esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application of the Proposed Methodology
### Stress and Stiffness Analysis  
This is the next step of the proposed methodology (see H on Figure 2). The 3D model obtained from the TO was modified according to the manufacturing technology (the part will be designed as a casting). Therefore, a 3D model with technological details (E--see Figure 2) was used for stress and stiffness analyses. The model contained ribs with defined wall thicknesses, technological chamfers, etc. The boundary conditions (force and fixed constraints) were chosen similarly to the TO. The load spectrum was applied to the model. The cutting force of 40 kN in three basic directions was chosen for the load condition with maximum torque. Spring elements were applied at the point of the fixators. The stiffness  
Figure 8: Input load cases for topology optimisation. (**a**) Topology optimisation—Load Case I. (**b**) Topology optimisation—Load Case II.  
Figure 9: Siemens NX CAM tool with developed NC code.  
of these elements was set to 6000 kN/mm in the normal direction and 500 kN/mm in the tangential direction based on previous research [29]. It was also necessary to define the guideway between the individual parts in the machine model. These were the guideway of the bottom of the column on the bed, the guideway of the cross-piece on the columns, the guideway of the headstock on the cross-piece, and the guideway of the slide in the headstock. In all cases, the hydrostatic guideway was used. It was idealised in the FEM model by elements of the contact type. This idealised the thickness of the film of the hydrostatic guideway as infinitely stiff. Furthermore, the feed mechanisms had to be idealised. The feed of the bottom of the column on the bed was solved by the MASTER-SLAVE system [52], the feed of the cross-piece on the column was solved by a ball screw, the feed of the headstock on the cross-piece was solved by the MASTER-SLAVE system, and the feed of the slide was solved by a pair of ball screws. The MASTER-SLAVE system was idealised using 1D elements with defined infinite stiffness, i.e., the ball screw using 1D spring elements with a defined stiffness (corresponding to the stiffness of the screw). The wall thicknesses of the individual parts (casting) were chosen according to the results of the TO. Greater wall thickness was selected in the bottom part of the column. Weak areas (with local stress peaks) were identified using stress analysis on this model. Adjustments were made to remove these stressed areas and the structure was analysed again. First, the whole portal assembly was analysed in terms of stiffness. Then, the section containing the headstock and slide was analysed to determine the behaviour of the machine at the location of the hydrostatic guideways. The results of the performed analyses can be found in Section 4.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 3 Methodology
### Pseudo-Label Generator  
#### 3.3.1 CLIP-Guided Module  
The CLIP-Guided Module leverages the cross-modal alignment capability of the CLIP model between video frames and textual queries, providing a coarse-grained temporal constraint for pseudo-label generation to ensure semantic consistency. The overall process can be broken down into three distinct stages:  
**(1) Semantic Vector Extraction**  
The input video \(V\) is uniformly sampled into \(n_{f}\) frames across its total length \(L\), represented by \(\left\{f_{i}\right\}_{i=1}^{n_{f}}\). Every frame is paired with a timestamp \(\left\{t_{i}\right\}_{i=1}^{n_{f}}\), where \(t_{i}=\dfrac{i}{n_{f}}\cdot L\). For each frame \(f\), the CLIP model's visual encoder yields its semantic vector \(v_{i}\), formulated as follows:  
\[v_{i}=\text{CLIP}_{\text{image}}(f_{i}),\quad i=1,2,\dots,n_{f} \tag{6}\]  
Concurrently, the query \(Q\) is passed through CLIP's text encoder to obtain its textual representation:  
\[u_{i}=\text{CLIP}_{\text{text}}(Q) \tag{7}\]  
**(2) Computing Similarities**  
To gauge the semantic congruence between \(v_{i}\) and \(u_{i}\), we calculate their cosine similarity. The calculation is as follows:  
\[S_{i}=\text{cosine\_similarity}(v_{i},u_{i})=\dfrac{v_{i}\cdot u_{i}}{\|v_{i} \|\|u_{i}\|},\quad i=1,2,\dots,n_{f} \tag{8}\]  
In this formulation, \(S_{i}\in[-1,1]\), with higher values signifying greater semantic harmony between \(Q\) and frame \(f_{i}\). The sequence of similarity scores for the \(n_{f}\) frames is denoted by \(S=\left\{S_{1},S_{2},\dots,S_{n_{f}}\right\}\).  
