esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Moisture Content  
The moisture content (MC) analysis results are presented in Table 2. The results showed that adding sea buckthorn extracts to the PBS polymer led to an increase in the moisture content of the films compared to that of a control sample of neat PBS (1.47 \(\pm\) 0.30%). Both chloroform extract and scCO\({}_{2}\) extract affected hygroscopicity but differed in efficiency. Samples containing added Chl extract (0.01 g/1 g PBS) exhibited a small increase in moisture content (1.73 \(\pm\) 0.61%), while for the scCO\({}_{2}\) extract at the same concentration, the increase was much higher (2.54 \(\pm\) 0.47%), indicating that the scCO\({}_{2}\) extract is more hygroscopic. Higher extract concentrations (0.05 g/1 g PBS) intensified this effect, reaching values of 2.55 \(\pm\) 0.45% for the Chl extract and values as high as 3.34 \(\pm\) 0.58% for the scCO\({}_{2}\) extract. These results suggest that hygroscopicity depends on both the type and concentration of the additive, with the scCO\({}_{2}\) extract exhibiting stronger moisture-absorbing properties. These modifications may potentially be suitable for applications where higher material moisture content is desirable, such as biodegradable packaging or medical applications; however, further research into the effects of moisture on the mechanical properties and thermal stability of the films is required.  
Earlier studies conducted on films based on poly(vinyl alcohol) modified with citric acid and sea buckthorn leaf extract showed an increase in MC for a film containing 2% plant extract and a decrease in the value of this parameter as the amount of extract increased to 4% and then 6%, respectively [22]. However, it should be noted that PVA is a hot water-soluble polymer, unlike PBS. Therefore, the differences may be due to the properties of the polymer itself and the interaction between the polymer and the active additive rather than the presence of the additive alone.
###### Abstract  
Frame analysis (FA) is known for its efficiency and low computer resource requirements. However, compared to finite element analysis (FEA), the applicability of FA for the seismic performance assessment of pile-supported wharves (PSWs) is limited, particularly in the ability to account for the kinematic force caused by ground displacement during an earthquake. This study aimed to develop a two-dimensional FA method for PSW seismic response analysis that considers a combination of inertial and kinematic forces. We performed FA and FEA and compared the results. First, we targeted the PSW model without considering the soil slope and discussed the spectral acceleration (SA) evaluation method for calculating inertial force. As a result, an equation for estimating a damping coefficient to evaluate the SA in accordance with the PSW width and natural period was proposed. Next, we targeted the PSW model by considering the soil slope and proposed a method to evaluate the kinematic force based on the amount of ground displacement and the soil spring characteristics. The results revealed that using the proposed method, FA, by considering kinematic and inertial forces, could reproduce the bending moments of the piles comparable to those calculated using FEA. Therefore, solely considering the inertial force for a PSW on a soil slope may cause the bending moment to be underestimated.  
frame analysis; pile-supported wharf; seismic response; finite element analysis; ground displacement; inertial force; kinematic force +
Footnote †: journal: _Sciences_
## 4 Discussion
### Discussion Benchmarking Results  
The benchmarking indicates that classifiers constructed using Ridge readouts outperform those based on MLP in both paradigms. Similar to the findings of the first scenario, overfitting phenomena appear when the MLP classifier is considered and are accentuated  
Figure 12: Confusion matrix corresponding to NLRC+NVAR+Ridge-based classifier with N = 55 and dim = 40. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Raw** & **SMOTE** & **REPLICA** \\
**Classifier** & **AUC\%** & **AUC\%** & **AUC\%** \\ \hline NLRC + Ridge & 91.48 & 88.79 & 62.07 \\ NLRC + MLP & 76.59 1 & 76.01 & 63.05 \\ LRC + Ridge & 91.55 & 91.47 & 62.65 \\ LRC + MPL & 75.59 & 75.36 & 63.94 \\ \hline \hline \end{tabular}  
* These results are obtained using specific configurations of the underlying classifiers: N = 55 and dim = 18.  
