esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background on Children's Speech Recognition (CSR)  
### Children's Speech Databases  
The development of children SLT is crucially dependent on the availability of children's speech databases. Table 1 is a list of commonly used children's speech databases. A foremost difference amongst the databases is the speaker age covered. Article 1 of the UN Convention on the Rights of the Child \"Definition of a child\" [26] defines the \"child as a human being who is below the age of 18 years\". However, children's speech recognition performance is heavily influenced by the speaker's age due to the large differences in pronunciation and language use between different developmental stages in language and speech acquisition, with speech of younger children typically recognized as much worse by an ASR than that of older children and with speech from teenagers being recognized only as slightly worse than that of adults [6; 27]. Age-related development stages can be broadly categorized as: newborn (ages 0-4 weeks); infant (ages 4 weeks-1 year); toddler (ages 12-36 months); preschooler (ages 3-5 years); school-aged child (ages 6-12 years); and adolescent (ages 13-19) [28]. As newborns, infants, and toddlers are beyond the scope of this work, they are not further addressed nor discussed. Most children's speech databases contain English speech [3; 29; 30; 31; 32], with only a few datasets available in other languages [33; 34; 35; 36; 37; 38]. For the English language, the earlier databases (first few rows in Table 1) consist primarily of read speech consisting of isolated words, commands, and phrases [3; 29; 39]. Current data collection efforts are more focused on spontaneous and conversational speech, which is however not easily obtained with children [40]. Moreover, where earlier databases consist of speech of only a small number of child speakers, current efforts focus on obtaining speech from many different speakers, which makes these databases more useful for the development of children's speech technology. Recognizing native children's speech thus already presents inherent challenges. These challenges increase when recognizing non-native children's speech. This is due to the non-native accents in the speech, which is caused by the influence of the native language on the pronunciation of the words in the non-native language, mispronounced words, ungrammatical utterances, disfluencies (false starts, partial words, and filled pauses), and code-switched words. Several databases containing non-native accented (children's) speech have been created that can be used to develop speech technology for non-native (child) speaker groups [34; 41]. The availability of these databases in the public domain along with the organization of several challenges [42; 43; 44] has sped up the much-needed development and improvement of CSR systems. In this work, we use the SLT CSR Mandarin speech data [37], kidsTALC German speech data [38], and a part of the Jasmin Dutch [36] corpus. All these corpora are available on request from the respective sources for research purposes, which allows for easy benchmarking and comparisons of the approaches and systems.
## 4 Discussion  
This study evaluates the efficacy of differential anchorage, sequenced biomechanics with clear aligners to achieve expansion movement in patients with unilateral, bilateral, or single-tooth crossbites. In general, the highest predictability was observed for expansion at the premolar level and the lowest for expansion at the canine level. In this study, all patients were treated by the same experienced clinician. This fact reduces the variability associated with the virtual treatment design (attachments' design and placement, tooth movement staging, and aligners biomechanics knowledge), which plays a crucial role in defining the quality of the orthodontic treatment with aligners. It also improves the treatment achievement, since inconsistency due to the orthodontist's performance is controlled, too. We have used both percentages and raw data to describe our results. Reporting percentage changes gives the results of a trial in clinical terms that are easily accessible to clinicians and patients. However, according to [PERSON] [25], the percentage change from baseline has been demonstrated to be statistically inefficient. Therefore, it is important to provide the real amount of analyzed movements, and use raw data, too. The demographic characteristics of the patients and the outcomes related to their initial malocclusion and treatment features were similar for the three groups, which helps to control possible confounding variables that could influence the treatment results. The mean expansion ranged from 1.01 to 4.86 mm, similar to the range of 2 to 4 mm reported by [PERSON] et al. [1], who used a different software package (Keynote) for the measurements. The largest expansion was at the premolar level (4.86 mm) and the smallest at the canine level in the present series, as also found in Invisalign(r)-treated patients by [PERSON] et al. [12], who used Geomagic(tm) software and recorded a maximum expansion of 4.86 mm at the premolar level. In the same line, [PERSON] et al. [6] described significantly greater dentoalveolar expansion at the second premolar level in the upper arch of patients treated with the Invisalign(r) technique, while changes at the molar level were not statistically significant. Regarding the bucco-lingual linear translation of the teeth, [PERSON] et al. [8] revealed the worsening of the obtained outcome, moving from the center to the distal
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 4 Evaluation and Performance Analysis
### Comparison with Centralized Baseline  
To rigorously assess the advantages of a decentralized ledger-based architecture, TrustShare was evaluated in comparison with a conventional centralized system simulating a trusted intermediary. Identical transaction workloads were applied to both systems under controlled conditions. Metrics were averaged over 10 runs, capturing transaction latency, throughput, availability, and trust requirements. The corresponding results are illustrated in Figure 7 (latency under load), Figure 8 (throughput across nodes), Figure 9 (availability during node failures) and Figure 10 (comparative performance metrics), and summarized in Table 8.  