**(3) Generating the Constrained Interval**  
Drawing on the similarity sequence \(S\), a threshold \(\tau\) is utilized to filter relevant frames by their time span. First, all frames with \(S_{i}\geq\tau\) are identified, and their minimum and maximum indices, \(i_{min}\) and \(i_{max}\), are determined according to Equation (9).  
\[i_{\min}=\min\{i\mid S_{i}\geq\tau\},\quad i_{\max}=\max\{i\mid S_{i}\geq\tau\} \tag{9}\]  
Subsequently, these frame indices are mapped to actual temporal boundaries: \(t_{cs}=t_{i_{\min}}\); \(t_{\sigma c}=t_{i_{\max}}\). Finally, \([t_{cs},t_{ce}]\) denotes the coarse-grained temporal interval guided by the CLIP model, which constrains pseudo-label generation.
# Speaker recognition Based on Dunge Beetle Optimized CNN
## 5 Conclusions and Future Work  
A DBO-CNN algorithm has been proposed for speaker recognition in this study and compared with the CNN, PSO-CNN, and SSA-CNN algorithms on a dataset of 50 people. After optimization, the performance of all models has improved. DBO-CNN has better optimization ability. It not only has the fastest convergence speed but also has the highest average accuracy, reaching 97.93%. In terms of anti-interference, DBO-CNN and SSA-CNN have similar performance, and their recognition rates are both above 50% in harsh environments of 20 dB. Based on the results, it demonstrates that the DBO-CNN has greatly improved speaker recognition compared with other optimization algorithms and also illustrates that DBO has enormous potential in optimizing CNN. However, the current work still needs a lot of improvement; for example, the model can only classify the voices of 50 people. Converting sound data into a spectrogram and importing it into the model for inference takes a lot of time.  
In future work, we will increase the size of the dataset and the number of speakers that can be identified, consider the problem of cross-domain recognition, and classify and recognize complex data sets containing multi-species sounds. In addition, the use of speaker recognition to discriminate AI-based voice synthesis technology will be the focus of future research and a synthetic approach to measuring the performance of the indexes will be used for speaker recognition [36]. Swarm intelligence is a promising and challenging science subbranch. In 2021, the Nobel Physics Award was partially granted to the work on swarm behavior and intelligence. Meanwhile, new developments from multiple fields are  
Figure 8: Recognition accuracy of four models by different signal-to-noise ratios.  
Figure 7: Accuracy of the models CNN, PSO-CNN, SSA-CNN, and DBO-CNN.  
deepening and broadening the cognition of swarm intelligence. For the swarm intelligence method based on bionic computing, some recent biological achievements, such as adaptive mutability [37] and epigenetics [38], are sure to improve the performance of speaker recognition. These sophisticated swarm optimization algorithms will be attempted for speaker recognition. The edge deployment of the proposed model will also be accelerated, especially the design based on FPGA, which will promote the application of machine learning for speaker recognition.  
Conceptualization, X.G. and Z.F.; methodology, X.Q.; validation, X.Q.; investigation, Y.Z. and Q.Z.; data curation, X.Q.; writing--original draft preparation, X.G.; writing--review and editing, P.W. and Z.F.; supervision, P.W. and Z.F. All authors have read and agreed to the published version of the manuscript.  
