esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Properties of Ultrasound-Extracted Okra Mucilage
###### Abstract  
Utilizing chemical and physicochemical techniques, the mucilage part of okra was extracted with the use of ultrasound, and the polysaccharide extract's structural and thermal characteristics were assessed. Analysis of the extraction yield revealed that there was no discernible impact of time and that the yield increased with a decreasing okra to distilled water ratio (mean 8%). Differential scanning calorimetry was employed to determine the phase change enthalpy by examining the glass transition temperature and enthalpy. The glass transition temperatures of the samples were found to be about 50 \({}^{\circ}\)C and the melting temperatures were in the range of 166-170 \({}^{\circ}\)C for varied solid/solvent ratios (1:10, 1:25, 1:30, and 1:50) and extraction durations of 5 and 30 min. Using thermogravimetry (TG) and differential thermogravimetry (DTG) techniques, it was discovered that the okra polysaccharides were thermally stable with considerable weight loss above 240 \({}^{\circ}\)C. For the purpose of illuminating the bonds of the polysaccharides, FTIR analysis was used to characterize the polysaccharides obtained by the varying extraction times and rates of solid/solvent. This analysis provides detailed information about the composition of the extracts. It was found that the molecular structure of the mucilage from okra was unaffected by the varying ratios and times. The study's findings indicated that the use of ultrasound could be a promising approach for extracting polysaccharides that possess strong thermal stability, making them suitable for use in various industrial applications. The study noted that variations in ultrasound application time and solid/solvent ratios did not appear to impact the thermal stability of the extracted polysaccharides. The important parameters for the extraction conditions such as the time and low amount of sample used are preferred for applications. The findings obtained indicate that ultrasonic extraction application at a 1/50 solid/solvent ratio for 5 min is statistically significant in terms of thermal properties and yield. These findings could have important implications for the energy costs associated with the industrial use of ultrasound extraction.  
okra mucilage; DSC; TGA; ultrasound; extraction; molecular characterization [PERSON]  
[PERSON], [PERSON], [PERSON], S. Thermal Properties of Ultrasound-Extracted Okra Mucilage  
1 Food Processing Department, EfeS Vocational School, Dokuz Ejful University, Izmir 35920, Turkey; [EMAIL_ADDRESS]  
2 Food Engineering Department, Engineering Faculty, Ege University, Izmir 35100, Turkey; [EMAIL_ADDRESS]
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 2 Methodology  
### Overall  
As shown in Figure 1, the proposed technical route for bearing fault diagnosis involves four key steps: vibration signal acquisition and preprocessing, time-frequency analysis using the S-transform, feature extraction using MobileNet as the backbone network, and model compression using knowledge distillation. This approach leverages the strengths of time-frequency analysis and deep learning to provide an accurate and efficient solution for bearing fault diagnosis. The use of MobileNet as the backbone network ensures that the model is lightweight and suitable for real-time applications, while knowledge distillation further compresses the model, making it deployable on resource-constrained devices. This combination of techniques offers a promising solution for the early detection and diagnosis of bearing faults, which is crucial for preventing catastrophic failures and reducing maintenance costs in industrial applications.
## 4 Experiments and Results
### Experimental Setup  
All speech recognition experiments were performed using the Kaldi toolkit [29]. The input features were equivalent to the global mean-variance normalized 40-dimensional log-Mel filter-bank feature spliced across \(\pm 2\) frames. The acoustic model used was LSTM-projection (LSTMP) trained by  
Figure 4: Block diagram and an example of the lexicon update. (**a**) Block diagram of the lexicon update; (**b**) phoneme-based lexicon given at point D; (**c**) proposed lexicon obtained at point E; (**d**) example of the lexicon update using CD phoneme \"$ - a + b\".  
layer-wise back-propagation supervised with a 3-state left-to-right hidden Markov model. The LSTMP model has 3 layers, and each layer has 1,024 memory cells and 256 hidden nodes in the projection and the output layer, with about 8,033 nodes using the softmax activation function. The language model was trained via Kneser-Ney discounting (cut-off 0-3-3) using the SRILM toolkit [30] and included 1.8 million unigrams, 32 million bigrams, and 43 million trigrams from various corpora, including broadcast subtitles (excluding the evaluation datasets). The decoder was set to have an acoustic weight of 0.125, a beam-size of 10.0, and a lattice-beam of 5.0. The recognition results were compared by calculating the word error rate (WER) in the decoding unit.  
