esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bread Analysis  
#### 3.3.1 Color Analysis  
The color of bread is considered as an important parameter reflecting its quality and acceptance by consumers [23]. The color parameters, namely L*, a*, and b*, for the control bread and the enriched bread with 5, 10, and 15% bemp protein are illustrated in Figure 5, while images of wheat bread (control, 0% bemp protein) and bread containing 5, 10, and 15% bemp protein are presented in Figure 6. ANOVA analysis revealed significant differences among the color parameters. In detail, the control breads exhibited significantly higher lightness values than the fortified bemp protein breads, indicating that the incorporation of protein resulted in the formation of a darker bread crumb. This is related to the green-brown color of the bemp protein [60] and to the Maillard reactions occurring due to the presence of more amino groups in hemp protein [61; 62]. The dark color of the fortified breads could be verified by the images of the breads containing hemp protein, which had a darker color compared to those without hemp protein (Figure 6). This was more pronounced in the breads with 10 and 15% bemp protein. Similar to our results, [PERSON] et al. [23] investigated the texture and color of wheat bread enriched with hemp raw material (hemp seed press cakes, hemp seed grit) and reported that the color of the enriched bread was darker compared to the control bread (0%). A dark color in hemp-enriched breads was also reported by [PERSON] et al. [24], who studied the effect of substituting wheat flour with hemp seed cake flour at different levels (from 5 to 40%) on the quality of bread. Additionally, a significant decrease in the lightness values was observed in gluten-free rice flour breads containing hemp infloresc [60].  
Significant differences were also observed in the a* and b* values. All samples had positive a* and b* values, indicating that the main colors of the breads were red and yellow, respectively. The fortified hemp protein breads showed a significant increase in the a* values and a decrease in the b* values compared to the control bread. [PERSON] et al. [23] found positive a* and b* values in the case of wheat-based breads with 1% heemp raw materials. On the contrary, [PERSON] et al. [16] reported that the addition of hemp seed flour contributed to the increase of a* values and a decrease of b* values in wheat flour bread. Over storage, there were no significant differences in the lightness values of the breads, except for the bread with 10% bemp protein which had higher b* values than the other samples. These differences could be attributed to the presence of more protein aggregates in the bread samples.
# Scalability Analyses of Premium Connections Characterized by a Surface Fractal Function
## 3 Contact Analysis of Premium Connections
### Analysis of Influencing Factors
#### 3.3.3 The Influence of Internal Pressure  
The Von Mises stress nephograms of the premium connection when the internal pressure was 5 \(\times\) 10\({}^{7}\), 7.5 \(\times\) 10\({}^{7}\) and 1 \(\times\) 10\({}^{8}\) Pa are shown in Figure 13, and the distribution of contact pressure on the sealing surface is shown in Figure 14. As shown in Figure 13, the maximum Von Mises stress of the sealing surface increases when the internal pressure is increased. When the internal pressure exceeds 7.5 \(\times\) 10\({}^{7}\) Pa, the area of stress concentration is transferred from the thread to the sealing surface. This is because with the increase in the internal pressure, the plastic deformation of the asperities increases, leading to an increase in stress. Meanwhile, due to the increase in the quantity of contacts and the proportion of plastic deformation in the fractal asperities, there is an increase in the area of stress concentration on the sealing surface. When the internal pressure was 1 \(\times\) 10\({}^{8}\) Pa, the plastic deformation of the asperities increased; the stress concentration, then, is serious, making the sealing surface prone to strength failure.  
As shown in Figure 14, the contact pressure and effective contact length on the fractal surface increase when the internal pressure is increased. This is because deformation of the contacted asperities increases under increasing internal pressure, which causes the non-contacting asperities to come into contact with the surface gradually. The increase in the quantity of contact with the asperities eventually led to an augmentation in the effective length of contact with the sealing surface (from 9.08 \(\times\) 10\({}^{-3}\) to 1.06 \(\times\) 10\({}^{-2}\) m).  
Figure 13: Nephogram of the Von Mises stress under internal pressure. (**a**) 5 \(\times\) 10\({}^{7}\) Pa; (**b**) 7.5 \(\times\) 10\({}^{7}\) Pa; (**c**) 1 \(\times\) 10\({}^{8}\) Pa.
