esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System
## 4 Discussion
study (with sensitivity 25% and specificity 91% in real-world conditions) yielded a sensitivity range of 94.1-94.4% and specificity of 92.1-94.6% in a laboratory setting [28]. This underscores the critical need for real-world evaluations like ours, which offer a more accurate and comprehensive understanding of how fall detection systems perform in the environments where they are actually used. The qualitative analysis in Phase 2 enriched our understanding of the Neo Care Monitoring System's performance, revealing a complex interplay between user experience and system functionality. A number of participants suggested that, in addition to its auto-detection features, the Neo could be trained to recognize a trigger word (e.g., \"Help!\") to allow users to manually call for help in the case of the system failing to trigger automatically. This could potentially improve the sensitivity performance (given that the Neo missed 3 out of 12 falls, which anecdotally occurred because the falls were quiet). Another common suggestion for improvement was a system customization feature (for example, reducing the Neo's sensitivity to crying, screaming or whimpering noises in homes with dogs or young visiting children). Such a feature is likely to improve the device's specificity, though as already acknowledged, actions to improve specificity will risk harming the device's sensitivity. Participants' suggestion that personalized calibration aligned with previous research advocating for machine learning algorithms in healthcare settings [32]. On the theme of future use, participants expressed varied preferences. While some were open to continuing with the Neo system if enhancements were made, and appreciated that it offered a non-wearable monitoring option, others leaned towards wearable devices, a trend noted in other studies [33]. The potential for Neo's application in institutional settings, like aged care facilities, suggests a broader scope for its utility, consistent with recent calls for versatile monitoring solutions in such environments [34]. Cost considerations were diverse, but participants generally indicated a willingness to invest in a reliable and effective system. The influence of healthcare providers in shaping these choices was noteworthy and aligns with existing research emphasizing the role of clinicians in technology adoption [35]. The Neo system's non-reliance on wearable technology offers a unique advantage, addressing common issues of forgetfulness or discomfort associated with wearable devices [31].
# Three-Dimensional Elastodynamic Analysis Employing Partially Discontinuous Boundary Elements
## 2 Time-Domain Boundary Integral Equation of Elastodynamics and Its Numerical Implementation
### Numerical Implementation of the BIE
 \tag{12}\]  
in which \(u_{j}^{mll}\) and \(p_{j}^{mll}\), respectively, represent the displacement and traction at the \(k\)-th node in the \(l\)-th element at \(t=m\Delta t\), and \(N_{k}(\mathbf{q})\) represents the shape function at the integration point \(\mathbf{q}\). Equation (12) can use the Gauss-Legendre quadrature method to perform numerical integration on each element. Then, the physical variables at all element nodes can be obtained by solving the linear equations derived from Equation (12). In this paper, the spatial discretization of Equation (12) adopts partially discontinuous elements. As shown in Figure 1, the blue lines represent the boundary of the surface, the black lines represent the edges of the mesh elements, the hollow dots represent the original element nodes, and the red solid dots represent the element collocation points which are moved inward. The nodes located on the surface but not on the edges (suchas black solid dots in Figure 1) remain unchanged. It can be seen that in the partially discontinuous element, only the nodes located at the corner points are shrunk to the surface, and other nodes remain unchanged. Therefore, the partially discontinuous element can avoid the discontinuity problem of physical variables at the corner points without adding too many nodes. It is a compromise between the continuous element and the completely discontinuous element. Numerical Integration Scheme of the Partially Discontinuous Element and the Treatment of the Singular Integral  
In this section, the numerical integration scheme of the partially discontinuous element is presented according to [20; 21; 22]. To reduce the side effect of the time step on the singular integral accuracy, an improved singular element subdivision method is proposed in this section.
# Fatigue Performance of Tunnel Invert in Newly Designed Heavy Hauilway Tunnel
## 3 Mechanical Characteristics  
The maximum principal strain was selected to illustrate the stress state of the IASL (Figure 5) because it was easy to acquire during the fatigue tests, facilitating a results comparison between the calculation and tests.  
