esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech
## 4 Experimental Results
### Proficiency Evaluation Test
#### 4.2.4 Features for Proficiency Scoring  
All features for proficiency scoring are computed based on the time-aligned phone sequence and its time information [11; 12; 14]. Table 6 shows the proficiency scoring feature list used to train the automatic proficiency scoring models in this work.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Feature Name**} & \multicolumn{1}{c}{**Description**} \\ \hline Genie\_pron & Pronunciation score \\ SLLR & Sentence-level log-likelihood ratio \\ amscore0/amscore1 & Acoustic model (AM) score/anti-model-based AM score \\ Uttgesdur & Duration of entire transcribed segment but without inter-utterance pauses \\ Globsegdur & Duration of entire transcribed segment, including all pauses \\ wpsec/wpsecutt & Speech articulation rate/words per second in utterance \\ Silpwd/Silpsec & Number of silences per word/second \\ Numsil & Number of silences \\ Silmean/Silmeandev/Silstddev & Mean/mean deviation/standard deviation of silence duration in second \\ Longpfreq/Longpwd & Frequency/number of long pauses per word (0.495 s \(\leq\) duration) \\ Longpnm/Longpmeandev/Longpstddev & Mean/mean deviation/standard deviation of long pauses \\ Wdpchk/Scepchk & Average speech chunk length in words/seconds \\ Wdpchkmeanedev/Scepchkmeanedev & Mean deviation of chunks in words/seconds \\ Repfeq & Number of repetitions divided by number of words \\ \hline \hline \end{tabular}
\end{table}
Table 6: Features for the proficiency scoring modeling.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Proficiency Level** & **Applied Method** & **1** & **2** & **3** & **4** & **5** \\ \hline Train database only & - & 57.3 & 53.4 & 30.1 & 23.4 & 22.7 \\ Augmentation & SpecAugment & 52.1 & 45.4 & 27.2 & 21.9 & 20.6 \\  & Speed perturbation & 51.3 & 44.9 & 27.0 & 21.8 & 20.7 \\  & CVAE-SC & 49.3 & 43.1 & 26.0 & 21.5 & 20.4 \\  & Proposed method & 40.9 & 37.7 & 24.5 & 20.5 & 19.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Speech augmentation and word error rate (%) results.  
Figure 9: Results of the AB test and the ABX test.
## 4 Results and Discussion  
### Calculations  
Figure 2 shows the dependence of \(\Delta t_{\mathrm{REAL}}\) on \(\alpha X_{1/2}\) (or \(\Delta t_{\mathrm{FIL}}\) on \(X_{1/2}\)), which was calculated in the range from 2\(\upomega\) to 16\(\upomega\) (\(\omega\) = 4155 cm\({}^{-1}\)) using Equation (16). Because the dispersion increases at higher frequencies, the pulse width increases even at the same value of \(\alpha X_{1/2}\). For example, when \(\alpha X_{1/2}\) = 1 mm, the pulse width becomes 5.0 fs at 2\(\upomega\) and 95 fs at 16\(\upomega\). Another example would be \(\alpha X_{1/2}\) = 10 mm, providing pulse widths of 16 fs at 2\(\upomega\) and 300 fs at 16\(\upomega\). Therefore, the pulse width can be evaluated based on the observed data to show the dependence of the two-photon ionization signal on the displacement (\(X\)) of the second prism in the pulse compressor.
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 4 Deterioration of CH Artifacts: Causes and Process
### Organic Materials
#### 4.2.4. Textiles  
Textiles can be divided into two main groups, depending on the origin of their fibers: vegetable and animal textiles. The main components are keratin for wool and fibrin for silk.  
Even for textiles, the manufacturing processes are crucial for determining their deterioration and disposal. Loosely woven textiles accumulate dust more easily, and dust is hydrophilic; hence, its presence facilitates water retention and increases humidity levels, which creates a favorable environment for any biodeteriogens. On the contrary, the presence of precious metals, typical of ancient precious textiles, has a microbiological and bactericidal action.  
While cellulose is more vulnerable to fungi, proteins (the main component of textiles of animal origin) are more vulnerable to bacteria. However, insects pose the greatest danger of all to textiles.
