esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Given that the printing performance reflects the temperature behavior of the bioinks and that we obtained clearly defined results, we assume no serious change in printing behavior. The cell-laden grids were incubated for 2 weeks to determine the viability of the embedded cells. We observed viabilities exceeding 91.2% even after 14 days, which confirms the mixing protocol is compatible with the printing method. Previous studies with gelatin and its derivates as carriers (5% GelMA/8% gelatin) also achieved high cell viabilities up to 7 days and reported good compatibility with the printing process [7]. [PERSON] et al. [10] also observed very high viabilities with 10% GelMA as gelling agent. However, if the gelling agent content was too high it interfered with cell homeostasis (30% GelMA: 62.7-70.4% viability) [7]. [PERSON] et al. [6] also found some consequential damage after printing due to the increasing extrusion force for more viscous hydrogel samples, using two gelling agents (4% gelatin and 3-4% alginate). However, they reported only -50% viability after day 15, which may reflect an inadequate nutrient supply because the initial cell density in the bioink was too high. Other researchers also report a strong dependence of cell viability on gelling agent content and composition [5]. Besides the complexity of the surrounding material, the supply conditions for the cells have to be appropriate. Mass transfer and cell consumption models can be used to ensure sufficient nutrient supply [15]. Further, more detailed studies of cellular metabolism and functionality over time should be performed. The long-term survival of cells in encapsulated constructs is necessary to establish cell-cell signaling and regain functional tissues, such as pancreatic islets [32]. By day 7, we observed the formation of large cell aggregates with the appearance of spheroids. [PERSON] et al. [6] also observed this form of cell-cell interaction but at the earlier stage of 4 days after bioprinting. We therefore assume that cell expansion and cell-cell signaling are possible using our bioink. Further studies should focus on the morphological structure of our spheroids, functionality, metabolism and dose-response experiments to determine their properties as a potential 3D cell culture study model.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 5 Simulation Results and Analysis
### Sound Pressure Comparison at a Point Of Interest  
In this section, we reconstruct the sound field with the captured time domain spherical harmonic coefficients at a point in the region of interest, and compare it with the desired sound field at the same point of interest. We use a signal containing three frequency components of 600 Hz, 850 Hz, and 1300 Hz. Figure 8 shows the desired sound pressure and the reconstructed sound pressure calculated by \(a_{nm}(\
u)\) at the point \([-0.13,0.07,0.02]\) m and \([-0.03,0.01,0.1]\) m inside the region of interest in time domain. The desired sound field has been manually delayed for 272 samples to match the group delay of the reconstructed sound field, where the details of this delay will be shown in Section 5.5.  
We note here that when reconstructing the sound-field with (6), we face the problem that at a point \(\mathbf{x}=(r,\theta,\phi)\) where the radius \(r\) is very small, the filter \(p_{n}(\
u,r)\), whose filter length dependents on \(rF_{s}/c\), is too short to perform efficient filtering. To overcome this problem, we up-sample the obtained \(a_{nm}(\
u)\) with a rate of \(R_{Q}/r\) and construct corresponding \(p_{n}(\
u,r)\) with the same length of \(L_{p}=2*R_{Q}F_{s}/c+1\). We then down-sample the resulting \(b_{nm}(\
u,r)\) with a rate of \(r/R_{Q}\) to keep the sampling frequency consistent with \(F_{s}\). We can see that the obtained \(a_{nm}(\
u)\) by our proposed method can successfully reconstruct the sound pressure at a point inside the region of interest with a tolerable error. This supports that our time domain coefficients contain certain spatial information of the sound field that the sound pressure at an arbitrary point inside the region of interest can be properly calculated with the measurements only being taken on the boundary of the region.
## 4 Results and Discussion  
Extensive experiments were conducted to evaluate the effectiveness of different embedding strategies and model architectures for social media spam detection, comparing the performance of embeddings integrated into LSTM models with that of standalone textual embeddings, specifically BERT and ELMo. A variety of Python libraries, including TensorFlow, Keras, and Scikit-learn, all within the Google Colab environment, aided in the development and training of these models. The efficacy of these models was evaluated on two distinct spam detection datasets: one comprising Twitter data and the other containing YouTube comments. Both datasets feature text classified into \"spam\" and \"quality\" categories.  
