esa-sceva/satcom-chunk-collection huggingface dataset:
# Moodle LMS Integration with Amazon Alexa: A Practical Experience
## 5 Results
Similar to the activation and account linking processes, we found no evidence of error that might cause users to stop using the skill or which would impact negatively to the experience. As previously mentioned, most of the interactions with the skill happened during the presentation day, with sporadic usages on the same days as the instructor reminded the class about the skill. Once again, based on this insight and the data gathered from the feedback survey, we can resolve that students are not yet familiarized with voice technology and do not feel comfortable as to integrating it on their daily routines. Easy and fast accessibility to content are highly valued needs for students, and platforms like Moodle already provide a solution for such expectations, to a certain degree, while to really match such expectations, Moodle and voice technology require to attain more elaborated integration capabilities than currently available. Today, Moodle offers powerful tools to gather information from external applications, such as the Moodle Web Services. However, in order to create smooth and realistic interactions between users and [PERSON] devices, there is a need for a missing layer of interoperability. As we found during the development process, the information gathered from Moodle via Web Services required to be processed so that [PERSON] could dictate the sentence in a way that the user could make sense out of it. We were forced to implement this missing layer, which required to hard-code into our application part of the data that would be necessary for the construction of those sentences to be spoken by the [PERSON] device. As the data had to be hard-coded, only developers were able to influence and modify the resultant outspoken sentence, but people with no software development knowledge, as, for example, some of the course instructors, could not make any changes. While this is a manageable situation when developing a prototype for close-controlled tests, it is not a plausible alternative for a production-ready application. Instructors must be able to manage information within Moodle to create voice-specific content through the same user interfaces used currently to create visual content displayed on the web. Moodle would have to be enhanced with new forms of inputting information for voice-specific matters, in addition to new tools that will allow instructors to pre-listen the outspoken sentences and do the necessary adjustments, prior to publicly releasing such content. It is when the instructor feels comfortable with the tools to create such voice content that the student would receive a gratifying experience when interacting with voice devices, which then would lead to an increase of the interaction with the voice device, and ultimately making the interaction with voice devices an integral part of the user's educational life.
## 5 The Emotion Detection Visual-Based Component: GEFA
### Facial Features Extraction  
Once the user face has been detected in the observed scene, the next step is to extract the information about the facial expression in an automatic way. There are several computational approaches to represent and extract the information associated with that facial expression.  
One of them is based on interest points, also known as \"local approximation\", and the geometric relationships among them [73, 74]. In this approach, the detection of certain points of the face is needed. Using these points and their relationships, the geometric features are obtained.  
Another approach is known as the \"holistic approach\", since the face is represented as a whole unit. In this approach, a 3D mesh is situated over the detected face [75, 76, 77]. This mesh, also known as \"active appearance model\", uses the difference between the current estimate of appearance and the target image to drive an optimization process. This approximation presents more robustness regarding the head movement in real time and to partial occlusions and illumination changes, in comparison with the local approximation.  
Finally, the face can be modeled using a hybrid approach that combines the holistic approach and the interest points (the local approximation). In this approach, the interest points are used to determine the initial position of the mesh [78].  
The facial feature extraction must be made without loosing information. Nevertheless, some factors, such as hair, glasses, _etc._, complicate this task, since they can hide some parts of the face. Another problem is the size and orientation of the face in the input images. Furthermore, the noise in the images is another inconvenience.
## 4 Experiment and Result Analysis
### Ablation Experiment Results and Analysis  
#### 4.4.1 Ablation Experiment of Multi-Granularity Feature Interaction Fusion Mechanism  
To verify the effect of the multi-granularity feature interaction fusion mechanism on the model performance in DCCMM, four sets of ablation experiments were conducted in this paper. Except for the change in the multi-granularity interaction fusion module, the other model parameters remained unchanged. The results are shown in Table 6. The specific settings of each experiment are as follows.  
The DCCMM model outperforms the other models in ablation experiments 1-3 on both datasets. On the dataset weibo_senti_100k, the accuracy and F1 score of the DCCMM model are improved by 1.23% and 1.27%, respectively, compared to the model in ablation experiment 1, by 1.37% and 1.44%, respectively, compared to the model in ablation experiment 2, and by 1.16% and 1.20%, respectively, compared to the model in ablation experiment 3.  