###### Abstract  
In this paper, we propose an approach combining optimal softening laws and a phase-field regularized cohesive zone model (PF-CZM) for modeling the fracture and damage properties of quasi-brittle materials accurately. In this method, the optimal softening law is determined by comparing the predicted results with experimental data in the framework of the PF-CZM; three typical softening laws are considered. The PF-CZM with a length scale is used to model crack initiation and propagation without considering the mesh bias. We first investigate the mechanical responses and crack propagations of different concrete beams based on the above approach; the predicted results are compared with the data from conventional methods and experiments. The results indicate that the mechanical properties of concrete beams with the optimal softening law are better than the data reported in the literature. Further validation indicates that once the optimal softening law is determined, it is stable for the same group of materials. Moreover, we demonstrate that the PF-CZM can naturally predict and reproduce the critical notch offset and fracture transition process of three-point bending concrete beams and the fracture features of typical double-notched concrete beams, such as the interaction between two notches objectively, together with the changes of limit load capacity.  
quasi-brittle materials; phase field; damage; fracture; crack propagation; optimal softening law Article
## 3 Results and Discussion
### Adsorption Study  
The adsorption of MB was assessed through UV-vis spectral analysis, with the maximum sorption capacity at equilibrium and the dye separation percentage being calculated  
Figure 1: (**a**) FESEM micrograph of AG, (**b**) higher-magnification FESEM, (**c**) pseudo-color topographic contrast of SEM image, (**d**) FTIR, (**e**) XRD, (**f**) Raman spectra of AG.  
as per Equations (1) and (2). The data obtained are plotted in Figure 2a. It is clear from Figure 2a that the adsorption capacity (\(q_{e}\)) increases rapidly at a lower equilibrium concentration (\(C_{e}\)). At a \(C_{e}\) of 150 mg L\({}^{-1}\), the maximum qe exceeded 650 mg g\({}^{-1}\), achieving a removal efficiency greater than 97% within 80 min. However, once a saturation peak was reached, the sorption process leveled off, with only minimal additional adsorption observed. The maximum sorption capacity attained was 691.89 mg g\({}^{-1}\), equating to a 98.32% removal rate within 120 min. It is anticipated that the microstructure morphology of the adsorbent drove the adsorption process. The presence of numerous reactive carbonaceous groups (epoxy, hydroxyl, carboxylic, and phenolic) in the AG facilitated an electrostatic interaction with the dye's mass center. As a result, the AG demonstrated superior adsorption compared to the other similar graphene family materials (as shown in Figure 2b) (relevant data detailed in Supplementary Table S3). The skeleton structures of these materials are nearly identical and likely to exhibit some degree of \(\pi\)-\(\pi\) stacking interaction. However, higher levels of oxygen functionalities may result in a lower affinity for the dye molecules, leading to either reduced adsorption or an extended time needed to achieve equilibrium.
## 2 Related Research
### Novelty and Contribution of This Study  
The novelty of this study lies in the development of a motion control model capable of representing individual motion generation. Previous studies have designed optimization problems for motion generation and provided valuable insights into movement mechanisms. However, they primarily focused on applying human motion to bipedal robot control and trajectory generation without fully explaining individual differences or the complete process of motion generation. Additionally, while machine learning-based motion generation achieves high accuracy, its generative mechanism remains a black box, making the process from input to output unclear.  
In this study, we aim to construct motion control by adopting a bottom-up approach, systematically integrating the fundamental elements of motion generation. Specifically, we emphasize a white-box design to ensure transparency in control mechanisms, thereby establishing a framework that allows an intuitive and mathematical understanding of motion generation principles. Moreover, human body control exhibits redundancy, leading to an overwhelming amount of information that must be accounted for. To address this issue, we employ a simple dynamical model to capture motion holistically and regulate its parameters to abstractly represent the complex human system, thereby simplifying the problem.  
Based on this approach, we design controllers that incorporate factors such as energy and stability, which are presumed to be considered in human movement optimization. This allows us to describe movement's abstract and linguistically challenging aspects as \"motion strategy\". Furthermore, we introduce a new concept, \"motion tactics\", which serve as a bridge between strategies and dynamics, enabling the implementation of strategy-based movement generation. Additionally, by adjusting the weighting of multiple tactics, we aim to represent individual differences in movement. Through this, we construct a model that quantitatively describes individual movement generation, contributing to a more systematic understanding of movement.