Experimental Findings
* **Latency:** The centralized system has lower latency under light load, but performance degrades sharply under stress. TrustShare's latency increases more gradually, maintaining predictable performance, as presented in Figure 7.
* **Throughput and Scalability:** TrustShare scales with node count until consensus overhead gains plateau. The centralized model shows declining throughput as node count increases due to coordinator saturation. The results are presented in Figure 8.
* **Trust Model:** TrustShare distributes trust through consensus. The centralized system requires full trust in a single coordinator, introducing a critical vulnerability.
* **Availability and Fault Tolerance:** TrustShare maintains availability under node failures via Byzantine fault-tolerant mechanisms. In contrast, the centralized system fails after coordinator loss, as depicted in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Latency (ms)** & **Throughput (TPS)** & **CPU Usage (\%)** \\ \hline Single Organization, & & & \\
2 Peers & & & \\ Two Organizations, & & & \\
4 Peers & & & \\ Kubernetes Cluster & & & \\ With CouchDB & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance metrics under various configurations.  
Figure 6: TrustShare performance metrics across deployment configurations.  
Figure 8: Throughput vs. node count.  
Figure 7: Latency under increasing load.  
Figure 9: System availability vs. node failures.
## 5 Conclusions and Future Work  
In this paper, we have presented a study on computational challenges and implementation issues arising in real-world systems of semi-autonomous vehicles. We have described the main features of a real-world information system employing semi-autonomous connected vehicles that was designed and deployed and is currently under test in the city of L'Aquila (Italy) within the broader EMERGE initiative, a smart mobility and communications project to develop technologies for autonomous and assisted driving systems. We have given a thorough overview of computational problems that often emerge in the considered application domain and a systematic survey of the most relevant known algorithmic results for pickup and delivery problems. We have showcased implementation, deployment, and testing efforts related the algorithmic components of the mentioned real-world system that takes care of computing solutions to instances of the pickup and delivery multi-vehicle problem with time windows that arise in such system. Concerning possible future investigation following this work, perhaps the most interesting one is that dedicated to adapting the system to handle entirely dynamic scenarios and to exploit cooperation among vehicles to perform more refined strategies of reoptimization (see, e.g., policies for task re-allocation that have been studied for systems of agents [2]).  
Conceptualization, G.F. and E.V.; Methodology, E.D. and G.D.S.; Software, G.F. and E.V.; Validation, G.F. and E.V.; Formal analysis, E.D.; Investigation, M.D., E.D. and G.D.S.; Data curation, G.F. and E.V.; Writing--original draft, E.D.; Supervision, M.D. and G.D.S.; Project administration, G.D.S. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by: (i) project EMERGE: innovation agreement between the Ministry of Economical Development, Abruzzo Region, Radiolab, Elital, Leonardo, Telespazio, the University of L'Aquila, and the Centre of EXcellence EX-Emerge, funded by the Italian Government under CIPE n. 70/2017 (7 August 2017); (ii) Italian National Group for Scientific Computation-Istituto Nazionale di Alta Matematica (GNCS-INdAM).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Figure 15: The execution time in seconds (on the \(y\) axis) to compute the shortest paths between all pairs of customers with [PERSON]’s algorithm as a function of the number #Customers of customers (on the \(x\) axis).
## 2 Principle
### Graphical User Interface Design  
In order to facilitate system design and numerical analysis, we developed a simulation platform of PTA-UOWC channel estimation with configurable parameters using MATLAB. The graphical user interface (GUI) of this app is presented in Figure 4, which is mainly composed of channel modeling, numerical results illustration, and performance indicators output. The upper left part of this GUI is divided into 3 major panels where users can freely set proper transceiver parameters and environment cases. It is to be noted that we use some slider components to guarantee the reasonable range of some input parameters such as gain, quantum efficiency, and dark current. Besides, in the zone of underwater channel configuration, several weather condition options are provided to differentiate the sea surface solar irradiance level based on the data reproduced from experimental results. After parameter initialization, users should select the visualization option and click the \"Simulate\" button to obtain the channel estimation results plotted in the right part of the GUI. Additionally, the channel estimation results and system performance indicators are displayed in the status panel including RMS delay spread, PTA and BL path loss and BER. The simulation figures and statistical results can be saved by clicking the \"Save\" button.  