On the dataset SMP2020-EWECT, the accuracy and F1 score of the DCCMM model are improved by 4.39% and 5.41%, respectively, compared to the model in ablation experiment 1, by 2.01% and 1.88%, respectively, compared to the model in ablation experiment 2, and by 1.44% and 1.28%, respectively, compared to the model in ablation experiment 3. The result shows that only using character-level or word-level semantic information to complete the sentiment analysis task is ineffective, and using both character-level and word-level semantic information in combination to complement each other can improve model performance.  
The DCCMM model outperforms the model in ablation experiment 4 on both datasets. On the weibo_senti_100k dataset, the accuracy and F1 score of the DCCMM model are improved by 0.78% and 0.82%, respectively, compared to the model in ablation experiment 4. On the SMP2020-EWECT dataset, the accuracy and F1 score of the DCCMM model are improved by 1.05% and 1.04%, respectively, compared to the model in ablation experiment 4. This verifies the effectiveness of the proposed multi-granularity interactive fusion mechanism. Direct splicing of the character-level and word-level semantic information cannot realize the fusion between them effectively. The multi-granularity interactive fusion mechanism can enhance the interaction between character-level and word-level information, realize the complementarity between character-level and word-level information, strengthen the weight of important information, and can filter out noise at the same time, resulting in the output of more effective joint character-word feature information and improving model performance.
# Online EEG Seizure Detection and Localization
## 2 Materials and Methods
### Power in Band of Interest  
One of the features used for distinguishing brain activity is the change in power in the EEG signal. This feature has been widely studied for differentiating between seizure and non-seizure EEG signals [23; 32; 33]. A significant rise in the magnitude of certain frequency components in the EEG signal can be a sign of a transition towards abnormal brain activity, such as the transition from a preictal to an ictal state. In the proposed algorithm, we measure this change in the following manner. For each lead, or channel, \(c\), and epoch, \(t\), the channel power, or the power at each electrode, (P\({}_{\text{E}}\)), in the frequency band of interest is computed as the sum of the squared magnitude of the appropriate FFT coefficient.  
\[P_{E}(t,c)=\sum_{i=1}^{n}|X_{t,c}(i)|^{2}, \tag{1}\]  
where \(X_{t,c}(i)\) are the FFT coefficients of the EEG signal from the channel \(c\) obtained during epoch \(t\) in the frequency band of interest, and \(n\) is the number of FFT coefficients in that band. The total power (P) during epoch \(t\) is then computed as the sum of the power in each band.  
\[P(t)=\sum_{c=1}^{K}P_{E}\ (t,c), \tag{2}\]  
where \(K\) is the number of leads. As we are interested in the change in power, the power obtained using Equation (2) is normalized to get the value of the power in the band of interest (PBI) for epoch \(t\) as  
\[PBI(t)=\ \frac{P(t)-\min(P(t-2j)\ldots P\ (t-j))}{\max(P(t-2j)\ldots P(t-j))- \ \min(P(t-2j)\ldots P(t-j))}. \tag{3}\]  
The computed power in the current epoch is normalized by the background activity. The background activity is measured using the maximum and minimum power in the epochs from about 3  min to the current epoch to about 90 s prior to the current epoch. Each epoch overlaps the previous epoch by 5 s. Therefore, the variable \(j\) in Equation (3) is set to 17. Since neuronal activity differs between individuals, activity that may signal abnormal behavior in one individual may not do so in another individual. Therefore, we compute an adaptive threshold, as described in the following section, to determine the significance of the PBI value. If the PBI value for an epoch is greater than the adaptive threshold, we declared the current epoch to be a candidate for being in an ictal period.
