check-ins or, in the case of a smart warehouse, using barcodes, RFID tags, or some other unique IDs [24]. Unique IDs for all the items, goods, or products make it easier to track them at later stages for production, packing, or dispatch. Barcodes are effective for short distances, but for longer distances, RFIDs are used, that efficiently scan the product even from a distance, hence saving time and hassle; the tags are long lasting as well, making them cost-efficient. From the incoming dock, the goods or products are sent to the storage area.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Solving Phase I (Search): Maze Exploration
#### 3.3.1 The Approach  
A general procedure for the implementation of an ant system was introduced in Section 2.3.3. The AA local path-planning-based algorithm applies the ACO probability density function at each step to determine the next best step using:  
\[P_{i}(t+1)=\frac{(c+n_{i}(t))^{\alpha}}{\sum_{j=1}^{5}(c+n_{j}(t))^{\alpha}} \tag{2}\]  
where \(P_{i}(t+1)\) is the probability of moving in direction \(i\), and \(n_{i}(t)\) is the amount of pheromone in block \(i\). The summation (\(j\)) in Equation (2) is over the five possible directions of movement (cf. Figure 6) with the 5 th direction remaining stationary. Additionally, \(c\) is the degree of attraction to an unexplored path, and \(\alpha\) is the bias in using a pheromone-concentrated path. An example of AA parameter values would be \(c=20\), and \(\alpha=2\), as demonstrated in [105].  
With AA, however, ants were noticed to be clustering together, due to the attractive nature of pheromone which makes ants follow each other with little randomness. To counter this effect, and to quicken exploration, an inverted AA (iAA) model was also developed, where the pheromone was designed to be repulsive, encouraging ants to venture into unexplored territory, by making the attraction component negative. The probability density function used for decision making in iAA is  
\[P_{i}(t+1)=\frac{(c+n_{i}(t))^{-\alpha}}{\sum_{j=1}^{5}(c+n_{j}(t))^{-\alpha}} \tag{3}\]  
With promising initial results, two new versions of iAA, namely inverted-AA with beacon initialization (iAA-B) and inverted-AA with an increased sensing range (iAA-R), were also developed. The iAA-R was developed to test the effect of a longer sensing range on the  
Figure 5: The search and rescue process can be seen as two sequential phases: Phase I, where the search is conducted in an environment; and Phase II, where the extraction of an item or person from the environment is realized (i.e., the rescue part). For details, see Section 3.2.  
speed of convergence of the solution. The workings of a basic AA model are shown in Algorithm 1. The different models differ only in terms of the Probability Density Function used to generate the roulette wheel distribution in the algorithm. A visual depiction of the decision making based on the pheromone levels for all four models is shown in Figure 6.
## 3 Preparation for Fault Localization
### Target Memory for Automotive ECU Software  
In general, memory is composed of a data section, a text section, and a stack section, depending on its intended use. The fault-localization method proposed in this paper is aimed at the data section. The definition of each memory section is as follows.  
* **Text section**: this is where the program's source code is stored; it is used only for'read' during program execution. Therefore, this section cannot be used for fault localization.
* **Stack section**: this is assigned to each function called during program execution. Therefore, considering regions in this section as potential fault locations is not helpful to developers because this section is allocated dynamically. However, it does contain information such as the Return Address (RA) for each function, the Stack Pointer (SP), and local variables. Therefore, it is possible to partition the stack section into functional units by using this information. A partitioned stack section can be used to partition the data section.  
* **Data section**: this is the region assigned to global variables. Treating regions in this section as potential fault locations means identifying a specific object file or symbol as a fault candidate, since this section is fixed and allocated by object file/symbol unit during program execution.  
For these reasons, we propose a method that can reduce the fault candidate region in the data section. In order to do this, it is necessary firstly to partition the data section. This section is used in accordance with the object file/function unit determined at source-code level. The memory map and the stack also include information about object-file unit or function unit that is determined at a high level. Therefore, we partition the data section based on the memory map and stack.
