esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Questionnaire Survey and Data Analysis
#### 4.3.4 Analysis of Questionnaire Data about the Scale of Multimedia Technology Understanding
The percentage of agreement (including \"agree\" or above) for the questions in this dimension varies significantly, ranging from 54.2% to 83.8%, indicating that observers hold diverse opinions and perspectives on these questions. 2. For question T11, the percentage of agreement is 83.8%, with an average value of 4.15 and a relatively small standard deviation of 0.77, indicating that the way multimedia technology is used does not significantly affect the audience's evaluation of the show. 4.3.4. A Summary of Questionnaire Survey Data Analysis The following is a summary of the results of the questionnaire survey data analysis. 1. _Emotional impact:_ The audience consistently provided high ratings of 4.0 or above in response to questions about the emotional impacts, suggesting occurrences of significant emotional influence on the observers during the magic viewing. 2. _Magic impression:_ Positive impressions were gained by the observers during the magic viewing process. 3. _Entertainment satisfaction:_ The observers sensed an overall entertaining atmosphere with positive experiences in various aspects, especially the audio-visual effects and stage design. 4. _Multimedia awareness:_ The observers demonstrated a relatively high level of awareness regarding multimedia technology. 5. _Interaction perception:_ The observers were receptive to digital imagery in magic, but exposure to multimedia and interactive technology is still considered novel. 6. _Technology acceptance:_ Diverse opinions existed regarding the use of multimedia technology, with some observers unaffected by its use, while others preferred not to see magic shows with pure body movements.
## 4 Dynamic Error Propagation Model in UWB-Based Positioning
### Errors Caused by Atmospheric Interferences  
In general, UWB wireless communication signals can be disturbed by the atmosphere during their propagation. The ranging distance \(D\) that accounts for atmospheric interference conditions can be calculated as [43]  
\[D=ct/n \tag{9}\]  
where \(c\) denotes the speed at which light waves travel in a vacuum; \(t\) denotes the propagation time of the signal between the transmitter and receiver; and \(n\) = _f(T,P,E,\(\lambda\))_ in which \(T\) denotes the temperature, \(P\) denotes the air pressure, \(E\) denotes the humidity, and \(\lambda\) denotes the mean value of the signal wavelength.  
To a certain extent, \(T\), \(P\), and \(E\) will affect the transmission of light speed in the form of electromagnetic waves, which normally travel in a straight line, and are deflected by changes in atmospheric conditions as they pass through the atmosphere.  
Figure 8: Reflection model of UWB signal propagation.  
The refractive index of the atmosphere, \(n_{0}\), under standard atmospheric conditions can be calculated as [44],  
\[n_{0}=1+\left(2876.04+\frac{48.684}{\lambda^{2}}+\frac{0.68}{\lambda^{4}}\right) \times 10^{-7} \tag{10}\]  
According to the arbitrary \(n_{0}\) calculated under atmospheric conditions \((t,P,e)\), the atmosphere refractive index is then  
\[n=1+\frac{n_{0}-1}{1+\alpha t}\times\frac{p}{760}-5.5\times 10^{-8}\times e/(1+ \alpha t) \tag{11}\]  
where \(\alpha\) denotes the coefficient of air expansion. According to the experimental description in the literature [44], under environmental conditions of temperature = 21 \({}^{\circ}\)C, relative humidity = 71%, and atmospheric pressure = 101.325 kPa, the error caused by the atmospheric refractive index can be calculated approximately at the centimeters level and increases with the observation distance.