We used about 1000 h of Korean broadcast data [25; 31] for the subword unit derivation and the acoustic model training. This database was automatically constructed from broadcast audio and their subtitle text using a lightly supervised approach [32]. The collected data contain some incorrectly transcribed text that does not match the speech signal and has a mixture of background noise and music. The broadcast data were extracted from broadcasts recorded on South Korean broadcasting channels from March to June 2016. The broadcast data are largely composed of seven genres: News, current affairs, documentary, culture, drama, children, and entertainment. Here, we note that the news, documentary, and current affairs genres, which include many reading-style utterances, comprise 25%, 12%, and 5%, respectively (42%), of the total broadcast data. On the other hand, the culture, drama, entertainment, and children's genres, which include many spontaneous-style utterances, comprise 22%, 16%, 12%, and 3%, respectively (54%) of the total broadcast data. The remaining 4% consists of sports broadcasts and music programs.  
The evaluation datasets featured 1.5 h of broadcast news data ('News'), 3.5 h of web news data ('Web'), and 7 h of spontaneous data ('SPT'). Here, the News dataset may contain the same speakers and similar sentences because it was collected during a similar time to the training data. The Web dataset, on the other hand, consists of sentences on subjects different from those in the training dataset, since the Web dataset was collected from web sites in 2019. The SPT dataset contains unwanted pauses, word fragments, filler words, self-corrections, and repeated words. All evaluation datasets were not used for training the acoustic model or building the proposed unit.
## 3 Results and Discussion  
### Friction Test Results  
The friction test results are shown in Table 2. \(X\) is the test number, where 1-27 is the test number of the piston with a cylindrical pit array, and \(S\) is the test number of the standard piston. \(F+\) is the average friction force in the forward stroke of the piston, and \(F-\) is the average friction force in the reverse stroke of the piston. It can be seen that, compared  
Figure 11: Mesh refinement.  
Figure 12: Boundary conditions.  
with the standard piston, the \(F+\) and \(F-\) values of pistons 1-27 were both reduced to some extent. Among them, \(F+\) and \(F-\) for tests 4, 13, 22 and 25 reduced the most. Their \(F+\) drag reduction rates were 46.25%, 43.42%, 44.01% and 40.68%, respectively. Their \(F-\) drag reduction rates were 52.29%, 53.11%, 52.24% and 48.14%, respectively.  
When \(D\), \(H\) and \(N\) are unchanged, \(F+\) and \(F-\) are shown in Table 3. It can be seen that as \(D\) increased, both \(F+\) and \(F-\) decreased first and then increased, and a minimum was achieved at \(D=1.5\) mm. Therefore, in order to improve the friction performance of the piston, \(D\) should not be too large or too small. \(F+\) increased with the increase in \(H\), while \(H\) had no significant effect on \(F-\). In Table 3, an increase in \(H\) leads to a notable variation in the average value of \(F+\), ranging from 7 N to 10 N. However, the average value of \(F-\) exhibits minimal changes, remaining distributed within the range from 174 N to 177 N, with a difference of approximately 3 N. Therefore, an excessive \(H\) value has adverse effects on the friction performance of the piston. When \(H\) reached its maximum limit of 1.5 mm, both \(F+\) and \(F-\) exhibited their highest average values, which is not ideal for our purposes. Taking into account the overall trend in the variations of \(F+\) and \(F-\), a value of \(H\) equal to 0.5 mm was deemed most advantageous for optimizing the piston's friction performance. \(F+\) and \(F-\) both achieved the minimum value at \(N=36\), which means that the friction performance of the piston was better when \(N\) was larger.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 3 Proposed Method
### VGG19 as Encoder  
VGG19 [42] is a CNN architecture created by the Visual Geometry Group at Oxford University. VGG19 remains a popular CNN architecture and computer vision due to its robust architecture and performance. It extends the VGG16 model with additional layers, enabling it to capture more intricate features. VGG19 is composed of 19 layers that consist of the following:  
* 16 convolutional layers containing kernels with a size of \(3\times 3\), a stride of 1, and padding of 1, which preserves the spatial resolution of the input;
* three fully connected layers at the end of the architecture with 4096, 4096, and 1000 nodes, respectively;
* five max-pooling layers with a filter \(2\times 2\) in size and a stride of 2 to reduce the spatial dimensions.  
