esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 3 Hierarchical Phoneme Classification
### Overall Architecture  
We propose a hierarchical phoneme classification model to improve the performance of phoneme classification. Figure 4 shows the overall model architecture. First, phoneme is predicted through the baseline phoneme classification model in which a whole phoneme is learned by using the same feature extraction method without distinguished characteristics. The baseline model was constructed using long short-term memory (LSTM) [17]. LSTM is  
Figure 3: (**a**) The confusion matrix rearranged according to the phoneme clustering result. Each row of the matrix represents the instances in a truth while each column represents the instances in a predicted class. (**b**) Confusion matrix of phoneme groups obtained by phoneme clustering.  
an RNN cell that can avoid the problem of vanishing gradient using three gates of input, forget, and output. A typical LSTM uses a unidirectional structure that learns only by checking information from previous contexts. In the speech recognition, a bi-directional structure that learns both past and future information shows good performance [42; 43; 44]. Therefore, we implemented the model with the bi-directional LSTM (BLSTM) as a baseline system for phoneme recognition [45]. Three BLSTM layers were stacked and a fully-connected layer is added. At the output, the phoneme is classified using the softmax layer.  
The learning target of the predicted phoneme for the baseline model is the index of the phoneme group in Table 3. Then, the speech feature is re-extracted using the optimal window size for each phoneme group. In addition, autocorrelation is used together to distinguish voiced and unvoiced phonemes. The extracted speech features are used as inputs to each phoneme group model. The phoneme group model finally predicts the phoneme.
## 2 Materials and Methods
### Antioxidant Activity
#### 2.8.2 DPPH Free Radical Scavenging Effect  
The antioxidant activity against DPPH of _Pinus_ spp. bark extracts was approached as explained before [21; 30] and adapted to the 96-well plate scale. Ascorbic acid was used as the antioxidant reference, being dissolved in methanol/water (50:50, \(v/v\)) at 1 mg mL\({}^{-1}\). Stock solutions (1 mg mL\({}^{-1}\)) of three polar extracts from the _P. pinaster_ Ait. and _P. pinea_ L. barks were also prepared in methanol/water (50:50, \(v/v\)). Briefly, in each well, 30 \(\mu\)L of 1 mM DPPH methanolic solution was mixed to 75 \(\mu\)L of sample and 195 \(\mu\)L of methanol, obtaining the concentration ranges of 1.0-43.2 \(\mu\)g mL\({}^{-1}\) for the extracts and 0.5-15.9 \(\mu\)g mL\({}^{-1}\) for ascorbic acid. The blank was composed of 30 \(\mu\)L of DPPH (1 mM) in methanol and 270 \(\mu\)L of methanol. After mixing, the microplate was protected from the light for 30 min. The absorbance of the resulting solution was measured at 520 nm in a microplate reader (Thermo Scientific Multiskan(tm) FC). The DPPH scavenging effect percentage was determined according to Equation (3).  
\[DPPH scavenging effect\ (\%)=[\frac{A_{control}-A_{sample}}{A_{control}}]\times 100\% \tag{3}\]  
The IC\({}_{50}\) was determined by plotting the percentage of scavenged DPPH free radicals against the concentration logarithm. For comparison reasons, the AAI was calculated according to Equation (4) [31].  
\[AAI=\frac{DPPH_{final\ concentration}(Hg\ mL^{-1})}{IC_{50}(Hg\ mL^{-1})} \tag{4}\]  
with the DPPH final concentration equal to 61.874 \(\mu\)g mL\({}^{-1}\). Each concentration was assayed in triplicate.
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 4 Numerical Simulation Method Based on Dual Softening Effects  
In ABAQUS finite element analysis, the mechanical effect of groundwater on surrounding rock is typically calculated by the fluid-structure coupling method, and the Mohr-Coulomb constitutive is commonly used for the surrounding rock. However, the damage deterioration process of rock mechanical parameters during excavation cannot be realized using such a method. To address the problem, this section goes beyond the traditional fluid-solid coupling method and proposes a computational method that truly reflects the softening and deterioration process of the surrounding rock after excavation in water-rich soft geology. The specific calculation flow is shown in Figure 11 where our improvements are shown in dashed boxes. The mechanical parameters (strength, modulus of elasticity, cohesion, internal friction angle, etc.) of each element in every calculation step should be deteriorated referring to the calculated results of the previous step in the newly improved simulation method.  