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 4 The Hybrid Context Reasoning Mechanism
### Principles of the Different Reasoning Methods
#### 4.1.2 Rule-Based Reasoning  
To compensate for ontological reasoning's inability to represent complex rules and relations, SWRL is adopted to define rules in the proposed hybrid context reasoning mechanism. SWRL represents a combination of OWL and RuleML [46]. The expressivity of OWL is extended by including Horn-like rules enabled by SWRL. SWRL rules contain unary predicates for the description of classes and data types, binary predicates for expressing object properties, and several built-in n-ary predicates, such as math built-ins, comparisons built-ins, and string built-ins. In essence, SWRL rules are defined in an antecedent-and-consequent, also referred to as if-then, implication. If the set of conditions specified in the antecedent part is met, then the SWRL rule is executed and the assumptions specified in the consequent part can be inferred as high-level information. Hence, marine experts can encode their knowledge and experience into SWRL rules and insert them into the knowledge base. A simple SWRL rule example can be seen as follows:  
SWRL rule-based reasoning: _Robot(?x), hasBatteryLevel (?x,?y), swrlb:greaterThan(?y, 40%) \(\rightarrow\) RobotCandidates(?x)_. The rule above is very straightforward and expresses an assumption that a robot can be regarded as a potential candidate to carry out a task if its battery level is higher than 40%. Once the ontology model is populated with instances, the rule-based reasoning can infer over those instances based on the pre-defined rule. Afterward, more useful information (e.g., robot_[PERSON] is a candidate to execute the task) can be derived given low-level context (e.g., robot_[PERSON] is an underwater robot and its current battery level is higher than 40%). With the allowance of encasing user-defined rules in the ontology model enabled by SWRL, the reasoning task can be augmented in terms of logicality and human readability. Pre-insertion and formalism of SWRL rules are supported by the Protege [47]. The rules creator component also provides interfaces for operators to define and insert rules during missions. Reasoners Pellet and HermiT can enable automatic reasoning based on SWRL rules. Despite the fact that rule-based reasoning has several useful features (e.g., ease of use, ease of implementation, extendibility, and human readability), it also has several limitations remaining to be solved. Manually encasing a large volume of user-defined rules in the ontology model might lead to error-proneness and undecidability in the reasoning phase. Besides, in terms of the ability to define changing events, they lack interoperability and reusability. Similar to ontological reasoning, rule-based reasoning is unable to reason over uncertainties.
## 4 Hand-Transmitted Vibration Prediction Model
### Prediction Model Establishment
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model**} & \multicolumn{8}{c}{**Parameter**} \\ \hline \multirow{8}{*}{Nonlinear Model} & \multicolumn{2}{c}{\(d_{0}\)} & \(e_{1}\) & \(f_{1}\) & \(g_{1}\) & \(h_{1}\) & \(p_{1}\) & \(q_{1}\) & \(r_{1}\) \\  & Palm & \(-0.03796\) & \(0.4634\) & \(0.2162\) & \(0.6084\) & \(-0.6306\) & \(-0.5230\) & \(0.1984\) & \(-0.1076\) \\  & Opisthenar & \(0.1975\) & \(0.2469\) & \(0.4879\) & \(0.5180\) & \(-0.4865\) & \(-0.00003093\) & \(0.6801\) & \(0.8761\) \\ \cline{2-10}  & \multirow{4}{*}{Palm} & \multicolumn{2}{c}{\(e_{2}\)} & \(f_{2}\) & \(g_{2}\) & \(h_{2}\) & \(p_{2}\) & \(q_{2}\) & \(r_{2}\) \\  & Palm & \(-0.1239\) & \(0.1988\) & \(0.0006252\) & \(0.6145\) & \(-0.004515\) & \(0.272\) & \(0.4469\) \\  & Opisthenar & \(0.007234\) & \(0.2388\) & \(-0.3110\) & \(-0.05299\) & \(-0.3006\) & \(0.474\) & \(-0.03424\) \\ \hline \multirow{4}{*}{Polynomial Model} & \multirow{4}{*}{Palm} & \(a_{0}\) & \(a_{1}\) & \(a_{2}\) & \(b_{1}\) & \(b_{2}\) & \(c_{11}\) \\  & Palm & \(-0.02734\) & \(0.0009206\) & \(-0.000004275\) & \(0.0002007\) & \(-0.000003001\) & \(-0.0000008227\) \\ \cline{1-1}  & Opisthenar & \(-0.04146\) & \(0.001220\) & \(-0.000005253\) & \(0.0002963\) & \(-0.0000004451\) & \(-0.000001069\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The parameters of the two models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Index** & \(Q=1\) & \(Q=2\) & \(Q=3\) & \(Q=4\) & \(Q=5\) \\ \hline