The first author gratefully acknowledges the financial support of this research from The Open Project of Key Lab of Digital Signal and Image Processing of Guangdong Province.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Characterization of Materials
#### 2.1.4 Chemical Characterization of Sorbent--FTIR and XPS Analyses
After the functionalization of the nitrile groups, their atomic fraction (AF) decreases (becoming negligible) with appearance of the band representative of the -C=NOH groups of amidoxime (285.6 eV for C 1\(s\) signal and 399.91 eV for N 1\(s\) signal) and reinforcement of C-NH or C-NH\({}_{2}\) atomic fractions (with little decrease in BE from 285.4 to 284.9 eV). It is noteworthy that the AF of protonated amine (N\({}^{+}\)) decreases after amidoximation, and that the BE of the protonated nitrogen signal is also shifted at the two steps of the functionalization (from 401.3 eV to 400.5 eV); a new band appears at 532.07 eV (associated with O-N bond) for O 1\(s\) signal. The sorption of Sr(II) mainly affects C-NH/C-NH\({}_{2}\) (increase in AF and shift of BEs +0.8 eV) while the C-O/C=N signal almost disappears for C 1\(s\). The N 1\(s\) signals for protonated nitrogen and C=NOH disappear, being replaced with a nitrate-like band and associated with the reinforcement of C-N, C=NH, and -NH signals. Regarding O 1\(s\) signal, the O-Ca and O-N bands disappear while a nitrate-like peak appears with Sr(II) binding at BE: 405.95 eV (AF \(<\) 7%) [59]. These results confirm the contribution of -C=NOH and -NH groups for Sr(II) binding. Figure 1: XPS spectra of APEI and derivatives (CN-APEI, AO-APEI) and AO-APEI after Sr(II) sorption (survey spectra).
# Designing and Implementing Weather Generators as Web Services
## 2 Addressing Bottlenecks of Legacy Weather Generators for Service
### Legacy Procedures for Operating Weather Generators  
This section describes the operation of weather generators. DisAg was implemented in Delphi, and predictWTD was written in Fortran. These programming languages are strong for scientific computations, however, they lack good support for cross-platform and cross-languages portability. Before running the weather generators, the programs must be recompiled to create executable programs so that they can be compatible with users' working environment. If the working environment changes, the programs will need to be recompiled. It is not however an easy task for users to find compilers suitable for their OS (e.g., mac OS and Linux) to re-compile and re-create executable programs for their needs.  
To operate the weather generators, two main input files are required as shown in Figure 1. One is a long-term historical weather data file in WTD format, which is used for calibrating the stochastic models. The other one is a parameter file (ASCII) named param_WTD.txt that provides the conditions for running the weather generator. The parameter file includes a weather station name that becomes the name of the generated weather file (WTD format), simulation period, number of realizations to be generated and SCF.  
The format of param_WTD.txt file is somewhat strict. If the file is not in the right structure, the process will not work. Users need to understand every parameter for operating the model. This is a barrier for new corners who wanted to utilize weather generators as a product. The file needs to be in the same directory with the executable program. Operating conditions for running a weather generator using a plain text format is not convenient and can easily lead to errors, e.g., file formatting mismatch. If we can bundle the weather generator operation including data input preparations as a web service, generating weather scenarios will be more convenient.
## 5 Performance Evaluation and Analysis
### GRU Performance
#### 5.2.1 GRU Model Architecture  
The architecture details shown in Table 5 indicate a two-layer GRU network followed by a series of dense layers. The GRU layers contain 150 and 300 units, respectively, enabling the model to capture complex temporal patterns in resource demand and availability.  
The model also includes a sigmoid activation function at the output layer, which is suitable for determining the optimal allocation or reallocation of resources in the V2X communication scenario. This setup allows the system to efficiently predict and manage resource allocation decisions.
## 2 Data and Methods
### Methodology
#### 2.2.5 Geobody Generation  
Seismic reservoir characterization is commonly conducted based on the integration of reservoir properties in a 3D geobody/geocellular model [22]. Geobody is a geological modeling terminology to describe the geological elements in a reservoir. As a result of the depositional environment, these elements are often distinctive, giving insight into the reservoir architecture. Geobody can be delineated or generated through facies modeling [23] and spectral decomposition volume attributes analysis [24]. [PERSON] et al. [23] used an interactive approach of petrophysical, mineral composition, well-log facies and horizon attribute analyses, unsupervised vector quantifier artificial neural network and sequential indicator simulation to model the reservoir facies. They successfully generated a geobody that delineates a fluvial fan-delta sedimentary system. The reservoir sand depositional environment, sand distribution and thinly laminated beds of fine-grained black shale and lime muddy siltstones are identified very well. Whereas [PERSON] et.al [24] successfully identified channels and defined the reservoir architecture by analyzing the 3D volume of dominant frequencies from spectral decomposition attributes.  