\end{table} 
Table 5: Benchmarking classification results of classifier after database augmentation. when combined with the NVAR. In contrast, overfitting is reduced when the Ridge readout is considered. Typically, inter-patient outcomes are less accurate than intra-patient outcomes. Therefore, the classification results presented in Table 6, which are obtained with the current algorithm, are promising. The high ACC of 96.06% is noteworthy, especially when compared to algorithms trained with balanced data (marked with an asterisk (*)), which is not the case for the proposed classifier. However, this classifier performed poorly in identifying the S category, with low levels of SPEC, SEN, and PPV pointing to its failure. The difficulty of correctly identifying the S category for the inter-patient classification task is due to its complex nature and its high similarity to the N category (see Figure 2) that requires additional rhythmic information, like the R-R interval (i.e., the interval between successive beats) [69]. Additionally, classifiers based on RCs are still in the early stages of development, and further improvements are necessary to adapt them to inter-patient classification in general. In the case of the intra-patient paradigm, Table 7, the inability of the proposed classifier to detect the S category persists. This is marked by the low values of the SEN and PPV. In contrast to the S category, the classification performance of the N and V categories could be compared favorably with that of the literature. On the whole, the intra-patient findings are similar to other results, but additional effort is required to improve the performance of the proposed classifier. We also notice that some classifiers based on the MLP as a readout achieve better results using a specific configuration, especially a different number of internal nodes \"N\" and the number of features \"dim\". To illustrate, the (NLRC + NAVR + MPL) classifier demonstrates its optimal performance when utilizing N = 15 nodes and dim = 24 features. Furthermore, the impact of the circular implementation of the standard RC and the bidirectional nature of the broadcast data on the classification performance is intuitively positive which conforms to the results in [57].
# SLoPCloud: An Efficient Solution for Locality Problem in Peer-to-Peer Cloud Systems
## 1 Introduction
Generalized Hypercube Connected Cycle (GHCC), to be used as an overlay layer in P2P cloud networks. GHCC is compatible with the proposed solution and could be easily converted into different well-known topologies with just a simple modification in its parameters. It is worth noting that choosing a proper graph has a considerable effect on the network performance metrics such as the bandwidth, congestion, and delay. 3. This paper proves the performance and the scalability of the proposed solution SLoPcloud, through comprehensive simulations. Here, SLoPcloud solution has been implemented while topologies Ring, Hypercube, CCC, and GHCC are considered as the overlay network. The focus of simulation study in this paper is on the network bootstrapping time and the control traffic caused by the locality solution. The rest of the paper is as follows. The related works are represented in Section 2. Section 3 provides a preliminary background information on P2P network topologies. In Section 4, the proposed solution regarding a suggested topology for the overlay graph is presented in details. Simulation parameters and experimental results are illustrated in Section 5. Finally, the paper is concluded in Section 6.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 2 Laser-Matter Interaction in the Femtosecond Regime
### Laser Ablation and Plasma Formation
Due to non-linear absorption, the absorbed energy distribution becomes narrower with increasing order (n) of multiphoton absorption. The effective absorption coefficient for n-photon absorption is proportional to the n-th power of the laser intensity, \(I\) (\(I^{n}\)) and the effective laser spot size for n-photon absorption becomes respectively smaller. The effective beam size d for n-photon absorption is expressed as \(d=d_{0}/n\)\({}^{1/2}\) where \(d_{0}\) is the actual spot size of the focused laser beam [74,75]. Ablation at the surface, in addition to atoms and atomic ions, also produces larger particles, which can further catalyse the particle formation rate in the expanding plasma plume. Non-uniform power densities are at the onset of various processes dependent on the incident laser intensity. Thus, by applying laser radiation with a Gaussian intensity-profile, these processes get mixed. Destruction of the sample integrity (appearance of cracks, flaking of the surface), melting, ablation, and ionisation due to the spatial intensity distribution across the focal spot take place across the laser-affected area even if the average spatial intensity is higher than the ablation threshold. Some improvements in the reduction of particles and better control of the ablation products can be made when local fluences are above the ablation threshold and equal at every point across the focal area. Application of laser radiation with a flat-top intensity-profile offers more optimal conditions for the ablation than typically applied Gaussian-shaped beam, which exhibits a changing intensity across the ablation area [43]. So far, the flat-top fs-laser beam is not used, and mostly Gaussian laser beams were applied in LIMS experiments [76].