# Analysis of Vibration Characteristics of the Grading Belt in Wolfberry Sorting Machines
## 3 Results and Discussion
### Analysis of the Results of Structural Optimization Design  
#### 3.5.1 Response Analysis Results  
The harmonic response simulation analysis of the belt drive system in the fresh wolfberry sorting machine was performed using the mode superposition method. Figure 22 presents the frequency-response curve of the lateral vibration for the belt drive system with a fixed classification device within the 0-20 Hz range. Experimental results indicate that an excitation frequency of 4 Hz induces a resonance peak of 0.33 mm, while an excitation frequency of 8 Hz induces a resonance peak of 0.42 mm. This occurs because the excitation frequency approaches the natural frequency of the graded belt. Lateral vibration of the graded belt is significantly intensified only when the excitation frequency couples with its natural frequency. Consequently, severe resonance responses are observed at excitation frequencies of 4 Hz and 8 Hz.
## 4 Pool-Experiments Verification and Discussion
### The Control Voltage of Underwater Manipulator  
In this section, the comparative experimental analysis of the joint control voltage of the proposed method and the method in [17] is carried out, to verify that the proposed method can reduce the chattering of control voltage.  
Under this circumstance of continuous trajectory tracking experiments, the target trajectory is Equation (32). The contrast experimental results of the two methods are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MATE** & **SDE** & **Motion Lag Time** \\ \hline The proposed method & (1.971, 2.716, 2.660, 2.247) deg & (0.999, 1.149, 1.159, 0.896) deg & (3.0, 2.9, 4.4, 3.2) s \\ The method in [17] & (3.086, 3.826, 3.942, 3.678) deg & (1.152, 1.340, 1.462, 1.333) deg & (3.9, 4.1, 5.5, 4.7) s \\ Reduced by & (36.13\%, 29.01\%, 32.52\%, 38.91\%) & (13.28\%, 14.25\%, 20.73\%, 32.78\%) & (23.08\%, 29.27\%, 20.00\%, 31.91\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pool-experiment results of sinusoidal trajectory tracking.  
Figure 4: Joint trajectory tracking curve of each joint of underwater manipulator. (**a**) Neck joint; (**b**) shoulder joint; (**c**) elbow joint; and (**d**) wrist joint.  
To give a quantitative analysis, the results of Figure 5 are summarized in Table 5. In addition, consistent with the reference [25], the standard deviation (SD) was used as the evaluation index for the chattering amplitude of the control voltage, and the standard deviation of the derivatives of control variables (SDDCV) was used to evaluate the chattering frequency of control voltage.  
From Table 5, compared with the method in [17], the above results validated that the chattering amplitude of the control voltage was roughly the same as the method in [17], but the proposed method could greatly reduce the chattering frequency, which is beneficial to the protection of the motor of the underwater manipulator.
## 4 Test Results
### Flexural Test Performance  
Table 6 summarizes the flexural strength (\(f_{r}\)), the toughness change for each section, and the residual stress (\(R_{e3}\)) for the flexural test performance using four types of concrete floor waterproofing reinforcement. In addition, according to the type of fiber reinforced inside the concrete as well as the fiber mixing ratio change, the correlation with the waterproofing reinforcement under concrete was clearly shown. Figure 7 shows the change in flexural strength due to the four types of waterproof reinforcement. Overall, the flexural strength due to the reinforcement of the waterproofing agent showed better strength than the OPC. Among the specimens reinforced with PE, the results of the fiber mixing showed an improvement in flexural strength in the order of SF20, MF1.0, and MF2.8. For the specimen reinforced with Typar, the flexural strength was greatly improved, but the effect on the change of fiber type and mixing ratio was found to be insignificant. In the case of the test specimens reinforced with Bituthene, the flexural strength was slightly lower compared to the results of the specimens reinforced with PE, but the tendency for the fiber type and mixing rate change was very similar. For the specimens reinforced with Preprufe, the changes in flexural strength due to SF20 and MF1.0 were insignificant, and the flexural strength of MF2.8 specimens was the best. As a result, it can be confirmed from the test results that specimens reinforced with the MF2.8 showed a very pronounced improvement in flexural strength due to four types of waterproof reinforcements. Figure 4: Load-displacement curve during crack propagation (SF 20). Figure 5: Load-displacement curve during crack propagation (MF 1.0). Figure 8 shows the toughness change for each section resulting from the waterproof reinforcement. The specimen without fiber reinforced inside the concrete showed a sharp decrease in toughness due to the brittle fracture after reaching the maximum load in the proportional limit [29; 39]. On the other hand, for the reinforcement of the waterproof reinforcement material, the concrete matrix cracked after reaching the maximum load in the proportional limit and the waterproof reinforcement reinforced on the concrete surface suppressed cracking and loads instantly. It shows the improvement of toughness due to the continuous load-displacement curve by exerting resistance to crack development. In particular, the toughness of the specimen reinforced with Typar and Preprufe was noticeable against crack propagation.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 2 Related Works  
### Low Power Wide Area Network (LPWAN)  
Recently, the 3 GPP (www.3 gpp.org) has standardized the LTE-M (Long Term Evolution for Machines) and Narrowband IoT (NB-IoT) to provide cellular connectivity for Internet of Things (IoT) devices. These technologies are targeting a significant coverage improvement in order to support the devices located in deep indoor and rural areas [5; 7]. However, the IoT devices may not rely solely on cellular connectivity, but on the radio access networks based on Low Power Wide Area Networks (LPWANs) in general (see Table 1). The most popular long-range LPWAN technologies are LoRa (www.lora-alliance.org), SigFox (www.sigfox.com) and Wi-SUN (www.wi-sun.org). While these technologies operate in sub-GHz unlicensed ISM bands and topologically adhere to a cellular-like structure, the associated operating principles are fundamentally different.  