In this article, geobodies are generated using cutoff analysis. The geobody of reservoirs is delineated by three-dimensional mapping of the extracted petrophysical value that was selected using the discovery well cutoff value. Expectedly, the generated quantitative petrophysical geobody is able to not only spatially delineate the reservoir but also help the reservoir development, e.g., reservoir modeling and reservoir simulation.
## 2 Materials and Methods
### Determination of the Anti-Biofilm Activity of the Extracts  
The crystal violet assay [29] was applied to evaluate the biofilm-forming inhibitory potential of hawthorn extracts. A volume of 200 \(\upmu\)L of 24 h old bacterial or yeast culture (approximately 10\({}^{8}\) CFU/mL) was transferred to the wells of a 96-well polystyrene microtiter plate (Sarstedt, Numbrecht, Germany). The plates were then incubated for 4 h at temperatures appropriate for the growth of bacteria or yeast tested. Subsequently, the non-adherent cells were removed, and the plates were rinsed with physiological saline. After 10 min of drying, 200 \(\upmu\)L of fresh medium containing hawthorn extracts was added to each treated well. For bacteria, the MIC/2 extract concentration was introduced into the wells, while 120.25, 176.25, and 155.75 mg/mL of the C, S1, and S2 samples, respectively, were applied to yeasts. The positive control wells contained adherent cells and 200 \(\upmu\)L of growth medium, while growth medium containing hawthorn extracts was introduced into cell-free wells for the negative control. The plates were then incubated for 24 h at appropriate growth temperatures. After incubation, the supernatant was removed from each well, and the wells were rinsed with physiological saline. A volume of 200 \(\upmu\)L methanol was added to each well to fix the biofilm, then the plates were incubated at room temperature for 15 min. Subsequently, methanol was removed and 200 \(\upmu\)L of 0.1% (\(w/v\)) crystal violet solution was transferred to the wells. After incubation for 20 min at room temperature, the excess dye was removed by washing the plates with distilled water. Then, the bound crystal violet was released by adding a 33% (\(v/v\)) acetic acid solution. After an incubation step for 10 min at room temperature, the absorbance measurement was performed at 590 nm using a SPECTROstar Nano spectrophotometer (BMG Labtech, Offenburg, Germany). The percentage of biofilm formation was calculated from the optical density data, considering the absorbance of the positive control as 100%.
## 4 Measurement/Calibration of \(R_{s}\)
### In Situ Calibration
#### 4.2.2 Performance Reference Compounds  
The method using PRCs has been proposed to calibrate the \(R_{\mathrm{s}}\) values of the POCIS under in situ conditions [114]. In this calibration, the PRCs, as stable isotope compounds with similarphysicochemical properties to the target chemicals, were pre-packed into the absorption phase in the samplers prior to deployment. The synchronization between desorption of PRCs and sorption of the target chemical was used to calculate the \(R_{s}\) and concentration of target chemicals in an aquatic environment on the basis of the degree of PRC desorption [46; 47; 122].  
For hydrophobic organic pollutants, the corresponding deuterium substitutes or \({}^{13}\)C markers (absent in the natural environment) and other substances with similar properties to targets have usually been selected for PRCs. In this case, the sorption of target compounds by receiving phase and desorption of PRCs from the absorbed phase should be in accordance with first-order kinetics [123; 124; 125]. Theoretically, the \(R_{s}\) and concentration of target compound in a water environment might be calculated at any time point according to the desorption degree and the distribution coefficient of PRCs between sorbent and water [126; 127]. Due to the identical interference of ambient conditions on the sorption of target compound and desorption of PRCs, the error in calibration could be reduced to a certain extent. Therefore, this quantitative method has been more widely applied in the POCIS [55; 128; 129]. However, many factors should be considered when applying PRCs. Firstly, the application of PRCs would increase the relative costs for sampler preparation and analysis of the obtained extracts. Secondly, the selection scope of chemicals as PRC is limited. Finally, the reproducibility obtained by loading the PRCs into the receiving phase and the possible mass loss should be taken into account by researches [119].