## 5 Results and Discussion
### Future Work  
All experiments were performed on a lab-scale network, involving a limited number of devices, links, and communication paths. While this scale is sufficient to evaluate protocol behavior and demonstrate specific vulnerabilities, larger-scale systems, such as smart grids, may exhibit slight behavior differences due to load, event rates, and other system stress factors. For example, issues like latency amplification, network-wide retransmission storms, or cascading connection failures in distributed systems were beyond the scope of this work. These scalability dynamics merit further study in future work using high-fidelity,distributed testbeds. The attack models explored were also executed under controlled conditions, focusing on the specific vulnerabilities exposed by the TCP exponential backoff behavior. Our future work will widen the scope and incorporate a broader threat model with both passive and active attack vectors.  
One important additional direction for future work is a more detailed examination of the performance-security trade-offs associated with deploying layered defense mechanisms in time-sensitive OT networks. While our study observed and reported only minor latency increases from measures such as SYN-flood protection and port filtering (as shown in Table 1), it did not evaluate whether these delays are acceptable in applications with strict timing constraints, such as sub-10 ms control loops found in power grid protection, motion control, and high-speed manufacturing systems. Future experiments will focus on measuring end-to-end system responsiveness under these conditions, using hardware-in-the-loop (HIL) setups and real-time process control benchmarks to quantify any disruption to deterministic performance.
# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
###### Abstract  
In South Africa, indigenous herbal teas are enjoyed due to their distinct taste and aroma. The acclaimed health benefits of herbal teas include the management of chronic diseases such as hypertension and diabetes. Quality control of herbal teas has become important due to the availability of different brands of varying quality and the production of tea blends. The potential of hyperspectral imaging as a rapid quality control method for herbal tea blends from rooibos (_Aspalathus linearis_), honeybush (_Cyclopia intermedia_), buchu (_Agathosma Betulina_) and cancerbush (_Sutherlandia frutescens_) was investigated. Hyperspectral images of raw materials and intact tea bags were acquired using a sisu-cherna shortwave infrared (SWIR) hyperspectral pushbroom imaging system (920-2514 nm). Principal component analysis (PCA) plots showed clear discrimination between raw materials. Partial least squares discriminant analysis (PLS-DA) models correctly predicted the raw material constituents of each blend and accurately determined the relative proportions. The results were corroborated independently using ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS). This study demonstrated the application of hyperspectral imaging coupled with chemometric modelling as a reliable, rapid and non-destructive quality control method for authenticating herbal tea blends and to determine relative proportions in a tea bag.  
herbal tea; tea blends; quality control; hyperspectral imaging; chemometrics; rooibos; honeybush; cancer bush; buchu Article
## 6 Discussion
### Additional Differences in the Performances: Type, Gender, and Section  
This section revisits the differences by sections but now including gender as an additional discriminant in the analysis. Apprecating the Physics teacher, both genders became committed to the course (classes and work) without noticeable distinctions. Some of the students commonly turn-off their cameras (a behavior permitted in the course) and switch them on to ask questions or collaborate with their partners in teamwork time. Both genders practiced such behavior without loss of representation.  
Differences suggested in Figure 5 can be statistically analyzed. By considering the input data for those plots grouped by gender and section for each type of performance, two ANOVA tests for two factors were performed. ANOVA tests (in this case, a two-factor test) compare the mean value of groups extracted from some population as a null hypothesis assuming independence in the selection, normality, and equality in the variance, versus the differences in those parameters in the alternative hypothesis. Based on a \(t\)-distribution to fit small samples, the current analysis fulfills this test's premises [47]. ANOVA tests are shown in Table 3: (a) the upper part for individual performances and (b) the lower one for teamwork performances.  