## 2 Methodology
### Process Description  
Figure 2 shows the process diagram for chitosan extraction from shrimp wastes with the following stages:  
* Pretreatment
* Depigmentation
* Demineralization
* Deproteinization
* Deacetylation \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Chemical Processes** & **Process Simulation** & **NuDIST Technique** & **Reference** \\ \hline Methyl methacrylate manufacturing & & + & [14] \\ \hline Biodiesel production & & + & [15] \\ \hline Catechol production from lignin & + & & [16] \\ \hline \hline \multicolumn{3}{c}{Fermentative hydrogen production from food waste} & + & & [17] \\ \hline Palm oil recovery & & + & [18] \\ \hline Hydrodealkylation process & & + & [19] \\ \hline Furfural and biogas production using pentoses & + & & [20] \\ \hline Production of chitosan from shrimp wastes & + & + & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of contributions about the application of the numerical descriptive inherent safety technique (NuDIST) tool and process simulation on chemical processes. Figure 1: Schematic representation of the methodology. The reactions taking place in each stage were collected from the literature and are summarized in Table 2. The first step in the production of chitosan from shrimp waste is the pretreatment that includes: (1) washing to eliminate impurities, (2) drying to reduce moisture content, and (3) crushing to reduce particle size up to 0.5 mm. The resulting material is fed into depigmentation stage to mix with 85%vol. ethanol, the latter is used to drag the astaxanthin from the chitin. Inter-stage neutralizations are needed to adjust the pH at 7 before entering to the following reaction process. During demineralization, the reactions taking place are the formation of chlorides from carbonates using hydrochloric acid. The chitin-reach stream is sent to the deproteinization stage to separate proteins with a solution of NaOH [21]. The extracted chitin undergoes a deacetylation process at high temperatures and basic environments to produce chitosan. This product is finally filtered, washed, and dried for further commercialization [22]. Gathering of Process Data from Computer-Aided Simulation  
The simulation of large-scale chitosan from shrimp wastes performed via Aspen Plus (r) provided process data such as extended mass and energy balances around the system. The chemical composition of shrimp exoskeleton was modeled considering amino acids, fatty acids, carbonates, and pigment, that are available in the software database. The processing capacity corresponds to 57,000 tons shrimp wastes per year following a previous work [21]. The thermodynamic model for property estimations was the electrolyte non-random two-liquid (eNRTL) model owing to the use of aqueous acids and bases
## 4 Experimental Methodology
### Feature Selection  
After the one-way ANOVA and \(t\)-test analyses were performed, the analysis of statistical results along with the analysis of the obtained histograms were followed to select relevant features for the training of the following machine learning algorithms. The selection of the features also considered the fact that, for the potential application of the classification model in practice in terms of optimizing data processing times, it is necessary to select the same parameters for each axis.  
It can be concluded that among the listed signal properties, none can be used to explicitly identify anomalies in the signal waveform from all three sensors in each axis, suggesting the need to use a set of properties to identify the errors. However, based on the above, some significant dependencies in data can be observed.  
Mean is consistently one of the most important characteristics in most data sets and often shows the highest \(T_{score}\) in the case of \(t\)-test and \(F_{score}\) in the case of one-way ANOVA. It can also be seen in the bar graphs in Figures 7 and 8 that the values of the statistical score for a given trait are much higher in most cases than the values of any other feature. RMS and Std are also often significant in distinguishing between groups of anomalous and normal data. These features are likely to be crucial for classification tasks.  
Peak Value in addition to Mean, RMS, and Std, has a relatively high \(F_{score}\), suggesting that it is also a potential key feature for classification. For the above parameters, the alternative hypothesis was accepted in most cases (6 features out of 9 for \(t\)-test and 7 out of 9 for one-way ANOVA). The acceptance of the null hypothesis for Mean, Std, and RMS in the case of \(Z_{1}\) is likely due to the sensor location in the middle of the front face of the base of the robot and, hence, significantly less variation in the anomalous data compared to sensors that are located near the fixing screws.  
After generalizing the above, we decided to use Mean, Std, RMS, and Peak Value parameters as key metrics for designing and training machine learning algorithms for the detection classification of normal and abnormal vibration sequences. The selection of the key features allowed us to reduce the dimensionality from the original 27 parameters for a single sensor (81 parameters for the whole dataset) to 12 parameters for a single sensor (36 parameters for the whole dataset). In addition, the Balluff BCM0002 sensors, when properly configured with the sensing profiles, allow direct sensing of three of the selected metrics, namely, Mean, RMS, and Std of the signal, which is also a benefit for feature implementation of the trained models.