When two 30 t axle load carriages pass through a tunnel, the tunnel experiences the coupled action of surrounding rock pressure and dynamic train load. Figure 5(a) shows that the maximum principal strain is located at point C1\({}_{t}\), the top center of the IASL. The maximum principal strain of R2\({}_{t}\) and D1\({}_{t}\) is also relatively large. Figure 5(b) indicates that peak strain occurs when a wheel set passes, and the maximum principal strain appears when the bogie passes (0.80 s, 1.02 s). The \(\varepsilon_{\text{max}}\) of point C1\({}_{t}\) is \(48.6\times 10^{-6}\) and that of point C1\({}_{t}\) is \(8.3\times 10^{-6}\). This illustrates that the influence of the surrounding rock pressure is stronger than the dynamic train load on the stress state of IASL; the vulnerable position is under a high static and low dynamic stress state.  
The time-history curve reveals that when a train passes through a tunnel, two types of dynamic impact occur: The first and last wheel sets of the train are separate wheel set impacts, while all carriages in the middle are impacted by bogies (two sets of adjacent wheel sets). The maximum stress cycle only occurs once, but hundreds of small stress cycles occur simultaneously.  
Figure 4: Layout of feature points and lines (cm).  
Figure 5: Maximum principal strain of inverted arch secondary lining: (**a**) Horizontal distribution and (**b**) time–history curve.
# Microbial Biosynthesis of Lactones: Gaps and Opportunities towards Sustainable Production
###### Abstract
Third, hydroxy fatty acids are not an abundant nor renewable raw material and, for instance, ricinoleic acid from castor oil (used as a \(\gamma\)-decalactone precursor) has several health, seasonal and economic issues related to the crop of the plant from where it is extracted [18; 19; 20]. Therefore, the development of production processes that use sustainable and more abundant substrates is of the upmost importance. However, microbial species that are able to de novo biosynthesize lactones (i.e., produce lactones from sugars or other non-lipid substrates) or even perform biotransformation from non-hydroxy and more abundant fatty acids are scarce. There is also a considerable lack of research either on fruits [21; 22] or microbes [23], resulting in enzymatic and genetic gaps in the biosynthetic process of lactones. Moreover, unlike other high-value chemicals, the engineering of microbial strains for lactone production was not extensively pursued, being mostly limited to the \(\beta\)-oxidation pathway of _Y. lipolytica_[24]. Nonetheless, in recent years, there is a growing number of studies that are addressing this problem and exploring the production of lactones from more renewable substrates. Recent examples are the identification and engineering of the filamentous fungus _Ashbyya gossypii_ as a microorganism able to perform de novo biosynthesis of lactones [23] and biotransformation of non-hydroxy fatty acids [10], as well as the engineering of _Y. lipolytica_ to perform biotransformation of non-hydroxy fatty acids [20]. In this review, we will address the chemical diversity and applications of lactones before providing an overview of the works performed either on microbes or fruits that contributed to the current understanding of the intermediates, enzymes and pathways required for the biosynthesis of lactones. \(\gamma\)- and \(\delta\)-lactones are the most stable and commercially valuable lactones and fungi are the most exploited microorganisms for their biotechnological production. Therefore, the identification of the most relevant studies to the microbial production of \(\gamma\)- and \(\delta\)-lactones in fungi either through biotransformation or by de novo biosynthesis will be presented next. Finally, we will point out some of the gaps in the research of lactones from an industrial biotechnological perspective and propose future directions. Together, these represent the challenges that should be addressed in order to drive the development of more sustainable second-generation lactone bioproduction processes. The literature search for this narrative review was made using the PubMed and Google Scholar search engines in addition to the keywords indicated after the abstract, both in separated and conjugated forms. No time frame filters were used for the searches made, as literature addressing the de novo biosynthesis of lactones is scarce.
# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 4 Materials
### Data Analysis
#### 4.3.2 Speech Rate  
Dysarthric speakers tend to have prolonged pronunciation leading to a slower speaking rate, slower articulation rate, more pauses, and more syllables. Impairments in speech rate are apparent when examining the mean values from Table 3. The difference in speech rate measures between healthy and dysarthric speakers is larger in Korean than in English. For example, the number of pauses for healthy and severe dysarthric speakers is 0.17 and 1.82 for English speakers, respectively, whereas, 0.09 and 4.74 for Korean speakers, respectively.  