\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**(a) Individual performance** & & & & & & \\ \hline
**Factors** & **d.f.** & **Sq. sum** & **Sq. av.** & \(F\)**value** & \(p\) & \(\mathbf{F_{critical}}\) \\ \hline Section & 332.18 & 3 & 110.73 & 13.50 & 0.03 & 9.28 \\ Gender individual & 8.17 & 1 & 8.17 & 1.00 & 0.39 & 10.13 \\ Error & 24.60 & 3 & 8.20 & & & \\ Total & 364.95 & 7 & & & & \\ \hline
**(b) Teamwork performance** & & & & & & \\ \hline
**Factors** & **d.f.** & **Sq. sum** & **Sq. av.** & \(F\)**value** & \(p\) & \(\mathbf{F_{critical}}\) \\ \hline Section & 51.90 & 3 & 17.30 & 0.76 & 0.59 & 9.28 \\ Gender team & 69.12 & 1 & 69.12 & 3.04 & 0.18 & 10.13 \\ Error & 68.16 & 3 & 22.72 & & & \\ Total & 189.18 & 7 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANOVA two factor tests to probe equality between section and gender performances working (a) individually, or (b) by teams.  
Note that only the individual performance \(13.50=F>F_{\text{critical}}=9.28\), so there is a meaningful difference among sections, not observed in the teamwork performance. Neither are differences by gender observed in any kind of performance. Those facts, then, are consistent with Figure 5.
# Hierarchical Phoneme Classification for Improved Speech Recognition
###### Abstract  
Speech recognition consists of converting input sound into a sequence of phonemes, then finding text for the input using language models. Therefore, phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. However, correctly distinguishing phonemes with similar characteristics is still a challenging problem even for state-of-the-art classification methods, and the classification errors are hard to be recovered in the subsequent language processing steps. This paper proposes a hierarchical phoneme clustering method to exploit more suitable recognition models to different phonemes. The phonemes of the TIMIT database are carefully analyzed using a confusion matrix from a baseline speech recognition model. Using automatic phoneme clustering results, a set of phoneme classification models optimized for the generated phoneme groups is constructed and integrated into a hierarchical phoneme classification method. According to the results of a number of phoneme classification experiments, the proposed hierarchical phoneme group models improved performance over the baseline by 3%, 2.1%, 6.0%, and 2.2% for fricative, affirate, stop, and nasal sounds, respectively. The average accuracy was 69.5% and 71.7% for the baseline and proposed hierarchical models, showing a 2.2% overall improvement.  
speech recognition; phoneme classification; clustering; recurrent neural networks +
Footnote †: journal: Applied Sciences  
doi: 10.3390/accepted: 30 December 2020  
Footnote †: journal: Applied Sciences
## 3 Methods
### Yield Prediction
#### 3.1.2 Gross Primary Productivity  
The total assimilation flux \(G_{i}\), = also known as the gross primary productivity (GPP) (gCm\({}^{-2}\)-day\({}^{-1}\)), can be calculated using the remote sensing-based plant light use efficiency (LUE) approach. The chloroplasts use incoming solar radiation with a spectral range between 400 nm and 700 nm in photosynthesis [47].  
\[G_{i}=PAR_{i}\times fPAR_{i}\times RUIE_{i} \tag{3}\]  
\[PAR=2.3(R_{O}\times\tau_{\partial})\rho_{sw} \tag{4}\]  
where \((R_{O}\times\tau_{\partial})\rho_{sw}\) represents the shortwave irradiance (\(R_{s}\)), \(R_{O}\) is daily top-of-atmosphere shortwave irradiance (J/m\({}^{2}\)/day) [48; 49], \(\tau_{\partial}\) is atmospheric transmissivity calculated using the Bristow-Campbell relationship [50; 51] calibrated for Australia, and \(\rho_{sw}\) is the ratio of shortwave irradiance at a sloping surface to that at a horizontal surface [52].  