The Twitter and YouTube datasets were utilized to train a total of five models with different embedder-architecture combinations. Rigorous hyperparameter tuning was performed prior to the final model selection. The evaluation metrics of precision, recall, accuracy, and F1-score were computed on the test set. The tests showed that contextualized embeddings consistently performed better than word vectors. This shows that they can pick up important semantic signals and accurately display social media text. Though the RNN models with tuned hyperparameters achieved respectable performance, the ELMo contextual embedder with a logistic regression classifier emerged as the top performer. On the Twitter dataset, ELMo obtained 90% accuracy, 91% precision, 89% recall, and 90% F1-score. The accuracy of the YouTube comments reached 94%, with 98% precision, 88% recall, and 93% F1-score. This demonstrates the significance of contextual awareness and the transfer learning capacity of pretrained language models for enhanced generalization. The comparative assessment provides valuable insights into tailoring embedding strategies and model components for the unique challenges posed by multiplatform social media spam detection in real-world deployed systems.
## 4 Antibacterial Nanotheranostics
### Theranostic Nanoparticles (NPs) and Nanofibers
#### 4.1.2 Nanoparticle-Mediated Therapeutic Antibacterial and Antibiofilm Actions
They attributed the prolonged inhibition of _Staphylococcus epidermidis_ (60 days with seven exposure cycles) to the synergetic (Ag NPs and titanium substrate) inhibition of bacterial adhesion via downregulation of the expression of biofilm-associated genes (_icaA_ and _icaD_). These _ica_ genes are virulence markers and mediate the full slime (EPS) production of _S. aureus and S. epidermidis_[174]. NPs could inhibit biofilm formation by preventing initial bacterial adhesion by targeting the cell-surface-associated adhesins [170]. The bacterial surface charge is anionic in both Gram-positive bacteria, owing to the contained teichoic acids, and Gram-negative bacteria, owing to the contained lipopolysaccharidesaccharides in the extra outer membrane [175]. Therefore, NPs could prevent bacterial-surface adhesion and cell-cell adhesion by electrostatic interactions [170]. For instance, carboxymethyl chitosan/amorphous calcium phosphate nanocomplexes decreased the adhesion and biofilm formation of _S. mutans_ and _Streptococcus_ on the enamel surface via electrostatic interactions. The cationic amino groups in the nanocomplexes neutralized the anionic bacterial surface charge, decreasing bacterial adhesion to surfaces. The nanocomplexes enhanced the flocculation of particles in the solution, reducing the cell-cell adhesion [176]. [PERSON] et al. [177] have manufactured theranostic Au@Ag core-shell NP-decorated silicon nanowires with therapeutic antibacterial properties against _S. aureus_ and _E. coli_ via physical stress and chemical effects. Bacterial sensing properties were facilitated in combination with laser-induced breakdown spectroscopy, quantifying the captured bacteria with a low detection limit. Nanowires offered shear stress and binding sites for bacterial cells and large-sized extracellular organelles (e.g., flagella). Au@Ag NPs offered other binding sites for small-sized extracellular organelles (e.g., fimbrie) or membrane proteins. Au cores improved the antibacterial activity of Ag shell atoms. In the same track of preventing bacterial adhesion and biofilm formation, an adhesive containing Ag NP shots releasing Ag\({}^{+}\) ions inhibited the growth of _S. mutans_ biofilms. Ag\({}^{+}\) ions downregulated _S. mutans_ glucosyltransferase (synthesize extracellular glucans essential for bacterial cell adhesion and biofilm formation) gene expressions (_gtf_B, _gtf_C, and _gtf_D) [178]. Ag NP shots could also prevent bacterial biofilm formation by the downregulation of QS-regulated genes, especially those encoding the secretion of virulence factors as detected in _P. aeruginosa_ biofilms [179]. We believe that further studies that elaborate on the downregulation by genetic and epigenetic mechanisms in biofilms by NP shots or ions will need to be undertaken.
## 4 Discussion
### Spontaneous Monopolar Self-Coagulation  
In Figure 25, the simulated axial profiles of the net chemical source of electrons in Ar/SF\({}_{6}\) plasma at different simulating times are shown. It is seen that in the periphery of coagulated structure, the chemical source is negative, and it becomes more negative with time. In Section 4.2.2, it is shown that the potential collapses at increasing the time due to the decouple of electrons from ions. The collapsed potential therefore provides the free diffusion component. Together with the negative source discovered here, the self-coagulation of electrons is formed in the periphery of the coagulated structure. It is noted that the electrons self-coagulation herein, together with the re-self-coagulations of minor species in Section 3.4, is a spontaneous (i.e., non-advective) type since it has not experienced the pushing role of an ambipolar potential. In addition, the self-coagulations of electrons and minor species are also monopolar due to the decoupling feature. The formula of spontaneous and monopolar self-coagulation is described in Equation (51) and it generates the delta function at steady-state, as illustrated by the self-coagulation theory in Section 4.1. Herein, \(n\) is the species density that is to be coagulated, and \(D\) and \(v_{rec}\) are the free diffusion coefficients of this species and its recombination frequency, respectively. It is seen that this type of self-coagulation does not create a novel plasma-collective interaction scheme anymore due to its decoupling feature.  
