esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background:The Roussouly classification, based on the functional parameter sacral slope (SS), describes the normal sagittal balance in adults and has proved useful for surgery. Reference values in children should be defined, since they are an important treatment target of conservative treatment. Moreover, during growth, there are few correlations between sagittal parameters, and we hypothesize that a new classification based on the anatomical parameter pelvic incidence (PI) could also be useful. We performed a cross-sectional study to identify the reference values for the Roussouly classification during growth and to develop a new classification based on PI in children. **Methods:** Correlations between sagittal parameters and age were searched in 222 healthy subjects at the first consultation (6-18 years old). A new classification, based on PI, and comprising three types, is defined and compared to the Roussouly classification. **Results:** With age, correlations among sagittal balance parameters increase, as well as SS and PI, but with different cut-offs. The distribution of Roussouly types do not correspond to that in adulthood; thus, we defined new reference cut-offs. We defined a PI-based classification in three types, not overlapping [PERSON]'s. We found a uniform and balanced distribution of cases among the nine possible combinations. **Conclusions:** In children, we need to use new thresholds for the Roussouly types. The new classification based on PI is correlated with the Roussouly classification, but it is also clearly different. Future studies will determine its validity.  
scoliosis; spinopelvic parameters; growing age; conservative treatment; orthopedics; rehabilitation
# Titanium Nitride Plating Reduces Nickel Ion Release from Orthodontic Wire
## 3 Results
### TiN Coating of Orthodontic Wires Suppresses the Elution of Metal Ions from the Wire Surface
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Wire**} & \multirow{2}{*}{**Solution**} & \multirow{2}{*}{**Innimension Time (min)**} & \multicolumn{2}{c}{**Ni**} & \multicolumn{2}{c}{**Ti**} \\ \cline{5-6}  & & & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multirow{5}{*}{TN-coated SS wire} & Sterile water & 0 & 0.43 & 0.28 & 1.82 & 0.99 \\  & & 30 & 1.35 & 0.95 & 2.57 & 2.11 \\ \cline{2-6}  & Physiological saline & 0 & 1.06 & 0.87 & 1.76 & 0.77 \\  & & 30 & 4.16 & 2.05 & 7.29 & 10.30 \\ \cline{2-6}  & Hydrochloric acid & 0 & 8.03 & 11.02 & 13.01 & 10.59 \\  & & 30 & 13.536.28 & 10.012.85 & 12,005.95 & 9671.59 \\ \cline{2-6}  & & 0 & 1.34 & 0.07 & 24.19 & 0.27 \\ \cline{2-6}  & & 30 & 1.44 & 0.03 & 24.56 & 0.33 \\ \hline \multirow{5}{*}{Uncoated SS wire} & Sterile water & 0 & 0.43 & 0.28 & 1.82 & 0.99 \\  & & 30 & 2.38 & 1.47 & 2.18 & 2.43 \\ \cline{1-1} \cline{2-6}  & Physiological saline & 0 & 1.06 & 0.87 & 1.76 & 0.77 \\ \cline{1-1} \cline{2-6}  & & 30 & 3.37 & 0.91 & 1.17 & 0.29 \\ \cline{1-1} \cline{2-6}  & Hydrochloric acid & 0 & 8.03 & 11.02 & 13.01 & 10.59 \\ \cline{1-1} \cline{2-6}  & Hydrochloric acid & 30 & 215,499.84 & 64,463.60 & 173,761.82 & 73,627.09 \\ \cline{1-1} \cline{2-6}  & & 0 & 1.34 & 0.07 & 24.19 & 0.27 \\ \cline{1-1} \cline{2-6}  & & 30 & 2.29 & 0.19 & 25.37 & 0.36 \\ \hline \end{tabular}
\end{table} 
Table 2: Metal ion release from NiNt wire (fig/L). Figure 1: Metal ions released from orthodontic arch wire immersed in different solutions. After immersion in HCl or lactic acid solution for 30 min, the amount of metal ions eluted from the wire was determined by ICP-MS. The concentrations of the eluted metal ions from TiN-coated SS wire and uncoated SS wire were: (**a**) SS immersed in HCl; (**b**) NiTi immersed in HCl; (**c**) SS immersed in lactic acid; (**d**) NiTi immersed in lactic acid. The statistical significance of differences was determined by the Shapiro-Wilk (* \(p<0.05\), **_p_ \(<0.01\)).