# Acoustic Impulsive Noise Based on Non-Gaussian Models: An Experimental Evaluation
## 2 Parameters Estimation
### GMM Parameters Estimation  
The GMM is a linear combination of Gaussians functions where the sum of all weight coefficients is equal to one. Thus, a random variable \(y\) with GMM distribution is defined by  
\[p(y)=\sum_{i=1}^{M}c_{i}N(x_{i}|n_{i},\sigma_{i}),\text{ with }\sum_{i}^{M}c_{i}=1, \tag{1}\]where \(c_{i}\) is the weight of the \(i\)-th Gaussian distribution function, \(M\) represents the number of Gaussian distributions in the mixture, and \(\mathcal{N}(x_{i}|\mu_{i},\sigma_{i})\) is a Gaussian distribution function given by  
\[N(x_{i}|\mu_{i},\sigma_{i})=\frac{1}{\sqrt{2\pi}\sigma_{i}}e^{-\frac{(x_{i}- \mu_{i})}{2\sigma_{i}^{2}}}, \tag{2}\]  
where \(x_{i}\) is the \(i\)-th Gaussian random variable with its expected value \(\mu_{i}\) and standard deviation \(\sigma_{i}\)[20].  
The MLE method is a way to estimate the parameters \(\mathbf{\theta}\) which specify a probability function \(f(x_{i}|\mathbf{\theta})\) of a random variable \(X\)[21]. The estimation is based on the independent and identically distributed (i.i.d.) samples \(x_{i}\) (observations) from the distribution, and a log-likelihood function, which is given by  
\[\ell(\mathbf{\theta})=\sum_{i=1}^{N}\log f(x_{i}|\mathbf{\theta}). \tag{3}\]  
Thus, the MLE chooses the model parameters \(\hat{\mathbf{\theta}}\) that maximize the likelihood function, yielding the most likely parameters to generate the observed data [22].  
The Expectation-Maximization (EM) is a method to determine the MLE of the parameters \(\theta_{GMM}\) of a GMM [23]. Therefore, assuming that the observed data is generated by \(M\) Gaussians, the estimated parameters \(\theta_{GMM}\) are \((\mu_{i},\sigma_{i}^{2},c_{i})\) for the whole set of \(M\) Gaussians.  
The EM algorithm employs an iterative procedure that is obtained by alternating the expectation step (E-Step) with the maximization step (M-Step). In the E-Step, we calculate the expected value of the log-likelihood concerning the current estimate of the distribution (estimation of \(\mu_{i}\) and \(\sigma_{i}^{2}\) parameters). In the M-Step, we maximized these expected parameters of the E-Step, also improving the estimation of \(c_{i}\). These parameters are then used to determine the new parameters in the next E-Step until convergence is detected [24].  
We selected the initial parameters using a heuristic to find centroid seeds based on k-means, and the algorithm iterates over the steps until convergence.
## 3 Results and Discussion
### Self-Assembly of SMNCs in Selective Solvent  
Finally, we induced the self-assembly of these SMNCs in a selective solvent, namely methanol. Methanol is a good solvent for silica lobes due to the inherent hydroxyl groups on the NP surface but a poor solvent for PSt brushes. We added methanol to the THF solution of SNP-5 nm-b-P (NB-PSt\({}_{80}\))\({}_{19}\) SMNCs to trigger self-assembly, followed by evaporation of THF to enhance solvent selectivity for SMNCs. This process yielded SMNC micelles with an average size of \(\sim\)213.7 \(\pm\) 37.8 nm (Figure 4a), significantly larger than the individual SMNCs. The micelles resembled spherical strawberries with a solid core (non-hollow structure) (Figure 4c). Calculated from the volume ratio of dried micelles (d = \(\sim\)213.7 nm) to SMNCs (d = \(\sim\)14.0 nm), the average aggregation number per micelle was approximately 14900, indicating a typical compound micelle structure. Additionally, a schematic was drawn to illustrate the orientation of SMNCs on the micelle surface (Figure 4d): SMNCs were packed together randomly to form compound micelles, with silica lobes preferentially oriented outward and BBP tails inward. This compound micelle structure confirms the amphiphilic properties of our SMNCs.  
Figure 3: SNP-b-P (NB-PSt) SMNCs synthesized with BBPs PCNB\({}_{87}\)-b-P (NB-PSt\({}_{80}\))\({}_{19}\) at nTMOS:n\({}_{\text{COOH}}\) = 10:1. (**a**) SEM image of dispersed SNP-5 nm-b-P (NB-PSt\({}_{80}\))\({}_{19}\). Inset shows a magnified SEM image of the selected area within the dashed-line box. (**b**) TEM image of a single SMNC. (**c**) The size distribution of SMNCs. The data are presented as statistical analysis from SEM images (orange columns) and dynamic light scattering (DLS) results for the SMNC solution (blue triangles) (\(D_{\text{h}}\) = 63.7 \(\pm\) 0.3 nm, PDi = 0.132) and BBPs (gray squares) (\(D_{\text{h}}\) = 27.3 \(\pm\) 0.2 nm, PDi = 0.173) in THF.