The judgment process of damage deterioration is shown in Figure 12. The Mohr-Coulomb criterion is still used, and the damage variables are calculated for the corresponding mechanical parameters after the shear yielding of the surrounding rock so that the parameters' deterioration is executed under compression shear damage. Referring to the analysis in Section 3.1, distinct tensile damage often occurs during the excavation unloading, and the tensile strain is not generated by the tensile stress. The Mohr-Coulomb criterion cannot describe the tensile yield of an element, nor can it represent the tensile yield of an element by setting the tensile strength (usually the tensile stress in simulation is minimal), so it is necessary to judge the tensile damage of the non-compression-shear damage elements by tensile strain, and then perform parameter deterioration for the elements under tensile damage.  
Figure 10: Time history curves of damage variables under different conditions.  
Figure 11: Simulation process based on the dual softening damage effects.  
Figure 12: Evaluation process of damage deterioration of surrounding rock elements.  
corresponding field variables. (Theoretically, the plastic development of each node is different, so the damage degree and post-softening parameters are different). Then copy the softened results to resubmit the next step calculation.
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 3 Results
### Integral Partition Design
\begin{array}{rl}\dot{V}_{2}&=-\mathbf{\omega}^{T}(k_{d}\mathbf{I}_{3}-l_{1}\mathbf{I} \mathbf{F})\mathbf{\omega}-l_{1}k_{p}\mathbf{q}_{v}^{T}\mathbf{q}_{v}-l_{4}k_{1}\mathbf{v}^{T}\mathbf{v }-l_{4}\mathbf{q}_{v}^{T}\mathbf{l}\mathbf{\omega}-(k_{I}+l_{4}k_{d})\mathbf{\omega}^{T}\mathbf{v} \\ &+(\mathbf{\omega}+l_{1}\mathbf{q}_{v}+l_{3}\mathbf{v})^{T}\mathbf{d}\\ &\leq-(k_{d}-l_{1}\lambda_{M}(\mathbf{J}))\|\mathbf{\omega}\|^{2}-l_{1}k_{p}\|\mathbf{q}_{v }\|^{2}-l_{4}k_{I}\|\mathbf{\upsilon}\|^{2}+l_{4}\lambda_{M}(\mathbf{J})\|\mathbf{q}_{v}\| \|\mathbf{\omega}\|\\ &+(k_{I}+l_{4}k_{d})\|\mathbf{\omega}\|\|\mathbf{\upsilon}\|+(\|\mathbf{\omega}\|+l_{1}\|\mathbf{q }_{v}\|+l_{3}\|\mathbf{\upsilon}\|)\overline{d}\\ &\leq-(k_{d}-l_{1}\lambda_{M}(\mathbf{J}))\|\mathbf{\omega}\|^{2}-l_{1}k_{p}\|\mathbf{q}_{v }\|^{2}-l_{4}k_{I}\|\mathbf{\upsilon}\|^{2}+\overline{1}l_{4}\lambda_{M}(\mathbf{J}) \Big{(}\|\mathbf{\omega}\|^{2}+\|\mathbf{q}_{v}\|^{2}\Big{)}\\ &+\frac{1}{2}(k_{I}+l_{4}k_{d})\Big{(}\|\mathbf{\omega}\|^{2}+\|\mathbf{\upsilon}\|^{2} \Big{)}+(\|\mathbf{\omega}\|+l_{1}\|\mathbf{q}_{v}\|+l_{3}\|\mathbf{\upsilon}\|)\overline{d} \\ &=-\Big{(}(k_{d}-l_{1}\lambda_{M}(\mathbf{J}))-\frac{1}{2}l_{4}\lambda_{M}(\mathbf{J})- \frac{1}{2}(k_{I}+l_{4}k_{d})\Big{)}\|\mathbf{\omega}\|^{2}+\overline{d}\|\mathbf{ \omega}\|\\ &-\Big{(}l_{1}k_{p}-\frac{1}{2}l_{4}\lambda_{M}(\mathbf{J})\Big{)}\|\mathbf{q}_{v}\|^{2}+ \overline{d}\|\mathbf{\eta}_{v}\|-\Big{(}l_{4}k_{I}-\frac{1}{2}(k_{I}+l_{4}k_{d}) \Big{)}\|\mathbf{\upsilon}\|^{2}+\overline{d}\|\mathbf{\upsilon}\|\end{array}  \tag{30}\]  
Hence, it can be found that, if the following inequality is satisfied, then \(\dot{V}_{2}\leq 0\) can be ensured. \[\begin{array}{rl}\|\mathbf{\omega}\|\geq\frac{\overline{d}}{(k_{d}-l_{1}\lambda_{M }(\mathbf{J})-\frac{1}{2}l_{4}\lambda_{M}(\mathbf{J})-\frac{1}{2}(k_{I}+l_{4}k_{d})} \\ \|\mathbf{q}_{v}\|\geq\frac{\overline{d}}{l_{1}k_{p}-\frac{1}{2}l_{4}\lambda_{M}( \mathbf{J})}\|\mathbf{\upsilon}\|\geq\frac{\overline{d}}{In other words, if inequality (31) is satisfied, the system can converge to its equilibrium point, and it can be concluded that, under disturbance torque, the system steady accuracy satisfies:  
\[\|\mathbf{\omega}\|<\frac{\overline{d}}{(k_{d}-l_{1}\lambda_{M}(J))-\frac{1}{2}l_{4} \lambda_{M}(J)-\frac{1}{2}(k_{I}+l_{4}k_{d})},\|\mathbf{q}_{v}\|<\frac{\overline{d }}{l_{1}k_{p}-\frac{1}{2}l_{4}\lambda_{M}(J)} \tag{32}\]  