The Beer-Lambert law overestimates the link attenuation due to the implicit assumption that all the scattered photons are considered to be lost, while a quite large number of scattered photons are still very likely to get back to the Rx. However, it is only inaccurate in waters with high scattering coefficients such as coastal and turbid harbor waters. In pure sea and clear ocean water, the scattering effect is not obvious and the light travels almost in a straight line, thus the attenuation results derived from the BL law and the PTA simulation are very close. Besides, considering its simplicity and efficiency, it can be used to do rapid qualitative analysis to see the impact of changing one system parameter on the system performance. Although the value is not accurate, we can observe the trend [31]. Thus, our simulation platform also provides such a function for fast evaluation. Overall, this simulation platform offers a concise and efficient approach for users to obtain an intuitive understanding of the UOWC link performance. It is much more convenient to construct input specifications, define different scenarios, and process the output data. Besides, users are not required to have expertise in UOWC models or programming skills and need not worry about an illogical input since we have controlled several variables within reasonable bounds. This user-friendly GUI incorporates the underwater channel characterization with the solar irradiance model and different modulation schemes, which enable the designer to make preliminary judgments and choose appropriate hardware components
## 2 Methodology
### Model error due to sensor-induced noise levels  
The PDs used in discrete-LED/PD systems and the color sensor used in the camera-sensor-based PPG signal acquisition system have their own sensitivity, noise level, sampling rate, and quantization limitations. In this study, we perform a noise analysis on the models (7) and (8), which is based on the experimental noise levels obtained from two different sensors. The noise is added to the received light intensity of the sensor and the \(\epsilon_{HbAlc}\), \(\epsilon_{HO}\), and \(c_{HHb}\) parameters are reversely calculated using the least square curve fitting method.  
To estimate the estimation error due to sensor-induced noise, the HbA1c and SPO\({}_{2}\) levels are taken in a range as previously described. Utilizing (9) to (12), different parameters are evaluated and placed in (7) to calculate the absorbance values \(A(\lambda_{1})\), \(A(\lambda_{2})\), and \(A(\lambda_{3})\) in different wavelengths of light. Two ratio values are also calculated from these absorbance values to cancel out the light traversing path length term, \(d\). From the original hypothesis of [21], we can say that the parameter \(d\) will change slightly when the blood enters a tissue region. So,  
\[\delta d=d_{1}-d_{2}\text{ and }\delta A(\lambda)=A_{1}(\lambda)-A_{2}(\lambda)\]  
Here, \(A_{1}\) and \(A_{2}\) indicate the two absorbance values at \(d_{1}\) and \(d_{2}\), respectively. These two values, \(d_{1}\) and \(d_{2}\), represent the diameter of the blood vessel when blood enters and leaves the vessel, respectively. So, we can define the ratio terms from these equations as (20) and (21).  
\[R_{1}=\frac{\delta A(\lambda_{1})}{\delta A(\lambda_{3})}=\frac{\left[log\frac {I(d_{2})}{I(d_{1})}\right]_{\lambda 1}}{\left[log\frac{I(d_{2})}{I(d_{1})}\right]_{ \lambda 3}} \tag{20}\]  
\[R_{2}=\frac{\delta A(\lambda_{2})}{\delta A(\lambda_{3})}=\frac{\left[log\frac {I(d_{2})}{I(d_{1})}\right]_{\lambda 2}}{\left[log\frac{I(d_{2})}{I(d_{1})}\right]_{ \lambda 3}} \tag{21}\]  
At this stage, the received light values are calculated from (6), and a noise parameter is added with the received light for each set of HbA1c and SPO\({}_{2}\) values,  
\[I(\lambda)=I_{0}10^{-A} \tag{22}\]  
\[I^{\prime}(\lambda)=I(\lambda)+N \tag{23}\]  
In (23), \(I^{\prime}(\lambda)\) is the noisy received light when a Gaussian noise \(N\) is added with the ideal received light, \(I(\lambda)\). The values of the terms \(d_{1}\) and \(d_{2}\) cancel out in the ratio equations.  
After adding noise to the received light, the ratio values are calculated using (20) and (21), resulting in \(R^{\prime}_{1}\) and \(R^{\prime}_{2}\). These two ratio values are used to inversely calculate the \(c_{\text{HbAlc}}\), \(c_{\text{HbO}}\), and \(c_{\text{HHb}}\) parameters from (7) and \(c_{\text{HbAlc}}\) and \(c_{\text{NonHbAlc}}\) parameters from (8), respectively, using the least square curve fitting algorithm. We then calculate the HbA1c estimation error for different models in different sensor-induced noise levels.
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
## 4 Performance Evaluation
### WRSP Parameters  
As shown in Equation (12), the parameters \(\alpha\) and \(\beta\) have a direct influence on routing decisions. We defined 10 combinations with different values, as shown in Table 1.  