\[\frac{\partial n}{\partial t}-D\
abla^{2}n=-n\
u_{rec}. \tag{51}\]  
Figure 23: Simulated density peaks of all considered _anion species_ in Ar/SF\({}_{6}\) plasma, by means of fluid model at a simulating time before the collapse happens. The discharge conditions are the same as Figure 4. The self-coagulated density peak of F\({}^{-}\) and the sum of peaks SF\({}_{6}\) – and SF\({}_{5}\) – are the closest to each other, hence treated as the point-charge models (_negative_ type) with equal charge amount.  
Figure 24: (**a**) Formula of expelling and the Coulomb’s force between two positive point-charge models. Herein, \(Q_{1}\) and \(Q_{2}\) are the charge amounts of two point-charges and \(Q_{T}\) is the total charge amount of them. \(\epsilon_{0}\) and \(l\) are the vacuum permittivity and the distance between them. (**b**) A function, \(f(x)\), is deduced from panel (**a**) and its curve at the assumed constant, \(Q_{T}=10\) (\(a.u.\)), is plotted. As seen, the extremum is found when the total charge amount is averaged between the two point-charges.  
Figure 25: Simulated axial profiles of net chemical source of electrons at three simulating times, \(10^{-5}\) s, \(10^{-4}\) s, and \(10^{-3}\) s, given by fluid model in Ar/SF\({}_{6}\) plasma. The discharge conditions are the same as in Figure 4. At later times, \(10^{-4}\) s, and \(10^{-3}\) s, the negative chemical source and quasi-free diffusion (refer to the potential collapse in Figure 19) consist of its own monopolar self-coagulation in the yellow-high lightened periphery of semi-circle coagulated structure.
## 4 Data Cleaning and Discretization
### Alignment of the Point Cloud Dataset  
One of the most critical and complex tasks involved the spatial alignment of the partial datasets. These comprised the exterior survey and the two internal levels of the structure. While the interior levels were separately captured using the mobile laser scanner, photogrammetric reconstruction managed to produce a unified model, thanks to the large central gap in the vault, which allowed visual continuity between the lower and upper areas. This enabled the algorithm to generate a single, coherent volumetric reconstruction.  
The alignment process was guided by control points obtained with the total station. These included coordinates from both the external facades and several internal locations, notably the station's own placement within the interior.  
A number of challenges were encountered in acquiring common reference points. The narrowness of the entrance, the steep slope immediately surrounding the tower, and the deteriorated state of the stairs to the upper level posed significant obstacles to the accurate positioning of the total station, making data acquisition a laborious task at times.  
Ultimately, once the respective models were loaded and the common reference points were introduced into each dataset, we were able to achieve a coherent alignment of the complete survey. This allowed the generation of accurate sectional views, facilitating subsequent analyses such as wall thickness measurement and interior-exterior spatial relationships.
## 1 Introduction  
With the development of new approaches of nanoparticle immobilization on polymer surfaces, especially those driven by forward-directed scattering forces in colloid solutions [1], the need to thoroughly investigate laser to polymer interaction in a water environment becomes of fundamental importance. Many theoretical models have been suggested to describe the processes at the water-polymer interface during laser treatment, however, experimental studies, addressing this issue in detail, are either missing or date back to early 1990s, which hinders comparison of obtained data to modern analytical outputs of present science. Several models of laser heating of polymers in air have been suggested so far. These include photothermal models [2; 3], especially for higher wavelength radiation (such as for infrared lasers). Contrary to that, in case of UV lasers, models based on both photothermal and photodissociative or photochemical breakdown have been reported [4; 5]. The photochemical model fundamentals lie in the chemical kinetics of bond dissociation and are coupled with the processes of photothermal decomposition of material. Some of these models also take into account dynamic attenuation of radiation by the expanding plasma plume [6]. Besides these, molecular dynamics is also frequently used to simulate laser ablation of polymers [7]. Although the fundamentals of the laser-matter interaction remain the same as in air even in the presence of water, such environment can have a significant effect especially to the heating of the polymer and in turn may result to the development of surface structures, which morphology is far away from those, typically observed in case of laser processed materials in air environment (LIPSS) [8; 9]. In particular, the phenomena of plasma formation, its expansion, and subsequent radiation attenuation effects, are greatly influenced by the specific type of confining media [10].  