According to the results from the two-way mixed ANOVA, there is no significant interaction between severity group and speech rate measure for English. However, a post-hoc pairwise comparisons test reveals a significant difference between healthy and severe speakers for articulation rate and speaking duration (\(p<0.05\)). Unlike English, a significant two-way interaction is present for Korean utterances, where F (8.42, 92.6) = 12.83, \(p<0.05\). Furthermore, the simple main effect of severity on all speech rate measures except for balance is significant (\(p<0.05\)). Pairwise comparisons tests reveal  
Figure 3: The mean values of all F0 measurements from the QoLT dataset.  
Figure 2: The mean values of all F0 measurements from the TORGO dataset.  
significant differences between healthy and severe utterance for all speech rate measures except balance (\(p<0.05\)), between healthy and moderate utterances for all measures except balance and number of syllables (\(p<0.05\)), between healthy and mild utterances for speaking rate (\(p<0.05\)). Significant differences are also apparent with mild and severe utterances for all measures except articulation rate and balance (\(p<0.05\)), and between moderate and severe utterances for the number of syllables (\(p<0.05\)).
## 3 Resilience
### Resilience in Smart ICI
Additional detail on the IST is given in Section 4. Since resilience is a multifaceted concept, it is crucial to evaluate resilience within the context of interest. The context of this paper is smart interdependent critical infrastructure systems, such as power networks, water networks, and health facilities, with a particular interest in disaster response operations. All the above definitions for resilience indicate how the system should be designed to protect an individual infrastructure from any disturbance event that could lead to a failure in the system functionality. The exact definition applies to the resilience of the interdependent infrastructure. That can be accomplished by expanding the protection from a single working infrastructure into \(n\) numbers of infrastructures that run simultaneously with interdependent functionality.
# Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR
## 4 Results
### Data Smoothing  
#### 4.3.1 Smoothing  
It can be seen from the curve in Figure 4, the fluctuation range of the signal after denoising is still obvious, thus it is necessary to smooth and trim the real curve (denoising signal) in order to make a correct judgment on the deformation law of the pile. The translational mean method, Savitzky-Goiay method and percentile-filter method are used to smooth and trim the signal denoised by [PERSON]'s unbiased risk threshold rigrsure processing method. The lines in Figures 5-7 are the smoothed results. Because of the large amount of field monitoring data, it should ensure that the trend of the smoothed curve is consistent with the original signal. The number of items taken by the translation mean method is the average of the values corresponding to each 40 points before and after the data of the intermediate point is obtained; while the Savitzky-Goiay method is a polynomial curve fitting method, which is also a commonly used data fitting method, the formula coefficients are generally quadratic.
# Location-Based Lattice Mobility Model for Wireless Sensor Networks
###### Abstract  
Significant research has been conducted for maintaining a high standard of communication and good coverage in wireless sensor networks (WSNs), but extra power consumption and mobility issues are not yet fully resolved. This paper introduces a memory-less location mobility-aware Lattice Mobility Model (LMM) for WSNs. LMM is capable of concurrently determining the node and sink mobility. LMM has a lower pause time, fewer control packets, and less node dependency (e.g., the energy consumed by each node in each cycle that is independent of the data traffic). LMM accurately determines a node's moving location, the distance from its previous location to its current location, and the distance from its existing location to its destination. Many existing mobility models only provide a model how nodes move (e.g., to mimic pedestrian behavior), but do not actually control the next position based on properties of the underlying network topology. To determine the strength of LMM, OMNet++ was used to generate the realistic scenario to safeguard the affected area. The operation in affected area comprises searching for, detecting, and saving survivors. Currently, this process involves a time-consuming, manual search of the disaster area. This contribution aims to identify an energy efficient mobility model for a walking pattern in this particular scenario. LMM outperforms other mobility models, including the geographic-based circular mobility model (CMM), the random waypoint mobility model (RWMM) and the wind mobility model (WMM), The simulation results also demonstrate that the LMM requires the least time to change the location, has a lower drop rate, and has more residual energy savings than do the WMM, RWMM, and CMM.  
wireless sensor networks; mobility; energy saving; disaster recovery; lattice mobility model; pattern +
Footnote †: journal: Sensors
## 2 A New NHPP Software Reliability Model
### New NHPP Software Reliability Model  
[PERSON] [3] formulated a generalized NHPP software reliability model that incorporated uncertainty in the operating environment as follows:  
\[\frac{d\ m(t)}{dt}=\eta[b(t)][N-m(t)], \tag{5}\]  
where \(\eta\) is a random variable that represents the uncertainty of the system fault detection rate in the operating environment with a probability density function \(g\); \(b(t)\) is the fault detection rate function, which also represents the average failure rate caused by faults; \(N\) is the expected number of faults that exists in the software before testing; and, \(m(t)\) is the expected number of errors detected by time \(t\) (the mean value function).  