###### Abstract  
This paper describes a comparison between hybrid and end-to-end Automatic Speech Recognition (ASR) systems, which were evaluated on the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge. Deep Neural Networks (DNNs) are becoming the most promising technology for ASR at present. In the last few years, traditional hybrid models have been evaluated and compared to other end-to-end ASR systems in terms of accuracy and efficiency. We contribute two different approaches: a hybrid ASR system based on a DNN-HMM and two state-of-the-art end-to-end ASR systems, based on Lattice-Free Maximum Mutual Information (LF-MMI). To address the high difficulty in the speech-to-text transcription of recordings with different speaking styles and acoustic conditions from TV studios to live recordings, data augmentation and Domain Adversarial Training (DAT) techniques were studied. Multi-condition data augmentation applied to our hybrid DNN-HMM demonstrated WER improvements in noisy scenarios (about 10% relatively). In contrast, the results obtained using an end-to-end PyChain-based ASR system were far from our expectations. Nevertheless, we found that when including DAT techniques, a relative WER improvement of 2.87% was obtained as compared to the PyChain-based system.  
Keywords:TV show speech-to-text transcription; ASR systems; hybrid DNN-HMM; end-to-end deep learning; domain adversarial training +
Footnote †: journal:
# Application of Simultaneous Symmetric and Cambered Airfoils in Novel Vertical Axis Wind Turbines
## 3 Methodology
### Numerical Domain  
The numerical domain and boundary conditions are depicted in Figure 2[53]. Two domains, rotating and fixed domains, are connected by an interface boundary condition. The rotating domain rotates inside the fixed domain via the sliding mesh technique for computing the power coefficient. The selected numerical domain can predict the performance of wind turbines, which is evaluated in the validation section. Figure 2b displays NSA-1 as an example in two rotation angles, \(\Delta\theta\) of \(0^{\circ}\) and \(45^{\circ}\)[53]. Note that VAWT blades at \(\Delta\theta\) of \(0^{\circ}\) and \(45^{\circ}\) are marked with gray and black, respectively. In Figure 2, D is the rotor diameter. The airfoils of the rotor have zero angles with respect to the rotor; that means the chord lines of airfoils are tangential to the circular border of the rotor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & \\ \hline Airfoils & - & NACA0018 and S815 & - \\ Rotational Velocity & \(\omega\) & 76 (constant) & Rad/s \\ Wind Velocity & U\({}_{\infty}\) & 11.21 to 29.89 & m/s \\ Tip Speed Ratio & \(\text{TSR}=\frac{\text{R}\cdot\omega}{\text{U}_{\infty}}\) & 1.5, 2, 2.5, 3, 3.5, 4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
Figure 2: Domain and boundary conditions: (**a**) whole view of the domain; (**b**) close view of the rotating domain of NSA-1.
# Accident Risk Analysis of Gas Tankers in Maritime Transport Using an Integrated Fuzzy Approach
## 2 Backgrounds
### Operational Security Procedures and General Risk Assessments  
The development of operational safety procedures within the scope of GCT and the comprehensive assessment of risks encountered during this process play a fundamental role in enhancing the safety of ship operations. In this context, studies have contributed to the development of effective safety management strategies by analysing the unique risk profiles of different types of gases used in transportation processes. For instance, the risk factors posed by LNG and LPG during transportation and the effectiveness of the safety approaches applied to these two gas types have been evaluated [28]. In a separate study, the operational procedures for oil tankers and gas carriers were analysed, with a focus on the scope and implementation processes of safety protocols on board ships [29].  
Furthermore, it acknowledges that human error is a major factor in a substantial proportion of accidents involving gas tankers. It has been documented that over 80% of such incidents are attributable to human factors, either directly or indirectly [9]. These findings underscore the necessity to fortify operational safety practices not solely through technical systems but also by prioritising human-machine interaction.  
Table 1 provides a chronological and categorised summary of key studies, their methodologies, years, and significant findings related to fire, explosion risks, fuel leakages, and operational safety in the transportation of GCT.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Methodology** & **Year** & **Key Findings** & **Section** \\ \hline
[15] & Quantitative Risk Analysis & 2014 & Evaluated fire and explosion risks in LNG-FPSO LNG  
The extant literature focuses on critical risk factors in GCT transportation, such as fire, explosion, fuel leakage, and operational safety, and evaluates these risks using different analysis methods and simulation techniques. However, most of these studies focus on a specific event or system and address specific safety measures and technical solutions. The present study diverges from preceding research in its comprehensive consideration of both operational and environmental risks of accidents. Utilising a holistic approach, it employs a hybrid fuzzy model to analyse these risks. A survey of the extant literature reveals that most studies have focused on specific topics, including \"Fire and Explosion Analyses [20]\", \"Risk Assessment of Fuel Leaks [23]\", and \"Safety and Operational Procedures [29]\". This study, however, comprehensively addresses all such risks and is among the few studies evaluating the operational effects and environmental damage of potential accidents in gas tankers in an integrated manner within the same model.