In this work, we report on investigation of surface morphology and chemistry of polyethylene terephthalate (PET) after underwater treatment with excimer KrF laser in broad scale of applied laser fluencies (4-20 mJ\(\cdot\)cm\({}^{-2}\)) and operating voltages (19-29 kV). Due to typical parabolic dependence of laser efficiency on operating voltage of main laser capacitor, induced structures exhibit a noticeable symmetry spread out around the maxima of laser efficiency at lower examined laser fluencies. Development of typical surface nanostructure takes place in a narrow interval of applied laser fluencies and is accompanied by considerable polymer ablation as the laser fluence increases.
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 2 Multi-Frequency RTK Positioning Mathematical Model
### Kalman Filtering Theory for Long Baseline RTK Positioning
#### 2.3.3 Settings in Kalman Filtering for Long Baseline RTK Positioning  
In long-baseline (>20 km) RTK positioning, the troposphere and ionosphere are difficult to eliminate. Therefore, the DD troposphere wet delay and DD ionosphere delay of the long baseline are regarded as unknowns for parameter estimation, which is added in the Kalman Filtering as random walk parameters [8].  
\[\mathbf{x}=(\mathbf{r}_{r}^{T},\mathbf{v}_{r}^{T},\mathbf{T}_{z,br},I_{z,rb}^ {1},\cdots,I_{z,rb}^{m-1},\mathbf{N}_{1}^{T},\mathbf{N}_{2}^{T},\mathbf{N}_{3 }^{T}) \tag{17}\]  
In Equation (17), \(I_{z,br}^{m-1}\) is the DD zenith ionospheric delay and \(T_{z,br}\) is the DD troposphere delay.  
The level of the troposphere error is related to the length of the baseline and the elevation difference between stations. Therefore, the zenith troposphere constraint and random walk noise are applied as follows [14]:  
\[P_{trop}=(\log(1+D\cdot 5\cdot 10^{-4})\cdot 0.05+H\cdot 5\cdot 10^{-5}) \tag{18}\]  
\[Q_{trop}=(\log(1+D\cdot 10^{-5})\cdot 0.02+H\cdot 10^{-5})/\sqrt{3600\cdot \Delta t} \tag{19}\]  
In Equations (18) and (19), \(D\) is the baseline length, \(H\) is the height difference in meter, and \(\Delta t\) is the epoch time difference in second.  
The magnitude of the ionosphere error is not only related to the baseline length and the elevation difference between the base and rover stations, but also closely related to the station latitude. Therefore, the ionospheric constraints and random walk noise are applied as follows [14]:  
\[P_{tomo}=(D\cdot 5\cdot 10^{-6}\cdot\exp((90-lat)/50-1))/\sin(E) \tag{20}\]  
\[Q_{tomo}=(D\cdot 5\cdot 10^{-6}\cdot\exp((90-lat)/50-1))/\sin(E)\sqrt{3600\cdot \Delta t} \tag{21}\]  
where _lat_ is the average latitude of the two stations and \(E\) is the elevation of the satellite.
# Mechanical Properties and Flexural Behavior of Sustainable Bamboo Fiber-Reinforced Mortar
## 3 Results and Discussion
### Mechanical Properties
#### 3.1.2 Splitting Tensile Strength  
The control mixture showed a splitting tensile strength of 7.2 MPa. Similar to the trends observed in the compressive strength results, the splitting tensile strength of the samples with 8/6/4 V% of fibers was reduced by 6.9/25.0/26.4% for the 300 \(\upmu\)m fibers and 23.6/30.6/31.9% for the 500 \(\upmu\)m fibers (see Figure 4 and Table 3). It is worth noting that the splitting test setup compression is applied to a small area along the specimen resulting in tensile stress (lateral force) within the matrix which causes splitting. The splitting tensile strength is the maximum load before cracks appear and therefore the crack bridging behavior of the fibers is not considered in this test. This is investigated with bending tests and discussed later on. According to [57], the tensile strength of a mortar matrix can be assumed to be in the range of 10% of the compressive strength which matches the results obtained in this study.