## 1 Introduction
It is reported that the printed outcome exhibits poor surface quality when using Carbopol as a support bath [15]. The pluronic F127 is stable at a temperature range of 25 \({}^{\circ}\)C-40 \({}^{\circ}\)C, which is typically used as mold and sacrificial material for bioprinting [5; 25]. Pluronic F127 has poor mechanical strength and tends to dissolve in aqueous environments, which may affect printed structure shape properties while using liquid hydrogel precursors [26]. The nanoclay is selected here as the support structure due to its superior properties such as the ionic insensitivity, thermal stability, and ultraviolet transparency. As a yield stress bath material, the nanoclay formulation begins in the gel state before processing. When the bioprinting nozzle translates in the nanoclay support bath, nanosilicates around the nozzle experience shear stress higher than the yield stress of the nanoclay, causing a material phase transition from the gel to sol state. After the nozzle travels away, the region rapidly recovers to its native gel state when the shear stress falls below the yield stress. Although bioprinting within a support bath has been widely investigated by using a different fluid-dispensing system and hydrogel materials with different rheological properties, there are fundamental questions about how the relationships can be mapped between the printing process parameters and the printing performance outcomes. To systematically evaluate the related outcomes for bioprinting within a support bath, this paper advances an experimental parametric study to optimize the 3D structural shape fidelity by tuning the process parameters including the layer height, extrusion flowrate, printing temperature, and print head speed. The Table 1 lists the key parameters and their values that will be investigated in this study. The tubular structure, which is widely studied as a mimic of human vessel tissue, is selected as an example to be investigated [13; 23]. Next, optimal printing conditions are then explored to achieve 3D tissue constructs with a smooth surface, shape integrity, and adequate stiffness. Based on the experimentally optimized printing condition, this paper aims to provide needed insight into how cell-laden hydrogel materials can be systematically processed under the emerging bioprinting support bath paradigm to produce tissue constructs with precise control of structural properties.
# The Evolution and Development Trends of LNG Loading and Unloading Arms
## 3 Development of Land-Used LNG Loading and Unloading Arms
### Overall Structure of the Land-Used Loading Arm
#### 3.1.1 Rotary Joints and Sealing of the Land-Used Loading Arm
On this basis, PetroChina Jiangsu LNG Co., Ltd. [55] proposed to abandon the original double-sealing structure of the spring-energized main static sealing ring and the PTFE secondary static sealing ring, optimize the inner-ring structure of the rotary joint, and choose a graphite wound gasket with good sealing effect and compensation characteristics under low-temperature conditions as the sealing element; the case is depicted in Figure 11. This gasket is made of flexible graphite and stainless steel wound together and is widely used in flange joints of pressure vessels, pipelines, and other fields. It is an excellent sealing element [56]. When conducting the overall design of the loading arm and the design and selection of components, the convenience of replacing the sealing rings of the rotary joint should be given key consideration. The dynamic sealing rings of cryogenic rotary joints are mostly made of polytetrafluoroethylene with special springs inside [57]. However, due to factors such as aging and wear, they need to be replaced regularly. In traditional cryogenic rotary joints, the balls need to be removed to separate the rotating shaft from the housing, and then the sealing ring can be taken out for replacement, which is time-consuming and labor-intensive. To solve this problem, Lianyungang Jerry Automation Co., Ltd. [58] designed a new type of detachable cryogenic rotary joint (the case is depicted in Figure 12), which allows the replacement of the sealing ring without removing the balls. This design adopts a structure in which the raceway and the housing form a hole-shaft fit, enabling quick disassembly of the equipment for sealing ring replacement. After verification, it is found that the new structure reduces the replacement time to one eighth of the original. In terms of sealing, two-stage sealing is chosen, with end-face rotary sealing as the main seal and shaft-used rotary sealing as the secondary seal, ensuring reliable sealing effects.
## 3 Numerical Results
### Multiprecision Analysis  
This section presents the multiprecision analysis of the LU algorithm shown in Table 1. The multiprecision analysis aimed to evaluate the minimum precision of \(A\) and \(b\) entries that was required to propagate the code's input data without information loss; in general, the larger the uncertainty of the inputs, the smaller the required precision. In order to estimate the exact number of bits and/or digits of the variables of merit, we replaced all the floating-point variables in our code with the stochastic data type provided by CADNA software. The required precision was evaluated for all the variables of the LU algorithm at each time-step iteration.  
Figure 2 shows the results of the CADNA analysis applied to matrix \(A\) and vector \(b\). At the beginning, matrix \(I-hA\) defined the system of neutron kinetics and lumped temperature equations coupled by means of the reactor core power; therefore, most entries of the input matrix were equal to zero. The LU algorithm gradually changed the entries of matrix \(A\); however, 21 entries were still kept equal to zero for all iterations due to the input sparse structure. In order to simplify the multiprecision analysis, these entries were excluded from consideration.  
The division operation on the lines 9 and 17 of the LU algorithm in Table 1 required special treatment. If the time step \(h\) is small, then denominator \(1-hx\) has to preserve a much higher precision than term \(hx\). In order to reduce the complexity of this operation, one should hardcode the function \(f(x)=1/(1-x)\). Note, this function can be resolved by the Newton-Raphson iterative method with the on-the-fly precision correction algorithm proposed by [39]. In our implementation, we computed the division function using double-precision arithmetic; however, the argument of this function was given in the reduced precision.  