Using VGG16 as an encoder in U-Net is particularly advantageous for sun glare segmentation, where high-quality feature representation and spatial detail retention are essential. VGG16 is commonly pre-trained on large datasets like ImageNet, which means it has already learned general image features (e.g., edges, textures, shapes). These pre-trained weights can improve segmentation performance, particularly when data are limited, as the model starts with robust feature representations. By using pre-trained weights, the model often converges faster because it begins with a more \"knowledgeable\" encoder, reducing the time needed to learn from scratch. It begins with basic edge and texture features in the initial layers and captures more abstract representations in the deeper layers. This is valuable for segmentation, where both local and global contexts are essential. Finally, the depth of VGG16 provides a good balance of complexity without being too computationally intensive, allowing it to capture useful hierarchies of features.
## 1 Introduction
This enhances the biodegradation of organic matter and is cost-effective and efficient [8]. In South Africa, PSW is disposed of through municipal wastewater treatment systems. AS systems are primarily used in domestic wastewater, livestock, and PSW treatment [19]. The disadvantage of municipal wastewater treatment systems, especially in PSW treatment, is the prevalence of biofoam, which occurs due to the presence of protein and FOG in the wastewater. To overcome this problem, synthetic defoamers have been used. However, reports have shown that they are toxic to humans and the environment; hence, there is a need to produce biodefoamers [20; 21]. To regulate AS and biofoam formation, several techniques are applied, i.e., mechanical (stirring), thermal, chemical (the addition of polymers and chlorination to regulate filamentous microbial growth), and biological [22]. Natural means (biodefoamers) in AS regulation would thus be preferable due to the technique's eco-friendliness and reduced input costs. Overall, AS regulation can improve its dewatering, reduce its bulking [23], and provide sloughing of inactive sludge particles, including filaments, i.e., turning them into soluble substrates, which are reduced to minimize foaming, thus imparting the AS granular formation and maintaining the sludge balance (keeping the mixed liquor TSS within a defined range). Filament sloughing can ensure the AS's health while reducing foaming and bulking, thus improving PSW treatment efficiency. Preferably, biodefoamers have antagonistic characteristics against biofoam formers for AS regulation. This study aimed to apply biodefoamers that were produced by a consortium isolated from PSW, as reported elsewhere [21], in an AS-designed bench-scale treatment system, whereby an assessment of the biodefoamer antimicrobial activity in terms of volumetric zones of inhibition (VZI, described by [PERSON]) [24] against the PSW and mixed liquor suspended solids (MLSS) microbial community in the form of AS and the removal of FOG, TSS, proteins, and COD. A comparison between bio- and synthetic-defoamer-supported activated sludge (Bio/Syn-AS) systems was made for the treatment of PSW. A control system containing no defoamer (CAS) was also used as a control experiment.
## 5 Results and Analysis
### Overall Performance Evaluation  
Table 2 presents the evaluation results of GPT-4o's understanding of questions related to four-character idioms and Sino-Korean words, based on the metrics outlined in Section 5.1. GPT-4o demonstrated consistently high accuracy across all question types. In particular, it achieved 97.0% accuracy for Question Type 2 and 97.5% for Question Type 4. Even in Question Type 1 and Question Type 3, the model recorded accuracies of 88.0% and 97.0%, respectively, maintaining a performance level above 88% across all types.  
An analysis of the incorrect responses revealed that GPT-4o rarely selected incorrect answer choices. Notably, in Question Types 2 and 4, the error rate remained below 3%,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Choice** & **Four-Character Idioms** & **Synonyms** & **Antonyms** & **Homophones** \\ \hline Choice 1 (the answer) & 88.0 & 97.0 & 97.0 & 97.5 \\ Choice 2 & 3.0 & 1.0 & 0.0 & 0.5 \\ Choice 3 & 7.0 & 1.0 & 0.5 & 1.0 \\ Choice 4 & 2.0 & 1.0 & 0.0 & 1.0 \\ No choice selected & 0.0 & 0.0 & 2.5 & 0.0 \\ \hline \end{tabular}
\end{table}
Table 2: GPT-4o’s selection rate for each choice (Unit: %).  
suggesting that the model is highly capable of distinguishing the most semantically appropriate choice among distractors. Furthermore, the near-zero rate of unanswered questions across all types indicates that GPT-4o generated responses with a high level of confidence when presented with questions involving four-character idioms and Hanja.  