## 2 Summary of the Paper  
The objective is to seamlessly integrate (into a single platform) three system ingredients: cognitive radio, cognitive radar and anti-jamming. One primary task of this proposed research is to provide new analytical and computational tools to allow for practical implementation of compressed sensing and, eventually, to aid the design of sensors that are capable of carrying out direct measurements motivated by the established theoretical bounds. Radar is a remote sensing system well suited for cognition [14]. The knowledge-based cognition is inherent in cognitive radio and radar, and can therefore be jointly designed together with anti-jamming. For example, waveform designs for radar sensing [15] and cognitive radio [23, 24] can, indeed, be co-designed in the same framework. Both underlay scheme (e.g., UWB radio IEEE 802.15.4a [16, 25, 26, 27]) and overlay scheme (e.g., TV band cognitive radio IEEE 802.22) can be studied in the unified framework for both cognitive radio and cognitive radar. Anti-jamming is the prerequisite for most DoD communication and radar applications, and can be naturally fitted into the unified framework. The focus is on the basics and proving the concept: multi-GHz waveforms and spectrum sensing, theoretical framework, and hardware testbed. The primary challenge is caused by the wideband (multi-GHz) nature of the problem at hand. Compressive sampling provides a new paradigm to greatly relax the ADC, which simplifies the front end. Compressed sensing can be also used to reduce the ADC for radar sensing [28]. On the other hand, wideband (multi-GHz) spectrum sensing--very challenging--is used for dynamic spectrum access. New anti-jamming capabilities can be explored in this novel framework. Experimental systems with real life imperfections are proposed Figure 3.  
Below is a list of topics being discussed extensively in the following sessions:  
* Spectrum Sensing for Cognitive Radio
* A Survey on Cognitive Radio Implementation Research
* Multi-Giga-Hertz Agile Radio Front-End Design
* A Compressed Sensing Based Ultra-Wideband Cognitive Radio
* Wideband Waveform Optimization for Multiple Input Single Output Cognitive Radio Using Time Reversal
* A Unified Framework for Cognitive Radio and Cognitive Radar
* Quickest Spectrum Sensing
* Soft Decision Cognitive Radio and Hybrid Overlay/Underlay Cognitive RadioWaveform Design
## 3 Materials and Methods
### Assessment Index System
* Economic efficiency index  
The selection of economic efficiency indicators mainly considers capital, resources, and other elements; in view of the availability of data at the district and city level, three indicators were selected: energy consumption per unit GDP, fixed asset investment of the whole society, and total retail sales of consumer goods. * Ecological carbon sequestration index  
The amount of organic matter accumulated in the unit of net photosynthetic productivity of plants is called net primary productivity (NPP). It represents the result of the interaction between plant biological characteristics and external environmental factors. As a key parameter of the terrestrial ecological process, NPP is an indispensable part of understanding the process of the terrestrial carbon cycle. It is also an important index to evaluate the structure and function of the ecosystem and the population carrying capacity of the biosphere. The annual NPP is derived from the sum of all-day net photosynthesis (PSN) in a given year. The PSN value is the difference between total primary productivity (GPP) and total organic matter (MR) to maintain respiration. The calculation formula is as follows:  
\[\text{NPP}=\sum_{i=1}^{n}\text{PSN}. \tag{1}\]  
\[\text{PSN}=\text{GPP}-\text{MR}. \tag{2}\]
* Land-cover index  
The vegetation, architecture, and water body were selected as the indicators of land cover, and the normalized difference vegetation index (NDVI) was used to reflect the ecological status. In this paper, NDBI and NDWI were innovatively introduced into the ecological index system. The normalized difference building index (NDBI) was used to reflect the urban building coverage, and the normalized difference water index (NDWI) was used to reflect the water coverage. The calculation formulas of NDVI, NDBI, and NDWI are as follows:  
\[\text{NDVI}=\frac{(\text{NIR}-\text{Red})}{(\text{NIR}+\text{Red})}, \tag{3}\]  
\[\text{NDBI}=\frac{(\text{MIR}-\text{NIR})}{(\text{MIR}+\text{NIR})}, \tag{4}\]  
\[\text{NDWI}=\frac{(\text{Green}-\text{NIR})}{(\text{Green}+\text{NIR})}, \tag{5}\]  
where NIR and MIR represent reflectance at the near-infrared and mid-infrared bands, while red and green represent reflectance at the red and green bands, respectively. The NDVI index, also known as the biomass index, is closely related to the transpiration and photosynthesis of plants; the combination of MIR and NIR constitutes the NDBI index. NDBI is mainly based on the high reflectivity of urban construction land in the TMS band. The research on urban land is often combined with NDVI; the combination of green and NIR constitutes the NDWI index. Due to the large number of rivers and lakes in Wuhan, the impact of water bodies on the ecological environment cannot be ignored. Therefore, the NDWI index was introduced to reflect the water coverage in Wuhan. The three indices were normalized, with values in the range of [\(-\)1, 1].