LoRa (Long Range) is a long-range, low-power, low-bitrate, wireless telecommunications system for the Internet of Things. In LoRa, data traffic can be initiated either by a sensor device or by an external entity for communicating with an actuator device [2; 4; 5]. A typical LoRa network has three different types of devices. The end-devices communicate to the gateway over a single wireless hop via LoRa. The gateway acts as transparent bridges between these end-devices and a central network server LoRa is a good candidate for smart sensing technologies such as health monitoring, smart metering, and environment monitoring as well as industrial applications.  
French company SigFox created an ultra-narrowband IoT communications system designed to support IoT deployments over long ranges. SigFox is a variation of the cellular system that enables remote IoT devices to connect to the access point [2; 5]. SigFox claims that each access point can handle up to a million end-devices, with a coverage area of 30-50 km in rural areas and 3-10 km in urban areas. SigFox operates on the unlicensed spectrum of the 915 MHz frequency band in the U.S. and the 868 MHz frequency band in Europe, with the spectrum divided into 400 channels of 100 Hz.  
Wi-SUN (wireless smart utility network) is a feasible solution to realize wide area IoT systems. Wi-SUN systems are based on IEEE 802.15.4g, which standardizes the physical layer specifications for outdoor low data rate wireless smart metering utility networks [6]. Wi-SUN has been applied to the smart metering systems for infrastructures such as electricity, gas and water. The system is composed of a high-performance base station (BS) and terminal devices with a sensor and meters.
## 4 Results and Discussion
### Accuracy Analysis Using Confusion Matrix
#### 4.1.7. The Conventional Artificial Neural Network (ANN) Consists of Layers and Weights
The first feature set contained eight clinical characteristics that were the same as those on PID, and the second feature set contained eleven additional clinical characteristics that were based on the Oman diagnostic method. A comparison was made between the PID models and the accuracy outcomes. According to the findings, increasing the number of features in a model improves its classification accuracy. The results show an accuracy ranging from 92.39 to 98.37%, which is higher than in Table 6. Training outcomes for all models across all datasets in terms of time and speed are displayed in Table 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **PID Dataset \%** & **Oman Dataset \%** \\ \hline K-nearest neighbours [4] & 91 & 92.39 \\ Support vector machine [6] & 90 & 96.74 \\ Naive Bayes [32] & 91 & 96.73 \\ Decision tree [32] & 88 & 98.37 \\ Random forest [32] & 94 & 98.37 \\ Linear discriminant analysis [7] & 81.1 & 96.19 \\ ANN [8] & 82 & 97.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative performance of our proposed method against the state-of-the-art work on the same dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Features PIDD** & **Accuracy** & **Features** & **Accuracy** & **Features** & **Accuracy** \\  & & **PIDD** & **Oman** & **Oman** & **Oman** & **Oman** \\ \hline K-nearest neighbours & 8 & 75.1 & 8 & 84.2 & 11 & 92.39 \\ Support vector machine; & 8 & 78.4 & 8 & 85.3 & 11 & 96.74 \\ Naive Bayes & 8 & 77.1 & 8 & 87.5 & 11 & 96.74 \\ Decision tree & 8 & 71.89 & 8 & 80.9 & 11 & 98.37 \\ Random forest & 8 & 76.47 & 8 & 85.3 & 11 & 98.37 \\ Linear discriminant & 8 & 77.7 & 8 & 86.95 & 11 & 96.19 \\
\begin{tabular}{c} Artificial neural \\ networks \\ \end{tabular} & 8 & 78.1 & 8 & 86.0 & 11 & 97.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance evaluation of the proposed method on both datasets. While training the model, none of the features were utilised by the trainer due to the following reason. The drop in score between the first and second most essential predictors is significant, while the drops after the sixth predictor are relatively small. A reduction in the critical score represents confidence in feature selection. Therefore, the significant drop implies that the software is confident in selecting the most important predictor. The small reductions indicate that the difference in predictor importance is not significant. The top five most important predictors were selected, as shown in Figure 18. The possibilities for the diabetic prediction from Oman were based on HbA1c and glucose values, and the prospects for the diabetic forecasts from PID were based on the glucose values. These features hold more weightage for the feature processing and have important scores to reproduce the probability of the feature selections.