Thus, a generalized mean value function, \(m(t)\), where the initial condition \(m(0)=0\), is given by  
\[m(t)=\int_{\eta}N\Big{(}1-e^{-\eta\int_{0}^{t}b(x)dx}\Big{)}dg(\eta). \tag{6}\]  
The mean value function [11] from (4) using the random variable \(\eta\) has a generalized probability density function \(g\) with two parameters \(\alpha\geq 0\) and \(\beta\geq 0\) and is given by  
\[m(t)=N\Bigg{(}1-\frac{\beta}{\beta+\int_{0}^{t}b(s)ds}\Bigg{)}^{\alpha}, \tag{7}\]  
where \(b(t)\) is the fault detection rate per fault per unit of time.  
We propose an NHPP software reliability model including the random operating environment using Equations (3)-(5) and the following assumptions [7; 8]:  
1. The occurrence of a software failure follows a non-homogeneous Poisson process.
2. Faults during execution can cause software failure.
3. The software failure detection rate at any time depends on both the fault detection rate and the number of remaining faults in the software at that time.
4. Debugging is performed to remove faults immediately when a software failure occurs.
5. New faults may be introduced into the software system, regardless of whether other faults are removed or not.
6. The fault detection rate \(b(t)\) can be expressed by (6).
7. The random operating environment is captured if unit failure detection rate \(b(t)\) is multiplied by a factor \(\eta\) that represents the uncertainty of the system fault detection rate in the field  
In this paper, we consider the fault detection rate function \(b(t)\) to be as follows:  
\[b(t)=\ \frac{a^{2}t}{1+at},\ a>0,\ a,b>0, \tag{8}\]We obtain a new NHPP software reliability model with S-shaped growth curve subject to random operating environments, m(t), that can be used to determine the expected number of software failures detected by time t by substituting function b(t) above into (5) so that:  
\[\text{m(t)}=\text{N}\bigg{(}1-\frac{\beta}{\beta+\text{at}-\text{ln}(1+\text{ at})}\bigg{)}^{\alpha}. \tag{9}\]
# A Taxonomic Survey of Physics-Informed Machine Learning
## 2 Taxonomy
### Bias  
In the taxonomy of PIML, biases describe how physics information is enforced in the machine learning application. Stemming from two different drivers, three resultant biases are defined. [PERSON] et al. [1] in their 2021 review of physics-informed machine learning, provided a categorization of bias in the machine learning process. An interpretation of these processes are given in Figure 4--which adopts the same general abstractions as those in Figure 3 and includes the generic representation of a physics model's governing equations by \(\frac{\delta u}{\delta l}\) and \(\frac{\delta v}{\delta l}\). The wide variety of drivers and biases are given in Table 1. As exemplified by the neural network in Figure 4, learning bias (1) is typically enforced in the training error, observational bias (2) is applied in the architecture, and observational bias (3) is applied at the data level.  
_Observational bias._ Physics information can be incorporated into the learning process through data. Models learning from numerically derived data-driven methods intuit physically relevant relationships in the structure of the data that have been produced ipso facto by the researcher's understanding of the underlying physics. The abundance of various sensors makes physically relevant observational data for multiphysics modeling problems equally abundant. Much work has incorporated the need to gain maximum generalizability from sparse simulated data and other sources of multifidelity data.  
_Inductive bias._ Physics-information is directly injected into the learning process through architecture-level decisions of the model. Various partitions of the model can be trained in a multitask fashion to implicitly satisfy the underlying physics. Architecture level changes induce bias on the training process by influencing modeling choices with intrinsic physical principles.  
_Learning bias._ Physics information is given as an informed biasing of the optimization step in the machine learning model. Often, loss functions are directly informed by calculating residuals of underlying physics equations. Rather than implicitly influencing the training process, learning bias forces constraints on the model in a multitask learning process where the model is trained with constraints informed by the underlying physical features.  