# Design of an Open Remote Electrocardiogram (ECG) Service
## 2 Related Works and Results Summary
The challenge is to finally make the service affordable also to marginal milieux, like developing countries and rural areas [16; 17] which are more in need of a qualified remote medical assistance than Western towns. We should apply a cost-aware approach to the solution of the technical challenges we meet in the design of a remote ECG service [18]. To implement such an approach, we address a design philosophy summarized in three key-words:  
* **Open Source**: components and protocols are exhaustively documented and freely reproducible;
* **Low Cost**: the less expensive option is always preferable. As a corollary, if one functionality is already available for free, it is not re-implemented;
* **Commercial Devices**: devices must be available on retail (aka Commercial Off-the-shelf (COTS)). We use the acronym OpLoC to indicate such a design approach. It needs to be applied to all aspects of the design--with a holistic attitude--to avoid its violation on one design aspect as a consequence of another design options, which, taken alone, appears as valid [19]. Put together, the three OpLoC principles ensure that implementations are easily reproducible, which is fundamental for further investigation. When scaling up to production, the OpLoC approach grants the coexistence of different solutions made of interoperable parts, since interfaces are open. Moreover, low cost widens the market, since the design is not selective by scale or wealth. Thus, given the advantages of an OpLoC solution, we want to investigate whether it is technically possible to obtain a useful solution for a remote ECG service which complies with the OpLoC principles. The design we describe in this paper uses _open source_ devices: an open source board for the sensor, an Arduino for real-time data acquisition, a board based on the ESP8266 chip (NodeMCU) for Internet delivery, a cloud container as a relay point, an HTTP-compliant browser to visualize the trace. The programs that have been written ad hoc are in public domain repositories, and the rest of the code is open source. The _price_ of a single device is of a few tens of dollars and can be significantly reduced using hardware that is not compatible with Arduino pinning; the one we use in the prototype is available on major online shops. Figure 1 summarizes our results, showing the trace displayed on the doctor's browser with a latency of less than one second. The patient wears wrist electrodes appropriate for self-measurement. Power consumption allows a day-long operation of the patient's sensor with a 3.3 V LiPo rechargeable battery. In a nutshell, we actualize the ideas of [PERSON] et al. [13], but in a BAN perspective [8], with a financial impact that allows extending nationwide a screening like REHEARSE-AF [1]. One step towards a future ubiquitous ECG monitoring service.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 1 Introduction
### Related Works
#### 1.1.1 Distribution Shift and Non-Stationary Time Series  
Domain adaptation (DA) [37; 38; 39; 40; 41] and domain generalization (DG) [42; 43; 44; 45] address distribution shifts in machine learning when predefining the domain is feasible. DA pertains to scenarios where training and test sets' distributions differ, while DG involves training with multiple domain sources. However, in non-stationary time series data, domain shift occurs gradually over time, making predefining domain specification impractical. Recently, the adaptive RNN architecture was proposed to handle distribution shifts in time series [19]. It splits the training data into periods to adapt the model for distribution shifts. In contrast reversible instance normalization [18], compatible with various forecasting models, employs normalization along with additional learnable parameters to obtain forecasts and restore the prior distribution.
# Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction
## 2 Factors That Affect HER Performance
### Catalytic Process
#### 2.3.2 Surface Structure Effect  
Noble metal catalysts with the same composition and different exposed crystal surfaces have different catalytic performances. The research results show that the catalytic performance is closely related to the atomic arrangement and coordination number on the exposed surface. For precious metal crystals with the face-centered cubic (_fcc_) structure, the crystal surface can be divided into low-index crystal surfaces, including three fundamentals (111), (100) and (110), and high-index crystal surfaces (hkl)(h \(\geq\) k \(\geq\) 1 \(>\) 0). In general, the nanocrystals with low-index surfaces have high coordination numbers and relatively low catalytic performance. Under normal conditions, the surface energy (\(\gamma\)) of the exposed surface follows \(\gamma(111)\) \(<\)\(\gamma(100)\) \(<\)\(\gamma(110)\) \(<\)\(\gamma(\)hkl\()\), such as Pt. The difference in surface reactivity caused by the atomic arrangement belongs to the electronic structure  
Figure 2: Periodic table of transition metals containing valence electron configurations, d and idealized d–band filling [31]. Copyright 2019, Royal Society of Chemistry.  
effect. Compared with low-index surfaces, the precious metal polyhedrons with high-index surfaces contain multiple crystal orders, twin grain boundaries, crystal edges, vertexes and corners [32]. These structures have more abundant defects and low coordination active centers, which are easier to bind with the catalyzed substances and show excellent catalytic performance [33; 34]. Therefore, developing noble metal nanostructures with high-index crystal surfaces is one of the effective ways to improve catalytic performance.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 3 Preliminaries of D2D Communications
### LTE-V2V Mode 4  
Mode 4 is studied as the baseline for V2V communication because of its ability to operate without the involvement of cellular infrastructure. Safety applications based on V2V communication cannot depend on the coverage of cellular network, making Mode 4 even more suitable for such communication. Vehicles in Mode 4 use PC5/the side link for V2V communication. Radio resources are autonomously selected, having no dependency on cellular network coverage. For in-coverage vehicles, the configuration decision of the V2X channel is performed and sent to vehicles by the network through the PC5 V2X configurable parameters. The message comprises the V2X resource pool, the number of subchannels in a subframe, the number of RBs in a subchannel, the carrier frequency, etc. [28].  
For vehicles present outside network coverage, a set of preconfigured parameters is used to replace the configurable PC5 V2X parameters. However, an exact value of each parameter is not specified in the standard. The subframes utilized for V2X communication are indicated in the V2X resource pool. Remaining subframes can be exploited for cellular communication. Geographical-area-based division of the V2X resource pool is included in the standard termed zoning. Vehicles in a specific area can use the resources assigned to that area [28]. In this paper, the channel was assumed to be entirely dedicated to V2X communication.  
In LTE Mode 4, resources are arranged as blocks called RBs. The following section discusses the mechanisms used for the selection of these blocks.
## 3 Results
### Data Fitting of the 120 h Biomass  
Data fitting of the 120 h biomass as a function of the exposure time was performed using the \"cftool\" function in the software MATLAB. As the biomass of the control group (25 \({}^{\circ}\)C) did not change significantly with the exposure time, the average value of the six biomass in the control group, 469 g/m\({}^{2}\), was used as the biomass without being thermal shocked for data fitting. The fitting formula is shown in Equation (4):  
\[m_{\text{f}}=m_{\text{f,ave\_25}\ ^{\circ}\mathbb{C}}-a\times(1-\text{e}^{-bt}) \tag{4}\]  
where \(m_{\text{f}}\) is the biomass of the thermal shocked biofilms(g/m\({}^{2}\)), \(m_{\text{f,ave\_25}\ ^{\circ}\mathbb{C}}\) is the average biomass of the control group (469 g/m\({}^{2}\)), \(a\) and \(b\) are coefficients correlated with temperature (g/m\({}^{2}\) and min\({}^{-1}\)) and \(t\) is the exposure time (min). The reason for choosing Equation (4) as the fitting formula will be discussed and the fitting results are shown in Figure 5.  