**Remark 5**. : _The steady accuracy field (32) is not strict since some in-equal transformations are made during the derivation. The system state may converge to a smaller field than (32) and stay within it, and inequality (32) is the steady accuracy considering the worst condition. It can be found that there are many control parameters in the stability analysis, but there are only three parameters in the controller, i.e., \(k_{d},k_{p},k_{I}\), and other parameters are selected to analyze system stability, but they do not affect system performance._
## 1 Introduction
With reference to variables governing household energy consumption, climate is one of the most important, although prediction still remains a challenge due to the variety of additional factors that affect, such as the diverse nature of the residential sector, physical properties of the building, installed equipment, energy prices, demographic and socioeconomic factors [17; 18], and the type of energy used [7; 19; 20; 21; 22]. Furthermore, spatio-temporal variations in electricity consumption is higher compared to other sectors (such as commercial or industry), which adds uncertainty in the prediction [23; 24]. The proposal of this paper is to combine meteorological and socioeconomical data with the power of available energy smart meters from The Low Carbon London (LCL) trial project [25] to address the challenge of predicting energy consumption in households, using the tools provided by artificial intelligence in a distributed environment. Two studies have already analysed these data with two different approaches. [PERSON] et al. [17] used a metric widely used in the UK' planning guidelines called after-diversity maximum demand (ADMD), which determines coincident peak energy demand for a large number of customers [26; 27; 28]. Their findings were based on ACORN classification using only 2013 data, and considering the number of household occupants as additional variable to determine general household demands for distribution planning practices. In this study, the importance of socioeconomic factors in energy demand is confirmed, as the need of further studies taking into consideration calendar seasons, days and hours, as the work in this paper. On the other hand, [PERSON] et al. [18] studied the accuracy and computational times of RF for hourly energy prediction using Apache Spark in AWS S3 as scalable distributed storage infrastructure and MongoDB as database. They confirmed, as it has been done in this paper, the need of a distributed architecture to improve computational times and predictive accuracy for large-scale datasets. With the evidence of the precious studies in analyzing demands at different levels of resolutions and the proposal of Big Data technologies for large-scale datasets, the novelty of this paper lies in the use of artificial intelligence approaches to analysis data with high temporal resolution (hourly), including socioeconomic variables that allow to break up the data-set in groups, increasing resolution of the results. Finally, the use of non-linear models allows more flexibility in contrast to the stiffness of mostly used linear models [16; 29]. To ensure the scalability and availability of the generated solution for a real application with larger data flows, a Spark distributed Cluster allocated in Cloudera platform is presented as the big data architecture for the querying and data processing, using the native Machine Learning module of Spark (MLlib).
# An Interoperable UMLS Terminology Service Using FHIR
## 4 Method  
This section describes the methodology followed to design and develop the UMLS FHIR terminology service, which represents UMLS knowledge, specifically, the mapping between the atoms and the UMLS concept and relationships, both hierarchical and associations between the UMLS concepts using FHIR. The workflow is represented in Figure 5.  