## 3 Materials and Methods  
### Research Instrument  
The methodology of this study is based on a systematic stakeholder analysis (Figure 1).  
First, in order to clearly understand the discussion framework, define the scope of application of I4.0 tools, create an interview protocol, and prepare interview questions, a comprehensive literature review on the I4.0 topic was conducted [12; 53]. This methodology implies the collection and review of material from databases using clearly defined transparent rules. For this study, Google Scholar and SCOPUS were used for retrieving academic materials, while Google Search was used for non-academic resources, such as firm reports. Literature was collected by searching various AND-OR combinations of the following keys: \"Industry 4.0\", \"digitization\", \"Kazakhstan\", \"technologies\", \"enablers\", \"tools\", imple  
Figure 1: An overview of systematic stakeholder analysis stages.  
mentation\", \"critical factors\", \"barriers\", \"drivers\", \"interview\", \"survey\", \"managers\", \"stakeholders\", \"stakeholders' analysis\", \"current state\", \"expectations\", \"perspectives\". The search results of each combination were stored in an Excel database with all necessary metadata. Then, duplicates were removed, and title-abstract (TA) review analysis eliminated semantically irrelevant items. Next, 143 academic papers and 27 reports proceeded for an analysis of the Abstract, Introduction, and Conclusions. Researchers' notes were attached to the database, and each article was categorized to improve navigation. Based on the researchers' notes assessing the relevance, 40 papers and 15 reports were chosen for full-paper content analysis. The results of the literature review are presented in Section 2. Moreover, this review shaped interview questions and the protocol.  
The qualitative interview method with a semi-structured questionnaire (research instrument) was preferred because of its openness to brand new and unstructured information [54]. The interview protocol refinement (IPR) framework was used to design seven open-ended questions for the interview with experts [55].  
All of the IPR requirements were met. Firstly, interview questions had a strong relation to research objectives. Questions aimed to examine the status of I4.0 in Kazakhstan from the stakeholder's perspective. Secondly, interviews aimed to gather information in 12 specific predefined dimensions identified after a literature review.  
Inspected parameters included: The I4.0 awareness, implementation level, the impact on KPI, demanded technologies, critical factors and barriers, international experience, and forecasts. Thirdly, six research colleagues provided feedback on the questions using the think-aloud method [56]. Finally, one pilot interview with an I4.0 user company and one with a provider were conducted to test the interview protocol. Over 15 interviews, the questions' wording slightly evolved and varied depending on the respondent's role (user/provider/consultant).
# 5G-MEC Testbeds for V2X Applications
###### Abstract  
Fifth-generation (5G) mobile networks fulfill the demands of critical applications, such as Ultra-Reliable Low-Latency Communication (URLLC), particularly in the automotive industry. Vehicular communication requires low latency and high computational capabilities at the network's edge. To meet these requirements, ETSI standardized Multi-access Edge Computing (MEC), which provides cloud computing capabilities and addresses the need for low latency. This paper presents a generalized overview for implementing a 5G-MEC testbed for Vehicle-to-Everything (V2X) applications, as well as the analysis of some important testbeds and state-of-the-art implementations based on their deployment scenario, 5G use cases, and open source accessibility. The complexity of using the testbeds is also discussed, and the challenges researchers may face while replicating and deploying them are highlighted. Finally, the paper summarizes the tools used to build the testbeds and addresses open issues related to implementing the testbeds.  
5G; multi-access edge computing; V2X communication; MEC orchestrator +
Footnote †: journal: Future Internet  
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