_fPAR_ is the portion of _PAR_ that is absorbed by a photosynthetic organism, and it is estimated using a linear relation between _fPAR_ and rescaled NDVI by thresholds (i.e., local minimum and crop-specific maximum NDVIs) [53; 54]. _LUE_ is highly linearly related to a diffuse fraction (\(f_{D}\)) and photosynthetic carbon flux [55].  
\[LUIE_{i}=0.024\times f_{Di}+0.00061A_{x} \tag{5}\]  
where \(A_{x}\) is the maximum photosynthetic capacity (\(\mu\)molCm\({}^{-2}\)-s\({}^{-1}\)), which is a crop-specific parameter; is 23, 40, and 45 for barley [56], canola [57], and wheat [57], respectively; \(f_{D}\) is the ratio of diffused to total solar irradiance varying from 0.2 (under clear skies) to 1.0 (under overcast skies) [48]. For a full description of C-Crop, see [PERSON] et al. [6].
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 2 Methodology
### Models
#### 2.4.1 Artificial Neural Networking (ANN)  
Artificial neural networking (ANN) is a computational model inspired by the human brain's structure [122]. It consists of layers of interconnected nodes, or neurons, which process and transmit data. The network is structured into an input layer, one or more hidden layers, and an output layer [123]. Each neuron receives input, applies an activation function, and passes the output to the next layer. Learning in neural networks involves adjusting the weights of the connections between neurons based on the error of the network's predictions [124]. This process includes forward propagation (passing input data through the network to generate an output), calculating the error using a loss function, and backpropagation (updating weights to minimize the error using gradient descent).  
Figure 6: Pearson correlation coefficient matrices for (**a**) real data, (**b**) BR-augmented data, (**c**) DGAN-augmented data, correlation coefficient difference for (**d**) real and BR-augmented data, (**e**) real and DGAN-augmented data.  
Various researchers have used ANN for the prediction of concrete properties. [PERSON] et al. developed ANN and multiple linear regression (MR) models for predicting the rheological properties of the concrete and found that ANN can give a more accurate prediction with an R\({}^{2}\) score of 0.89 in comparison to MR with an R\({}^{2}\) score of 0.323 [125]. Similarly, research conducted by [126] shows that the ANN model exhibits superior accuracy with respect to the prediction of concrete properties.  
Figure 7: Relative frequencies of key features (**a**) Water (kg/m\({}^{3}\)), (**b**) Cement (kg/m\({}^{3}\)), (**c**) Silica fume (kg/m\({}^{3}\)), (**d**) Fiber amount (kg/m\({}^{3}\)), (**e**) Cast compressive strength (MPa), (**f**) Printed compressive strength-Dir. 1 (MPa), (**g**) Printed compressive strength-Dir. 2 (MPa), (**h**) Printed compressive strength-Dir. 3 (MPa), (**i**) Slump flow (mm).  
less variance between the actual and predicted results, as indicated by an R\({}^{2}\) value of 0.67. In contrast, the Decision Tree (DT) model shows a lower R\({}^{2}\) value of 0.63. ANN is also used in the prediction of 3D concrete printing. ANN was used by [127] to investigate the impact of different steam curing conditions (temperature rise rate, retention capacity, and sustained temperature) on the performance properties of 3D-printed concrete materials at various curing ages effectively, with an R\({}^{2}\) score of 0.91. Similar findings are shown by [65; 126; 128; 129]. Although ANN is being used in predicting properties of concrete printing, their performance tends to improve with increased datasets because, with larger datasets, they can learn more complex patterns and reduce overfitting. More data also helps improve the network's generalization, leading to better performance on unseen data. The hyperparameters for ANN for DGAN and BR are shown in Table 3.