Figure 3: Flow chart of the physics-informed machine learning pipeline as a parallel to traditional multiphysics modeling.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 5 Conclusions  
In this paper, a noise reduction and dereverberation algorithm was presented by combining the CGMM-based MVDR beamforming and the multichannel linear prediction with Kalman filtering. The noise and a part of reverberation were processed by the MVDR beamforming, meanwhile the covariance matrix of the target speech estimated by beamforming was utilized to estimate the PSD of the target signal, which is an important parameter for Kalman filtering. In addition, a low-complexity version of the proposed algorithm was given. The effectiveness of the proposed method in the experiment, the influence of different PSD initialization methods on the algorithm, and the importance of accurate PSD estimation to the algorithm were carefully analyzed. The evaluation results proved that the proposed algorithm is superior to the reference methods and the low-complexity version keeps similar effectiveness with much less complexity.  
Conceptualization, C.B., F.T. and J.Z.; methodology, C.B. and F.T.; software, F.T. and J.Z.; validation, C.B., F.T. and J.Z.; formal analysis, C.B. and F.T.; investigation, F.T.; resources, F.T.; data curation, F.T. and J.Z.; writing\(-\)original draft preparation, C.B., F.T. and J.Z.; writing\(-\)review and editing, C.B., F.T. and J.Z.; visualization, F.T.; supervision, C.B. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (Grant No. 61831019).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Proposed-Kron**} \\ \hline Step & \((\times)\) & \((\star)\) & \((\star)\) \\ \hline
1 & \((2\mathrm{L}-1)\,(\mathrm{L}-1)^{2}\) & \(2(\mathrm{L}-1)^{3}-(\mathrm{L}-1)\) & \(--\) \\
2 & \(\mathrm{M}\,(\mathrm{L}-1)^{2}\,(\mathrm{M}+1)+2\,(\mathrm{L}-1)^{2}+2\,( \mathrm{L}-1)\) & \((\mathrm{M}^{2}+2)\,(\mathrm{L}-1)^{2}-\mathrm{M}\,(\mathrm{L}-1)\) & \(\mathrm{L}-1\) \\
3 & \((\mathrm{L}-1)+(\mathrm{L}-1)^{2}+(\mathrm{L}-1)^{3}\) & \((\mathrm{L}-1)^{3}+(\mathrm{L}-1)\) & \(--\) \\ \hline \multicolumn{4}{c}{Proposed} \\ \hline Step & \((\times)\) & \((\star)\) & \((\star)\) \\ \hline
1 & \(2\,\mathrm{M}^{2}\,(\mathrm{L}-1)^{3}+\mathrm{M}^{2}\,(\mathrm{L}-1)^{2}\) & \(2\,\mathrm{M}^{3}(\mathrm{L}-1)^{3}-\mathrm{M}(\mathrm{L}-1)\) & \(--\) \\
2 & \(2\,\mathrm{M}^{2}\,(\mathrm{L}-1)^{2}+2\,\mathrm{M}(\mathrm{L}-1)\) & \(2\,\mathrm{M}^{2}(\mathrm{L}-1)^{2}\) & \(\mathrm{M}\,(\mathrm{L}-1)\) \\
3 & \(\mathrm{M}\,(\mathrm{L}-1)+\mathrm{M}^{2}\,(\mathrm{L}-1)^{2}+\mathrm{M}^{3}\,( \mathrm{L}-1)^{3}\) & \(\mathrm{M}\,(\mathrm{L}-1)^{3}+(\mathrm{L}-1)\) & \(--\) \\ \hline \multicolumn{4}{c}{Complexity Reduction Factor} \\ \hline Step & \(\lambda_{(\times)}\approx\) & \(\lambda_{(\star)}\approx\) & \(\lambda_{(\star)}\approx\) \\ \hline
1 & \(1/\mathrm{M}^{2}\) & \(1/\mathrm{M}^{3}\) & \(--\) \\
2 & \(1/2+1/2\) M & \(1/2+1/\mathrm{M}^{2}\) & \(1/\mathrm{M}\) \\
3 & \(1/\mathrm{M}^{3}\) & \(1/\mathrm{M}\) & \(--\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Complexity comparison.