## 2 Materials and Methods
### Electrochemical Experiments  
Preliminary electrochemical experiments, aiming to establish the appropriate acid kind and concentration for the Cr(VI) electrochemical reduction, were carried out by using a 3 mm diameter glassy carbon (GC) rotating disc electrode (RDE, Metrohm), in a three compartment small electrochemical cell (200 mL), equipped with a KCl-saturated calomel reference electrode (SCE) and a Pt-foil counter electrode. All three electrodes were placed in different compartments, separated by a lugin capillary (reference electrode) and a glass frit (counter electrode). Linear sweep voltammetry experiments (i.e., current recording when the applied potential has been varied at 10 mV\(\cdot\)s\({}^{-1}\) sweep rate) were carried out with the help of an Autolab PGSTAT302N (Eco Chemie) workstation and controlled via the NOVA 1.11.2 software.  
For the electrochemical treatment of Cr(VI) leachates both batch and batch-recirculation prototype reactors were used and constant current or voltage in the two-electrode setup was supplied by a DSC--ELECTRONICS, model DP60-15H, 60 V-15 A power supply.  
The batch reactor consisted of a cylindrical vessel (400 mL capacity), a 4 cm \(\times\) 3.3 cm \(\times\) 0.4 cm RVC cathode (60 ppi, Goodfellow Ltd.) and a 7.5 cm \(\times\) 3.5 cm \(\times\) 0.5 cm platinized Ti anode. The latter was prepared by a variation of galvanic replacement technique presented in [28], using a 5 \(\times\) 10\({}^{-4}\) M K\({}_{2}\)PtCl\({}_{6}\) + 0.1 M HCl\({}_{4}\) solution (prepared from potassium hexachloroplatinate 99% trace metals basis, Aldrich and perchloric acid 70%, Merck). Thorough mixing was achieved via constant magnetic stirring.  
For the electrochemical treatment of 800 mL of Cr(VI) leachate placed in a glass container, an original parallel plate-and-frame flow cell was constructed from polypropylene. The plates and the frame had a cross-sectional area of 300 cm\({}^{2}\) (20 cm \(\times\) 15 cm); the frame was 1.5 cm thick and its void had a hexagonal shape of a cross-sectional area of 155 cm\({}^{2}\), resulting in a solution capacity of 232.5 mL. The gaskets were made from PTFE (i.e., Polytetrafluoroethylene), which provided good sealing and chemical resistance. The two end-plates carry two circular stainless steel current collectors (5 cm diameter) and the entrance and exit ports for the solution (2 cm diameter). The stainless steel current collectors connected to the outside of cell by stainless steel threaded bars screwed into the cell. The anode was 5 \(\times\) 2.5 cm plate of a Metcote ES2 IrO\({}_{2}\) anode (ICI) and the cathode an RVC foam (61 ppi, Goodfellow Ltd.). Both electrodes were attached to the current collectors by using carbon cement (WPI, Inc.). A peristaltic pump was employed to achieve a 33 L h\({}^{-1}\) volumetric flow rate (Figure 1).  
Samples were taken at 30 min (for the small batch reactor experiments) or at 2 h intervals (for the batch-recirculation reactor configuration) and the Cr(VI) concentration was measured by the standard 1,5-diphenylcarbazide colorimetric method (see also the previous Section 2.2).