**R-square** & \(0.9692\) & \(0.9827\) & \(0.9568\) & \(0.7010\) & \(-0.8748\) \\
**RMSE** & \(0.002408\) & \(0.001858\) & \(0.003720\) & \(0.01214\) & \(0.04475\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Goodness-of-fit of nonlinear models with different cumulative terms. The two models are drawn as shown in Figure 5. According to the goodness-of-fit, the second-order polynomial model is more consistent with the test results in the scope of the test (\(L\in[200,400]\), \(n\in[1,70]\), \(L\) and \(n\in Z\)), and has better interpretability for the test data. Vibration exposure can be accurately predicted for different operating rod lengths and the number of the non-detaching fastening device in the scope of the test. For example, the maximum vibration exposure of the palm and the opisthenar is 0.03322 m/s\({}^{2}\) and 0.04574 m/s\({}^{2}\), respectively, which occurs when 70 non-detaching fastening devices are tightened and loosened using the 240 mm and 249 mm operating rods. After the second partial differential derivation of the polynomial model, there is a global maximum point at \(L=218\), \(n=89\), and \(L=229\), \(n=96\) for the palm and opisthenar polynomial models, respectively. Since the extreme point is close to the scope of the test, although the second-order polynomial model can better explain the test data in the scope of the test, when the length of the operating rod and the number of the non-detaching fastening device exceed the scope of the test, the vibration exposure predicted by this model will decreasedrastically.
## 4 Experiments
### Method Analysis Results and Discussion
#### 4.3.3 Training Cost and Stability  
To more practically and comprehensively validate our method's efficiency and resource utilization, we present the training time and memory consumption of our method in Table 6, using SegRNN as backbone model with the same look-back and forecasting horizon of 96. The results show that the performance improvement requires only modest and acceptable increases in computational overhead. The shorter training time with the 720 horizon indicates that despite PCC introducing additional computational complexity, it can help the model to achieve more stable training and faster convergence when the forecasting horizon is longer and more likely to be unstable.  
Notably, the memory consumption remains nearly identical (less than 0.5% difference) when the prediction horizon is 720. Counter-intuitively, the maximum memory consumption with PCC is slightly lower than the original SegRNN when the prediction horizon is 96. We attribute this to the additional influence of the PCC module on the GPU memory allocation optimization strategies of PyTorch, as the additional memory consumption introduced by PCC is too small to be a significant factor in the total memory consumption.  
To evaluate the training process, Figure 6 illustrates the temporal evolution of the training and validation losses for the SegRNN backbone with and without PCC integration. The experiments were conducted with a fixed look-back window of 96 and forecasting lengths of 96 and 720. The training loss trajectories (depicted by blue lines) for both model variants exhibit stability and convergence in the latter stages of training, demonstrating that incorporation of the PCC module maintains training stability while enhancing predictive performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Design**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**Time (s)**} & \multicolumn{2}{c}{**Memory (MB)**} \\ \cline{2-7}  & **96** & **720** & **96** & **720** & **96** & **720** \\ \hline Original & 0.162 & 0.347 & 176 & 227 & 204.55 & 865.39 \\ \hline +PCC & 0.144 & 0.299 & 214 & 219 & 204.47 & 868.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of training time and maximum memory consumption of the SegRNN backbone with and without PCC. The look-back length is 96, while the prediction horizons are 96 and 720. We set the number of training epochs as 30 and the patience as 10.  
Figure 5: Fitting curves showing the performance of the SegRNN backbone model with different parameter scales with and without our PCC module. The results indicate that the proposed PCC module can help the backbone model to achieve better performance with a significantly smaller number of parameters.
## 3 Conceptual View of the Present Speech Recognition System
### Speech Segmentation  
Praat segmented long speech sentences into smaller segments to make the CNN process easier. The Praat program is considered a versatile speech analysis program and has been proved to be a standard tool in the field of speech and language processing. Audio quality is a significant factor in speech segmentation. Speech segmentation analyzes different frequencies and amplitude characteristics of recorded speech to locate speech particles. Praat also offers various audio tools to improve audio quality, such as a noise gate, de-noising, and a normalizer tool [29].  