## 4 Conclusions  
In summary, we demonstrated the high sensing performance of metasurfaces consisting of spiral metallic disks patterned on thin PP substrates, which operate in the low-frequency region of the THz range (0.2-0.5 THz). High \(Q\)-factor localized surface plasmon dark modes were excited by placing a C-shaped resonator near the disk. Metasurfaces with optimal design were fabricated by the photolithographic technique and tested with a sub-terahertz CW BWO spectrometer. The experimental transmittance showed good agreement with the simulations. The resonance frequency shifts \(\Delta f\) arising from very thin (\(d\approx 10^{-4}\)\(\lambda\)) photoresist coatings increased with \(d\), while approaching a constant at \(d=50\)\(\mu\)m. Dark magnetic modes excited on symmetric spiral disks with 90\({}^{\circ}\) C-resonators demonstrated very high _FOM_ values, especially for the smallest thicknesses (1670 (RIU-mm)\({}^{-1}\) at \(d=0.3\)\(\mu\)m) due to a narrow resonance width. The hybrid high order mode resonances of asymmetrical densely packed spiral disks (with a shift of the disk center) showed about two times larger _FOMs_ (up to 2950 (RIU-mm)\({}^{-1}\)) then symmetrical spirals, which is comparable with the best _FOM_ values obtained for alternative metasurfaces with densely packed unit cells fabricated with a similar technique. The attained results allow us to highlight the spiral disks design as promising for bio-sensing applications in the THz range.  
Conceptualization, V.V.G.; methodology, V.V.G. and S.A.K.; software, R.R.H. and S.A.K.; validation, V.V.G., R.R.H. and P.A.L.; formal analysis, V.V.G. and S.A.K.; investigation, V.V.G. and R.R.H.; resources, S.A.K.; data curation, V.V.G.; writing--original draft preparation, V.V.G.; writing--review and editing, S.A.K.; visualization, V.V.G. and R.R.H.; supervision, V.V.G.; project administration, V.V.G.; funding acquisition, V.V.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation, grant number 18-72-00112.  
This work was done using the infrastructure of the Shared Research Facility \"Siberian Synchrotron and Terahertz Radiation Centre\" of BINP SB RAS and the Shared Equipment Center CKP \"VTAN\" (ATRC) of the NSU Physics Department. Authors thanks [PERSON]. [PERSON] and [PERSON] for the sample preparation.  
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.
## 5 Conclusions  
In this study, a U-Net based framework for the radiation prediction of the LPBF process was introduced, which includes data pre-processing, training, and application of the prediction model. Training and test datasets were built using an integrated OT monitoring system. To represent the geometry of the printed parts, contours of five layers were used as input data. In addition, to enable the prediction of radiation for large-scaled parts, the resolution of input of the network was adapted to cover the whole building platform. The results showed that the proposed framework indicates its capability to predict radiation layer-wise by the contour of the designed part. This framework provides a data-driven method, besides numerical and analytical methods, to obtain a fast result of radiation, which can be used as a quick check of thermal behavior before the real manufacturing.  
For the future work, the prediction accuracy needs to be improved. Since the current input is the contour of the designed part, other types of information, which can influence the radiation, including scan vector orientation and laser parameter configuration, should be investigated to have a more complete model. Meanwhile, different material needs to be discussed as well. Furthermore, hyperparameter tuning will be of benefit to reduce the prediction error.  
Conceptualization, S.Z. and A.J.; methodology, S.Z.; software, S.Z.; validation, A.J. and L.J.; formal analysis, A.J. and L.J.; investigation, S.Z.; resources, S.Z.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, A.J. and L.J.; visualization, S.Z. and A.J.; supervision, J.H.S.; project administration, J.H.S.; funding acquisition, J.H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC-2023 Internet of Production-390621612.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The raw/processed data needed to reproduce these findings cannot be shared publicly at this time, as they are also part of an ongoing study.  
The authors declare no conflict of interest.  
Figure 9: Prediction results by training data of the first 150 layers and full training data at layers 80 and 300.
## 3 Joint Power Control and Resource Allocation  
A sub-optimal algorithm to solve OP1 is proposed, which is named joint power control and resource (JPCRA) allocation. It is based on solving two degenerated optimization works: power allocation with power control (PAPC) consideration and jointly optimum time allocation and power splitting ratio (JoTAPS) with a fixed transmit power. The final results can be obtained by using the alternative optimization algorithm based on PAPC and JoTAPS. This technique is described in detail next.