## 5 Study 2: Quantitative Validation of the Technology Acceptance Model in AI Recruitment
### Results
#### 5.2.2 Hypotheses Testing  
The structural equation model demonstrated a good fit to the data: \(\chi^{2}(353)=4.35\), \(p<0.001\), CFI = 0.99, TLI = 0.98, RMSEA = 0.07, 90% CI [0.06, 0.08], SRMR = 0.05 [55]. The standardized coefficients for the relationships between the variables are presented in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **M** & **SD** & **CR** & **AVE** & **MSV** & **1** & **2** & **3** & **4** & **5** \\ \hline
1. PU & 3.86 & 0.77 & 0.92 & 0.71 & 0.84 & (0.68) & & & & \\
2. PEU & 3.73 & 0.69 & 0.90 & 0.64 & 0.80 & 0.51 ** & (0.28) & & & \\
3. Attitude & 3.70 & 0.89 & 0.96 & 0.85 & 0.92 & 0.83 ** & 0.53 ** & (0.68) & & \\
4. ITU & 3.71 & 0.95 & 0.93 & 0.81 & 0.90 & 0.71 ** & 0.53 ** & 0.80 ** & (0.64) & \\
5. Age & - & - & - & - & - & \(-\)0.03 & 0.00 & 0.00 & 0.03 & \\
6. Tenure & - & - & - & - & - & \(-\)0.03 & \(-\)0.02 & 0.03 & 0.07 & 0.73 ** \\
7. Gender 1  & - & - & - & - & 0.02 & \(-\)0.00 & \(-\)0.01 & 0.05 & 0.12 * \\ \hline \end{tabular} Note. ** \(p<0.01\), ** \(p<0.05\), \(N=355\). 1 Gender: 1—Female; 2—Male. The square roots of the Average Variance Extracted (AVE) are presented in parentheses. M = Mean; SD = Standard Deviation; AVE = Average Variance Extracted; MSV = Maximum Shared Variance; CR = Composite Reliability. PU = Perceived usefulness; PEU = Perceived ease of use; ITU = Intention to use.  
\end{table}
Table 4: Descriptive statistics.  
Figure 5: Path coefficients of the proposed mediation model. _Note._ PU = Perceived usefulness; PEU = Perceived ease of use; AT = Attitude; IU = Intention to use. All path coefficients were significant.  
Hypotheses 1 proposed that attitudes toward AI tools would mediate the relationship between (a) perceived ease of use and (b) perceived usefulness, thereby influencing recruiters' intentions to adopt AI tools. The analysis of indirect effects revealed that the influence of (a) perceived ease of use and (b) perceived usefulness on recruiters' intentions to adopt AI tools, mediated by their attitudes toward these tools, was statistically significant (\(\beta\) = 0.027, \(p\) < 0.001, 95% CI [0.01, 0.04]; \(\beta\) = 0.127, \(p\) < 0.001, 95% CI [0.10, 0.16], respectively). The model accounted for 66.6% of the variance in intentions to use AI tools (\(R^{2}\) = 0.666), indicating a strong explanatory power. Thus, Hypothesis 1 was supported by the data (see Table 5 for a summary of the results).
## 5 Material and Methods
### Attack Detection Module
#### 5.6.2 Phase Two: Training the Decision Tree Classifier with Deep Features  
Decision Tree is a widely preferred method among supervised learning algorithms because it is easy to use, the results are straightforward to interpret, and it can handle both categorical and numerical data. Additionally, despite complex relationships within the dataset, decision tree algorithms can produce reliable results. This algorithm iteratively splits the dataset into smaller, more homogeneous subsets. At each node, decisions are made based on a specific feature, which helps the model gradually understand the structure of the dataset and improves classification accuracy [47].  
In the tree structure, internal nodes represent the key features of the dataset, branches represent decision-making rules, and leaf nodes represent the final classification outcomes. The Decision Tree compares feature values starting from the root node, following branches until more homogeneous subsets are formed at each stage. This process continues until a classification result is reached at the leaf node at the end of each branch.  
In this study, the Gini Index was used to select the best feature, as it evaluates the purity of the dataset. It is an appropriate method for minimizing the diversity of data at a node and optimizing the decision-making process. To control the complexity of the tree structure, the maximum depth of the tree was limited to 12. This limitation was imposed to prevent overfitting and enhance the overall performance of the model. While deep tree structures often perform well on training datasets, it should be noted that such structures can reduce the model's generalization capacity.