* **Step 1:** Problem identification and understanding, and defining research objectives and goals, discussed in Section 1.
* **Step 2:** An in-depth analysis to understand the fundamental similarities and differences between the UMLS knowledge structure and ConceptMap, discussed in Section 3.3.
* **Step 3:** Use the analysis to design FHIR extensions to profile ConceptMap (Figure 4), aligning it with UMLS knowledge (Figure 3) requirements to express UMLS knowledge as a ConceptMap.
* **Step 4:** Implement the designed FHIR extensions and profiled ConceptMap using the HAPI library. Develop the UMLS FHIR terminology service using HAPI library, profiled ConceptMap and other FHIR terminological resources.
* **Step 5:** Demonstrate the terminology server, evaluate the results, and reflect on the research.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 7 Open Challenges
### Ambiguous Allocation between Sound Events and Scenes  
Acoustic scenes often comprise multiple sound events, which are not class-specific, but instead appear in a similar way in various scene classes [2; 21]. As an example, sound recordings that are recorded in different vehicle types such as car, tram, or train often exhibit prominent speech from human conversations or automatic voice announcements. At the same time, class-specific sound components like engine noises, road surface sounds, or door opening and closing sounds appear at a lower level in the background. [PERSON] and [PERSON] used the gradient-weighted class activation mappings (GradCAM) to show that CNN based ASC models in general have the capability to ignore high-energy sound events and focus on quieter background sounds instead [35].
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Results and Discussion
#### 4.2.8 Running Time Comparison  
Running time is a crucial factor in evaluating the performance of different algorithms, as it directly impacts path generation and collision avoidance response. It represents the time required by an algorithm to generate a collision-free path, with lower execution times indicating better algorithm performance. The average execution times over 30 runs are recorded in Table 4 and depicted in Figure 13. The results indicate that PSO requires the shortest time to generate a collision-free path for the IoD, followed by ABC, CIPSO, and IPSO, while GA exhibits longer execution times.  
Figure 12: (**a**) SLR and (**b**) RPD of the IoD formation for all algorithms.
## 3 General Classification of Modern Haptic Interfaces
### Electromagnetic Haptic Interfaces  
Another alternative for providing pressure and vibration as haptic feedback is electromagnetic-based actuators. A permanent magnet reacts rapidly and provides significant deformation within an electromagnetic field. A coil, a supporting body, a cavity shape, a permanent magnet, and a handling layer make up a standard electromagnetic actuator. The created magnetic field, also known as the Lorentz force, will attract or repel the magnet hung by the handling layer when current flows through the coil. The number of coil turns, coil size, coil-magnet distance, magnet size, and magnetism affect the actuator's force. Individual tiny electromagnetic actuators must be created and designed to create skin-integrated electromagnetic haptic interfaces. After being packaged, the actuators and electronic sections may be combined within flexible circuits using microfabrication techniques. This approach results in a controlled haptic interface. Advanced structural and mechanical design may be used to improve the performance of LR electromagnetic actuators. Additionally, the benefits of electromagnetic actuators are higher force-displacement and stronger tactile feedback than piezoelectric ones, as well as a larger vibration frequency with quick response, compared to pneumatic or hydraulic haptic ones. Finally, the electromagnetic tactile interface has further benefits over pneumatic and piezoelectric ones, including non-contact sensing and control features that make it a viable option for touchless VR interfaces. Their working bandwidth is too narrow for ultrasonic applications compared to piezoelectric actuators; they also feature higher dimensions.