The Praat software tool was used to create manual segments of long-input speech audio. Figure 3 shows how the segments were designed for the given audio file. Though automatic segmentation is an easy task, manual segmentation was adopted due to the detailed visualization and identification of the start and end of sentences in the input speech. Careful practice is needed when creating small segments. Tables 2 and 3 demonstrate the system parameters for segmentation, including the programming language, the backend tool, GUI, the segmentation tool, and the development environment.  
Figure 4 shows the segmentation procedure in the current ASR system. Here, five segments were created using Praat. Figure 5 shows all the elements created for the input speech files, and text grids are shown on the left panel of the Praat window. These segments of lengthy audio files are valid for feature extraction and CNNs, as long sentences are too cumbersome. The proposed model was devised on a DELL machine with a VG card. The programming was implemented on Python, Tensor Flow was used as the backend, supported by Keras, and the Kaldi toolkit was used along with Python.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset Parameters** & **Values** \\ \hline Number of Males Speakers & 3 \\ \hline Number of Females Speakers & 8 \\ \hline Frame length & 10 ms \\ \hline Total stanzas in each recording by a speaker & 38 \\ \hline Total number of speakers & 11 (Age groups: 18–30, 30–50, 50–65) \\ \hline Speed of speech & Normal and continuous (Transcribed and labeled speech) \\ \hline Noises while recording & White noise, babble noise, motor noise \\ \hline File format &.wav \\ \hline Recording software & NCH Suite–WavePad, Australia \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset parameters used in the current system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System Parameters** & **Specifications** \\ \hline Programming Language & Python 3.7.3 \\ Python Library/Backend & Tensor Flow v2.9.1 \\ GUI & Anaconda Navigator 2.1.1 \\ Segmentation Tool & Praat v6.1.49 \\ Feature Extraction & MFCC 1.1.0, LibROSA 0.9.1 \\ Development Environment & Spyder 3.3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in current system.  
Figure 4: Spectrogram and accuracy generated for Speaker 1.  
Figure 5: Graph depiction for WERs for all input files (Speakers 1–11).  
Figure 3: Segmentation of audio file in current ASR system.
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
## 4 Implementation
### Single Detector  
Figure 6 displays a chip photo containing different THz detector structures with on-chip antennas. Enlarged views mark the HEMT integrations within the designs:  
* Structures A2 and B2: Broadband THz detectors with log-spiral and bow-tie antennas, respectively;
* Structures A1, C1, and D2: Narrowband slot antennas covering 100 GHz to 400 GHz.  
Unique features include:  
* Structure A1: Two HEMTs connected in parallel at the structure's edge.
* Structure D2: Narrowband design for 350 GHz.  
The compact designs, particularly A1 and D2, are well-suited for future large THz focal plane arrays. For dimension comparison in Figure 6, it is essential to note that the diameter of the log-spiral structure is 610 \(\upmu\)m.
# Natural Photosensitizers in Clinical Trials
## 3 Natural Photosensitizers in Clinical Trials
### Anthraquinones
\begin{table}
\begin{tabular}{c c c c} \hline
**Photosensitizer** & **Therapeutic Regimen** & **Findings** & **Article** \\ \hline \hline \multirow{7}{*}{Six sessions of PDT of dentures and oral cavity, three times a week for half a month using a CUR solution of 5 \(\upmu\)g/mL and light with a wavelength ranging from 440 to 460 nm & Reduction in mean number of CFU/mL of Candida from denture surface and palatal mucosa after 6- and 12-week follow-up after CUR-PDT for the treatment of stomatitis in patients who are heavy smokers & \\ \hline Use of mouthwash with 20 mL of 30 mg/L CUR solution for 5 min, its removal, and exposure to blue light with a wavelength of 455 +/ – 30 nm for 5 min & CFU reduction at 1 and 2 h after CUR-PDT & [PERSON] et al. [36] \\ \hline \multirow{7}{*}{Curcumin} & Rinsing the mouth three times with 15 mL of 25 mg/L or 100 mg/L curcumin solution for 1 min, followed by irradiation of the mouth for 6 min with 450 nm light & Reduction in microbial count 24 h after CUR-PDT & [PERSON] et al. [37] \\ \cline{1-1} \cline{2-5}  & Rinsing the mouth with a 1 mg/L curcumin solution for 2 min, followed by irradiation of the buccal and lingual surfaces of the teeth sequentially for 3 min with 450 +/ – 10 nm light & Reduction in _S. mutans_ in orthodontic patients after CUR-PDT; this effect was enhanced when 0.1\% SDS solution was added to curcumin solution & [PERSON] et al. [38] \\ \cline{1-1} \cline{2-5}  & Rinsing the mouth with 20 mL of 1.5 g/L curcumin solution for 5 min, followed by irradiation of the oral surface for 5 min with 468 nm light and application of the low-intensity laser PBM-T protocol & Cure of 12 of 14 cancer patients from oral mucositis after CUR-PDT & [PERSON] et al. [39] \\ \cline{1-1} \cline{2-5}  & Infusion of 2.5 mg/mL curcumin solution into canal 180 s before irradiation, subsequent cleansing with MTAD & Achieving the highest push-back bond strength (PBS) at two levels: cervical and midline after CUR-PDT & [PERSON] et al. [40] \\ \hline \hline \end{tabular} Application of a mask containing 1\% curcumin for 20 min, followed by exposure to 445 nm light; sessions at 3-day intervals, for a total of two treatments per week, administered continuously for 2 weeks  
\end{table} 
Table 2: Summary of results of clinical work investigating photodynamic therapy with natural photosensitizers.