The highest accuracy was observed for Question Type 4 (97.5%), demonstrating GPT-4o's ability to identify the most semantically suitable Hanja character among those with identical pronunciations. In contrast, the lowest accuracy was found for Question Type 1. Within this category, the model most frequently selected choice 3, which corresponds to a Hanja expression with the same meaning as the correct answer. This suggests that GPT-4o may encounter difficulties in contextually distinguishing between semantically equivalent Hanja expressions when selecting the most contextually appropriate form.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What Are the Main Types of Learning Techniques Used to Solve 5G Problems?
#### 3.2.3 Unsupervised Learning  
We examined four articles that used unsupervised learning to train the models proposed. In [61], the authors proposed a hybrid approach with both supervised and unsupervised learning to train the model with the purpose to determine an approximate solution for optimal joint resource allocation strategy and energy consumption. The authors in [30] also used a hybrid learning approach, combining supervised and unsupervised learning to train the model in order to identify faults and false alarms among alarm information considering single link connections. In [25], the authors trained a deep learning model through unsupervised learning to map constellation mapping and demapping of symbols on each subcarrier in an OFDM system, while minimizing the BER. In [24], an unsupervised deep learning model was proposed to represent a MU-SIMO system. Its main purpose was to reduce the difference between the signal transmitted and the signal received.
## 3 Results
### C and N Dynamics in Soil with MPs: Physicochemical Indicators
The fact that there was a higher net mineralization rate in T4 could have been related to a greater aeration in the soil matrix (microaggregates) due to the concentrations of the MPs used, which promoted greater biophysical contact with the C and N nutrient sources within the system, favoring a greater availability and increasing the production of N in its most assimilable inorganic forms (N-NO2\({}^{-}\), N-NO3\({}^{-}\), and N-NH4\({}^{+}\)) by the action of the biological phase of the soil. In the present study, high values of N-NO3\({}^{-}\)--mainly in treatment T4--may have also favored the dynamics of N\({}_{\text{min}}\) over time, since, of the three forms of inorganic N, N-NO3\({}^{-}\) was the one that contributed the most quantitatively to N\({}_{\text{min}}\). According to [PERSON] et al. [47], high values of the N-NO3\({}^{-}\) indicator are normally related to a greater nitrification of various N compounds, very similar to what was observed in treatment T4, where the dynamics of both N-NO3\({}^{-}\) and N\({}_{\text{min}}\) were statistically higher (\(p\leq 0.01\)) than those in the other treatments. Another important indicator is NI (N-NH4\({}^{+}\)/N-NO3\({}^{-}\)), as it represents the flow of the N cycle, providing information on whether the system or mineralization processes of C and N sources and their products are directed towards denitrification or nitrification, which is different from N\({}_{\rm min}\), since that represents the general behavior of inorganic N. The NI values presented the following order: T4 \(<\) T3 = T1 \(<\) T2. As can be observed in Figure 4K, there were significant differences between the treatments (\(p\leq 0.01\)). Treatment T4 was the one that presented the lowest value in terms of NI, having a value that was 59.5% below the control treatment T1, which indicated that this type of treatment leads to nitrification. On the other hand, T2 showed significant differences (\(p\leq 0.01\)), being 16.45% above the control treatment T1; therefore, unlike treatment T4, this one was directed towards denitrification. The results and their variations in NI indicated that high concentrations of PP fibers are linked to nitrification processes and vice versa. It should be noted that the indicators N\({}_{\rm min}\) and NI have not been evaluated in other studies related to soils and MPs, however, this study established that they are of the utmost importance, since N\({}_{\rm min}\) can give us an indication of the global mineralization of C and N sources in the presence of an organic amendment, contaminant, or, in this case, MPs in the soil. Moreover, the NI indicator can provide information on the flow or direction of the transformation and assimilation of N via the so-called N cycle.
# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 3 Multiuser Detection Based on Differential Characteristics
### Differential Characteristics Calculation
In conclusion, it is appropriate to set \(\left|\,L(\mathbf{b})\,\right|\) as the differential characteristics function in the multiuser detection algorithm. Here is an example of the proposed differential characteristics function \(\left|\,L(\mathbf{b})\,\right|\) with a condition of 10 users, 6 dB of receiver's output signal-to-noise ratio (SNR), spread spectrum gain \(N_{c}\) = 63, which is shown in Figure 2.  
According to Figure 2, it is clear that most of the wrong bits and the right ones have a significant difference in the feature space mapped by \(\left|\,L(\mathbf{b})\,\right|\) among the 200 tested bits, so it is accessible to map \(\mathbf{b}\) into a one-dimension feature space \(\left|\,L(\mathbf{b})\,\right|\) to preliminarily identify the wrongly received bits.
# Recent Advances in Additive Manufacturing of Fibre-Reinforced Materials: A Comprehensive Review
## 2 Techniques and Materials
### In-Line Impregnation  
This technique involves a hybrid system by combining the conventional method of deposition of fibres and thermosetting materials with the additive manufacturing method. This technique tries to benefit from the advantages of both these methods, from the freedom that robotic deposition from additive manufacturing gives in combination with the mechanical properties of the conventional method. Dry fibre is impregnated in a bath of epoxy resin or some other type of thermosetting material before entering the heated printing head and then followed by the deposition of the polymer through the nozzle onto the build platform [11]. One of the benefits of in-line impregnation is that raw fibres can be used, which are less expensive than pre-impregnated materials, and also the production of parts with customised composites and properties is achievable. Moi Composites employs this technique on a commercial basis and uses vinyl esters, acrylic, and epoxy resins for the matrices, alongside continuous carbon, glass, basalt, aramid, and natural fibres as reinforcement.
## 4 Results of Oscillation Mode under Different Parameters
### Conclusions on Oscillation Mode  
According to Sections 4.1-4.4, the evolution of the oscillation mode for triangular prisms with variations of dampings, stiffnesses, mass and section aspect ratios can be summarized in Figure 15. The corresponding characteristics of branches, self-excited regions, critical velocities, and the ability to be self-excited to galloping are also summarized in Figure 15.  
As the damping and mass increase, or the stiffness and the section aspect ratio decrease, the oscillation mode of a triangular prism gradually transitions from SG to CG and continues to HG;  
Figure 15: Evolution of the oscillation mode for triangular prism.  
the ability to be excited from VIV to galloping is continuously weakened. Correspondingly, the VIV branches, especially the VIV upper branch, gradually become complete; the galloping branch, including the VIV-galloping transition branch, gradually becomes incomplete. Meanwhile, the upper limit of the self-excited region decreases, the lower limit increases, the region narrows and gradually disappears. The critical velocity appears and gradually increases.
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
## 2 Dynamic Cable Rating
### Thermal Cable Analysis of Transient Conditions  
A three-core XLPE submarine cable contains multiple non-conductive layers. An equivalent electrical circuit can be developed to represent the thermal properties of the cable layers according to the thermal-electrical analogous parameters.  
By constructing a thermal analogy ladder network, it is possible to study the thermal behaviour in the centre of the conductor. Equation (16) is used to calculate the thermal capacitance of the respective layers, where \(S\) is the cross-sectional area of the respective layer, and \(c_{m}\) is the specific heat capacity of the material. Typical heat capacity values can be found in Table 1.  
\[Q=S\cdot c_{m}\qquad[\text{J}/\text{m}\cdot\text{K}] \tag{16}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Material (Abbreviation)**} & **Thermal Resistivity [10; 14]** & **Heat Capacity [15; 16]** \\  & \(\rho\) [**K**\(\cdot\) **m/W]** & \(c_{m}\) [**J/K**\(\cdot\) **m\({}^{3}\)]** \\ \hline Copper (Cu) & - & \(3.46\times 10^{6}\) \\ Aluminum (Al) & - & \(2.46\times 10^{6}\) \\ Lead and lead-alloy (Pb) & - & \(1.47\times 10^{6}\) \\ Polyethylene (PE) & 3.5 & \(2.4\times 10^{6}\) \\ Cross-linked polyethylene (XLPE) & 3.5 & \(2.4\times 10^{6}\) \\ Polypropylene (PP) & 10 & \(1.8\times 10^{6}\) \\ Seabed sand/gravel & 0.6–0.9 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Commonly used thermal resistivity for materials and surroundings.