From Figure 3, we can see that the packet loss ratio performance and throughput of combination #10 was the worst. This is because it gives all the weight to the packet reception ratio, i.e., the relay node with the largest value of the reception ratio is always selected without considering any other information. This is not sufficient to evaluate whether the packet can continue to be forwarded, since it does not take into account the prediction of future relay nodes. Thus, it leads to high packet loss ratios and low throughput. We can observe that performance improves significantly when relay advancement prediction is considered (\(\beta\
eq 0\)), since the link may be available for a very short time owing to the dynamic movement of nodes. Furthermore, the best packet loss ratio performance and throughput is reached when the values of \(\alpha\) and \(\beta\) are those in combination #4. These values were used in the experiment by default, unless specifically mentioned.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Combination \#** & \(\alpha\) & \(\beta\) \\ \hline
1 & 0.1 & 0.9 \\
2 & 0.2 & 0.8 \\
3 & 0.3 & 0.7 \\
4 & 0.4 & 0.6 \\
5 & 0.5 & 0.5 \\
6 & 0.6 & 0.4 \\
7 & 0.7 & 0.3 \\
8 & 0.8 & 0.2 \\
9 & 0.9 & 0.1 \\
10 & 1.0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: WRSP parameters.
## 4 Example
### Telemetry Data Anomaly Detection Experiment
#### 4.3.2 Discussion of Experimental Results
This indicates that while our approach performs well in most situations, it may need improvements when dealing with subtler anomalies. Additionally, our method did not account for the time dependency inherent in telemetry data sequences. In time-series data, the value of a data point may be influenced by previous data points. If the occurrence of an anomaly is related to previous data points, our method may fail to detect it. Figure 19: The abrupt changes of telemetry parameters (green part). To improve our approach, we are considering combining it with other algorithms to better address these issues. In particular, we are exploring the use of algorithms specifically designed for time-series data, such as anomaly detection algorithms based on autoregressive models. We expect that these improvements will enhance our method's performance in handling a variety of situations, especially in dealing with subtle anomalies and accounting for time dependency.
###### Abstract  
Using a concrete surface, the ingress of aggressive ions and the initiation of the corrosion reaction of an embedded steel rebar were studied. To reduce the corrosion reaction of the embedded steel rebar, either a coating on the steel rebar or a repair material was used on the concrete surface. Therefore, in the present study, 200 \(\upmu\)m thick Cu, Ti, and 85 Zn-15 Al were used as repair materials, and their coatings were deposited on the concrete surface using a twin-wire arc thermal spray process. Different experiments such as bond adhesion, water permeability, immersion in a 5 wt.% NaCl solution, and accelerated carbonation were performed to assess the durability of the coatings, and the characterization of the coatings was performed by using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The Cu and 85 Zn-15 Al coatings exhibited severe defects and porosity; therefore, these coatings exhibited very low bond adhesion, whereas the Ti coating showed a dense and compact morphology, and its bond adhesion value was 11 times greater than that of the Cu coating. The NaCl immersion results can be used to determine the extent of the deterioration of different coatings in coastal areas; based on these results, the Cu coating exhibited delamination, while 85 Zn-15 Al showed white rust deposition. By contrast, there was no detrimental effect of NaCl immersion on the Ti coating during the 28 days under study, and the coating exhibited characteristics identical to those observed after deposition. The Ti coating reduced the carbonation depth by 1.5-2 times that of the Cu and 85 Zn-15 Al coatings after four and eight weeks of exposure. The present study suggests that Ti can be the potential metal used as a repair material for concrete to enhance the durability of buildings and infrastructure.  
coating; concrete; arc thermal spray; repair materials; scanning electron microscopy; X-ray diffraction 2022
## 1 Introduction
proposed a method for image quality evaluation, which not only takes into account the effects of aperture diffraction, geometric aberration, and mounting error on the image quality, but also adds the effects of telescope surface shape error and X-ray scattering [29]. [PERSON] et al. optimised the geometric design of the nested conical Wolter-I X-ray telescope and analysed the effects of the initial parameters on its performance [30]. [PERSON] et al. proposed a neural network model which takes into account selected DFA (Design for Assembly) rating factors in predicting the optimum assembly time for mechanical parts in the assembly process [31]. These previous studies have partly analysed mirrors with ideal surface shapes and partly analysed certain stages of the mirror assembly process, but no studies have been carried out to analyse the change in imaging quality during the full-cycle assembly process of a grazing incidence focusing mirror with surface shape error. In this paper, a method for predicting the imaging quality of grazing incidence focusing mirrors based on the accurate modelling of their surface shape accuracy over the whole assembly process is proposed, which integrally considers the effects of gravity and the adhesive curing shrinkage force on the surface accuracy of the mirror during the whole assembly process to predict the effects of changes in the surface shape accuracy of the mirror on its optical imaging quality at different stages by optical simulation, and finally an experimental comparison is carried out to verify the result.