## 2 Results and Discussion
### Nearly PLA of Simply Stacked W-SiN Membrane
We mention that the magnetic-field distribution is uniquely determined by the boundary-connection conditions in the membrane structure and that light absorption mostly takes place in the W layer through one of Maxwell equation,  
\[\
abla\times\mathbf{H}=\epsilon_{0}\varepsilon_{\text{W}}\frac{\partial \mathbf{E}}{\partial t}, \tag{2}\]  
Figure 3: Light absorption properties and electromagnetic (EM)-field distribution at nearly perfect-absorption conditions. (**a**,**b**) 2D color and contour plots of light absorptance under p and s polarizations, respectively. In (**a**,**b**), the vertical axis is in common, representing photon energy in eV, and the horizontal axes represent incident angles. In addition, the common color bar is presented at the right-hand side. (**c**,**d**) snapshots of \(E_{y}\) components near the W-SiN membrane at the normal incidence and incident angle of \(60^{\circ}\), respectively. The photon energy was set to 1.208 eV. These conditions are indicated by open circle (\(\circ\)) and cross (\(\times\)) in (**b**). The incident electric-field vector \(\mathbf{E}_{\text{in}}\) was set to be parallel to the \(y\)-axis and \(|\mathbf{E}_{\text{in}}|=1\); (**e**) \(|\mathbf{E}|\) distribution at the condition \(\circ\) in (**b**); (**f**) profile of the \(|\mathbf{E}|\) distribution in (**e**) along the \(z\)-axis; (**g**) \(|\mathbf{H}|\) distribution at the condition \(\circ\) in (**b**); (**h**) profile of the \(|\mathbf{H}|\) distribution in (**g**) along the \(z\)-axis; (**i**–**i**) a set of EM-field distribution and the \(z\) profiles at the condition indicated by \(\times\) in (**b**), shown in a similar way to (**e**–**h**), respectively. The green lines in (**c**–**l**) denote the interfaces of the hetero-materials. where \(\varepsilon_{0}\) is permittivity in vacuum because spatial distribution of \(\mathbf{H}\) is directly affected by the complex relative permittivity in the W layer (\(\varepsilon_{W}\)); the permittivity \(\varepsilon_{W}\) has a large imaginary part [16] that is associated with optical loss. Figure 3i,j corresponds to Figure 3d and shows the \(|\mathbf{E}|\) distributions in a similar way to Figure 3e,f. Also under the oblique incidence (\(\times\) in Figure 3b), the \(|\mathbf{E}|\) profile indicates efficient light absorption. Figure 3k,l corresponds to Figure 3i,j, and shows the \(|\mathbf{H}|\) distribution and the \(z\) profile, respectively. Qualitatively, the descriptions for Figure 3g,h are true for Figure 3k,l as well. In short, the large light absorption at 1.0-1.5 eV and wide incident angles including \(0^{\circ}\) is ascribed to impedance matching of the two-layer membrane structure. The efficient absorption primarily originates from the property of W permittivity. We point out that the single layer of W does not realize efficient light absorption as shown in Figure 2a. In this sense, the two-layer membrane structure is the minimal requirement for large light absorption in W-ceramic systems.