Figure 2 shows the precision of \(A\) and \(b\) varied from two to six decimal digits, which is equivalent to 7 and 20 bits, respectively. The digits referred to the decimal representation of a floating-point number as the binary representation was not available in the CADNA version used. The number of digits varied as a function of time and reactivity as can be seen in Figure 3. One can notice the correlation between the required precision and the time-step history presented in Figure A1 of Appendix B; the smaller the time step used,  
Figure 1: Reactivity, power and temperature transients due to the ejection of a control rod at HZP; the dotted lines show the \(3\sigma\) percentile that accounts for 99.7%.  
the higher the precision required. The step size was a function of the local truncation error evaluated by the BDF as the discrepancy between corrector and predictor vectors. The correlation between the required precision and the local truncation error might be useful for a further optimization of the algorithm.
## 3 Jacobian Matrix Derivation for MES
### Jacobian Matrix for DGN  
#### 3.3.1 Traditional Matrix-Based DGN Model  
Let the state variable vector of the DHN be \(\mathbf{x}_{\text{g}}=[\Pi;f_{\text{E}};\mathbf{\rho};\mathbf{q}]\), and the mismatch vector for the traditional DGN model \(\mathbf{F}_{\text{g}}\) is given by  
\[\mathbf{F}_{\text{g}}=\left[ \begin{array}{l}\Delta f_{\text{L}}=-A_{\text{g}}f_ {\text{P}}+B_{\text{g}}f_{\text{E}}+f_{\text{L}}+T_{\text{C}}\tau_{\text{C}} \\ \Delta d=W_{\text{N}}\Pi+W_{\text{F}}f_{\text{E}}-d\\ \Delta\rho=\text{diag}(\rho)\left(f_{\text{D}}+T_{\text{C}}\tau_{\text{C}}+ \overline{B_{\text{g}}^{\text{C}}}f_{\text{C}}+\overline{-A_{\text{g}}}d_{ \text{P}}|\right)+B_{\text{g}}^{\text{S}}\text{diag}(\rho_{\text{S}})f_{ \text{S}}\cdots\\ \cdots-\sum\limits_{\ell\in\mathcal{G}^{\prime\prime\prime}}\left(-B_{\text{g} }^{\ell^{\prime}}f_{\text{P}}^{\ell^{\prime}}\right)-\overline{-B_{\text{g} }^{\text{C}}}\text{diag}(f_{\text{C}})\overline{B_{\text{g}}^{\text{C}}}^{ \text{T}}\rho-\overline{A_{\text{g}}}\text{diag}(|f_{\text{P}}|)\overline{-A_ {\text{g}}}^{\text{T}}\mathbf{\rho}\\ \Delta q=\text{diag}(q)\left(f_{\text{D}}+T_{\text{C}}\tau_{\text{C}}+ \overline{B_{\text{g}}^{\text{C}}}f_{\text{C}}+\overline{-A_{\text{g}}}d_{ \text{P}}|\right)+B_{\text{g}}^{\text{S}}\text{diag}(q_{\text{S}})f_{ \text{S}}\cdots\\ \cdots-\sum\limits_{\ell\in\mathcal{G}^{\prime\prime\prime}}\left(-B_{\text{g} }^{\ell^{\prime}}f_{\text{I}}^{\ell^{\prime}}\right)-\overline{-B_{\text{g} }^{\text{C}}}\text{diag}(f_{\text{C}})\overline{B_{\text{g}}^{\text{C}}}^{ \text{T}}\eta-\overline{A_{\text{g}}}\text{diag}(|f_{\text{P}}|)\overline{-A _{\text{g}}}^{\text{T}}\mathbf{q}\end{array} \right] \tag{50}\]  
Then, the Jacobian matrix of the traditional DGN model \(J_{\text{gg}}\) can be expressed as  
\[J_{\text{gg}}= \begin{bmatrix}\frac{\partial M_{\text{L}}}{\partial\Pi}&\frac{ \partial M_{\text{L}}}{\partial f_{\text{E}}}&\frac{\partial M_{\text{L}}}{ \partial\rho}&\frac{\partial M_{\text{L}}}{\partial q}\\ \frac{\partial M}{\partial\Pi}&\frac{\partial M}{\partial\rho}&\frac{\partial M}{ \partial\rho}&\frac{\partial M}{\partial\rho}\\ \frac{\partial M}{\partial\rho}&\frac{\partial M}{\partial\rho}&\frac{\partial M}{ \partial\rho}&\frac{\partial M}{\partial\rho}\\ \frac{\partial M}{\partial\Pi}&\frac{\partial M}{\partial\rho}&\frac{\partial M}{ \partial\rho}&\frac{\partial M}{\partial\rho}\\ \end{bmatrix} = \begin{bmatrix}J_{11}&J_{12}&J_{13}&J_{14}\\ J_{21}&J_{22}&\mathbf{0}&\mathbf{0}\\ J_{31}&J_{32}&J_{33}&J_{34}\\ J_{41}&J_{42}&J_{43}&J_{44}\end{bmatrix}  \tag{51}\]  