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
###### Abstract
CMAC uses only the statistics from the scene to be corrected, enabling image correction immediately upon download while eliminating correction errors arising through temporal data mismatch. CMAC development sought a workflow that uses scene statistics for AC rather than applying ancillary data required for RadTran inputs. Examples of ancillary data include aerosol optical thickness (AOT) and water vapor from external satellites such as MODIS [15]. Current RadTran-based methods for smallsats include emulating the radiance of other research-grade satellites such as Landsat 8 and 9 (L8/9) and S2 through the use of datasets that are \"harmonized\" to support the vicarious calibration of smallsat radiometry [16; 17; 18]. Cross-calibration with harmonized datasets provides the basis for estimating smallsat sensor radiance, thereby enabling RadTran application. However, that workflow is inconvenient at least and potentially adds uncertainty. CMAC operates in near real-time because it uses scene statistics. The accuracy and stability of CMAC version 1.1 output are examined here in relation to that of Sen2 Cor, the European Space Agency's (ESA) accepted method for S2 AC that is available as user-applied software [19]. This CMAC version was developed for AC of four-band visual VNIR data represented by S2\({}^{\prime}\)s blue (B02), green (B03), red (B04) and near NIR (B08a) bands. Section 2 describes CMAC development to address the AC problem and describes the experimental design to test CMAC. Statistical testing is purposely kept simple to allow expression of the results in clearly understandable terms. Section 3 provides CMAC surface reflectance estimates in comparison to Sen2 Cor. This comparison provides the context to judge CMAC accuracy, precision and limitations using both graphical and statistical data representation. The results of both methods are also compared to the uncorrected TOAR data to understand the statistical improvement provided by AC. The data are made available through extensive tables and figures in \"live\" spreadsheets that are downloadable through Supplementary Materials. Section 4 provides further analysis and context for a better understanding of the AC correction problem and the potential value of CMAC to serve the smallsat industry in its role of providing intelligence, surveillance, and reconnaissance. Section 5 lists five promotional aspects for AC of smallsats resulting from this paper.
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 6 Tests and Results  
In this section, the results for comparing the SPF-only and Agents strategies are provided. Since the different strategies and algorithms aim to reach maximum lifespan while maintaining an optimal connected coverage, the two main metrics therefore consist of the connected coverage and the network lifetime.  
As explained in Section 2, the connected coverage metric represents the fraction of the area that can be monitored by the robots that maintain network connectivity with the access point and the quality of the sensing they can perform in that area. In order to estimate this, we use the probabilistic sensing model proposed in [3], according to which the quality of sensing (sensitivity) that each robot can achieve gradually attenuates with increasing distance. Following this approach, in order to compute this metric, we divide the area into a probabilistic grid of \(M\) cells and we compute the quality of the sensing at each cell i as the probability of detecting an event that occurs at that cell (\(p_{i}\)). Since a given cell can be monitored by more than one robot, this in turn depends on the number of robots that can monitor that cell and the distance to each of them. Thus, if cell i is covered by N robots with connectivity to the AP, we compute \(p_{i}\) as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Mean CPU Usage (\%)**} & \multicolumn{2}{c}{**Peak CPU Usage**} & \multirow{2}{*}{**Memory Consumption (GB)**} \\  & & & at Startup (\%) 1 ** & & \\ \hline Idle & 0.5 & NaN & 1.7 \\
100 robots & 24.5 & 39.1 & 4.5 \\
200 robots & 38.1 & 91.2 & 9.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CPU and memory usage for 100 and 200 robots. Test machine specifications: Intel Core i7 12700K, 32 GB RAM, Ubuntu 22.04. Ten Robot controllers with 10/20 robots, respectively.  
\[p_{i}=1-\overline{p_{i}}=1-\prod_{j=1}^{N}(1-p_{ij}) \tag{17}\]  
where \(p_{ij}\) is the probability of robot \(j\) detecting an event that occurs at cell \(i\), which depends on Euclidean distance from robot j to cell i, \(d_{ij}\). To estimate the quality of the sensing of robot j at cell i, we define several sensing ranges \(R_{0}<R_{1}<R_{2}\) and compute \(p_{ij}\) as  
\[p_{ij}=\begin{cases}1&if\;d_{ij}\leq R_{0}\\ 0.6&if\;R_{0}<d_{ij}\leq R_{1}\\ 0.2&if\;R_{1}<d_{ij}\leq R_{2}\\ 0&otherwise\end{cases} \tag{18}\]  
Therefore, the quality of the sensing at each cell represented by \(p_{i}\) lies in the interval [0, 1], and \(p_{i}\) is 0 if cell i is not being covered by any connected robot. The total coverage of the area achieved by the network of robots is subsequently computed as the average of the quality of the sensing at each of the M cells into which we divide the area:  
\[C=\frac{1}{M}\sum_{i=1}^{M}p_{i} \tag{19}\]  
In the case of network lifetime, this metric is defined as the duration it takes the simulation to reach a certain threshold. This threshold, for example, represents the condition for ending the simulation, which is a number of 70 dead nodes.