# Practical Performance Analysis of Interference in DSS System
## 5 Field Test and Performance Analysis
### Test Results of Rate Matching  
Since the LTE of the DSS cell will cause serious interference to the NR of the DSS neighbor cells, we verify the interference mitigation after rate matching. The test environment is shown in Figure 13, and the no-load and 50% load conditions are verified, respectively, and the base stations are all turned on. The test results are shown in Figure 20.  
Figure 19: LTE to NR downlink interference test results—buffer setting (50% load).  
Figure 18: LTE to NR downlink interference test results—buffer setting (no load).  
As can be seen in Figure 20, regardless of whether it is no-load or 50% load, with rate matching, the LTE interference to NR in the neighbor DSS is significantly reduced. The downlink throughput of NR can be improved by about 60% at different points. It can be seen that the rate-matching scheme can significantly improve the user's network experience without setting the buffers. Therefore, in the case of severe interference, choosing the rate-matching scheme can steadily increase the throughput and mitigate the interference. The test results are consistent with the theoretical analysis results in Section 4.3.  
In summary, both the buffer setting scheme and rate matching can effectively mitigate interference, but the buffer setting scheme has a large difference in performance improvement at different locations and lacks stability. The rate-matching scheme can steadily improve the performance of different locations by 60%, and the improvement effect is obvious, which is consistent with the theoretical analysis results. Although rate matching will bring a certain amount of resource overhead, the benefits in interference mitigation are significant, which can effectively and stably improve the user experience.
# Fixed Wireless Access in Flexible Environment: Problem Definition and Feasibility Check
## 3 Assumptions and Inputs
### Service Traffic of the Planned Location  
The connectivity requirements can be split to multiple service traffic for different services [30; 31]. Let us mark these service traffic as (\(st_{1},st_{2},\ldots,st_{m}\)). Each service traffic \(st_{i}\) is defined by:  
1. the planned connection target(s): as a subset of \(pCT\) in timeslot \(t_{j}\); \(pct\)(i, j),
2. the connection parameters between PL and \(pct\)(i, j) in timeslot \(t_{j}\), including 1. the required throughput, \(TP_{\text{PL}}(st_{i},t_{j})\), defined as a quadruple of: * guaranteed bitrate for uplink, \(GBR_{\text{UL}}\)(i, j), * guaranteed bitrate for downlink, \(GBR_{\text{DL}}\)(i, j), * maximum bitrate for uplink, \(MBR_{\text{UL}}\)(i, j), and * maximum bitrate for downlink, \(MBR_{\text{DL}}\)(i, j).  
In general, available throughput meets the requirements if all guaranteed bitrate parameters are fulfilled. The handling of maximum bitrate parameter can be defined by policy (either ignored or considered in an objective function when selecting a radio link if multiple solutions are found). It is assumed that the throughput parameters do not represent average figures but include overhead to serve traffic spikes that may happen as part of normal deviations in traffic. Extreme traffic spikes can be served if capacities are available (withoutimpacting other services) as best effort services, or traffic is dropped/delayed (reducing service availability). b. the required availability, \(A_{\rm PL}(st_{\rm i},t_{\rm j})\); and c. the tolerated latency, \(L_{\rm PL}(st_{\rm i},t_{\rm j})\).