Similar to \(J_{\text{hh}}\), a few elements in \(\mathbf{J}_{\text{gg}}\) are also neglected for simplicity [27], causing the convergence of NR-based EFC methods to deteriorate.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 2 Materials and Methods
### Theory: Landslide Detection with SAR
#### 2.1.1 Synthetic Aperture Radar: Interferometry and Coherence
Surfaces such as bare rock or buildings tend to retain the same scattering properties over time and so have a high temporal coherence, while vegetated regions, which are likely to move or grow between image acquisitions, tend to have a lower temporal coherence. Figure 3: (**a**) Sentinel-1 interferogram produced using LiCSAR [33] for a pre-event SAR image pair acquired on 24 March and 17 April 2015. Each band of colour shows a phase change between image acquisitions equal to 2.8 cm. Background intensity shows the amplitude of the image acquired on 17 April. (**b**) An example of an area where many pixels are incoherent. In this case, neighbouring pixels do not have a similar phase change and so do not sum constructively, resulting in a low boxcar estimation of coherence. (**c**) An example of an area of high coherence. Neighbouring pixels have similar phase change so sum constructively, resulting in a high estimation of coherence. Changes to the ground surface between the acquisition of two SAR images, for example due to fire, flooding, building collapse or construction, earthquake surface rupture or landslides, alter the scattering properties of each pixel, leading to low temporal coherence and so a low observed coherence. Previous studies have demonstrated that SAR coherence can be used to map changes of this nature [20; 21; 35; 36; 37; 38]; and that these products can be rapidly generated and released for emergency response purposes [20; 21]. We therefore used SAR coherence as a starting point in this study.
## 2 Materials and Methods  
### Data Collection  
#### 2.1.1 Data from FLUXNET  
The FLUXNET2015 dataset, which can be downloaded from [[https://fluxnet.fluxdata.org/data/](https://fluxnet.fluxdata.org/data/)]([https://fluxnet.fluxdata.org/data/](https://fluxnet.fluxdata.org/data/)), contains global eddy covariance measurements and meteorological variables from more than 200 sites. In this study, we selected daily GPP, incident shortwave radiation (SW), latent heat flux (LE), sensible heat flux (H) and hourly air temperature (TA). FLUXNET GPP is calculated as the difference between ecosystem respiration (RECO) and net ecosystem CO2 exchange (NEE). SW was used to calculate PAR by multiplying the value by 0.48 [54]. LE and H were prepared for the EF according to EF = LE/(LE+H). Mean temperature (Tmean) was obtained by averaging hourly TA. To ensure that the FLUXNET data were reliable, we retained only high-quality data with the help of quality flags. Finally, we obtained 29,056 pieces of data distributed at 180 sites ranging from 2003 to 2014. All data were distributed in 12 types of vegetation, of which CRO (croplands) had 20 sites, CSH (closed shrublands) had 3 sites, DBF (deciduous broadleaf forests) had 24 sites, DNF (deciduous needleleaf forests) had 1 site, EBF (evergreen broadleaf forests) had 10 sites, ENF (evergreen needleleaf forests) had 35 sites, GRA (grasslands) had 33 sites, MF (mixed forests) had 9 sites, OSH (open shrublands) had 14 sites, SAV (savannas) had 7 sites, WET (permanent wetlands) had 18 sites, and WSA (woody savannas) had 6 sites. In this study, we considered WET as GRA because of the complicated conditions of WET.