It can be seen in Figure 5 that the biomass decreased in a negative-exponential form as the exposure time increased and Equation (4) yielded tight correlations with average \(R^{2}\) in different temperature groups larger than 0.92.  
The coefficients \(a\) and \(b\) increased as the temperature rose from 50 to 80 \({}^{\circ}\)C. Coefficients \(a\) and \(b\) were fitted as functions of the temperature rise (compared with 25 \({}^{\circ}\)C) in a linear form and the results are shown in Figure 6.  
Figure 4: Biomass (120 h) of biofilms thermally-shocked with different temperatures and exposure times.  
Given the expression of \(a\) and \(b\) in Figure 6, the fitting equation of the 120 h biomass as a function of the temperature and the exposure time can be written as:  
\[m_{\rm f}=469-[4.9848\times(T-25)+7.08]\times\{1-\mathrm{e}^{-[0.02884\times(T- 25)-0.5666]\times t}\} \tag{5}\]  
where \(T\) is the temperature (\({}^{\circ}\)C) and \(t\) is the exposure time (min).
## 4 Conclusions  
In this study, we implement an AMMGS, which combines SpeakerYAMNet for speaker identification, Google Speech-to-text API for speech recognition, and the Jieba word segmentation tool for keyword prompts. This system can identify the speaker who is speaking while the speech recognition results and keyword prompts are simultaneously generated in the graphic user interface. We employ the YAMNet as the fundamental architecture to learn speaker characteristics through the English Libri speech open-source corpus. Then, we use Mandarin Chinese spoken speech with transfer learning to teach the Speaker YAMNet to identify Chinese speakers. Each Mandarin Chinese speaker has more than 150 pieces of speech data as a private Chinese corpus. Performing transfer learning lets YAMNet learn the characteristics of Chinese speakers and obtain Speaker YAMNet to identify Chinese speakers. Experimental results reveal that the average precision rate, recall rate, and F-measure can reach more than 99% in the self-recording speech as the testing data set. Accordingly, the Speaker YAMNet can effectively identify specified speakers in a meeting. The proposed AMMGS can be applied in practical environments.  
Conceptualization, C.-T.L.; methodology, C.-T.L. and L.-Y.W.; software, L.-Y.W. and C.-T.L.; validation, C.-T.L. and L.-Y.W.; formal analysis, C.-T.L.; investigation, C.-T.L. and L.-Y.W.; resources, L.-Y.W.; data curation, L.-Y.W.; writing--original draft preparation, C.-T.L. and L.-Y.W.; writing--review and editing, C.-T.L.; visualization, C.-T.L. and L.-Y.W.; supervision, C.-T.L.; project administration, C.-T.L.; funding acquisition, C.-T.L. All authors have read and agreed to the published version of the manuscript.  
Figure 6: The GUI of the proposed AMMGS. (**a**) Arrangement of the system; (**b**) speaker identification and speech recognition results chart; (**c**) dialog of pressing the download button; (**d**) results of conference/meeting minutes, where the non-English terms are the generated Chinese characters.  
**Funding:** This work was supported by the National Science and Technology Council, Taiwan, grant number NSTC 111-2410-H-035-059-MY3.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** This study did not require ethical approval.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** Our gratitude goes out to the reviewers for their valuable comments which have improved the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Performance Comparison
#### 6.3.4 Apd  
Figure 10 shows that the APD of the existing scheme WDFAD-DBR is better than the proposed schemes, as it selects forwarders on the basis of depth of nodes. WDFAD-DBR has more APD, as it picks up the node with the minimum depth difference, and high priority is assigned through the calculated difference. If the forwarder node has neighbors, only then does it select it to relay the data. Therefore, a longer path is opted for, which is evident from Figure 10. On the other hand, the higher APD of RE-PBR is because of link quality, which leads to a longer data path, although the reliability of data delivery is high in RE-PBR as compared to the proposed and baseline schemes.  
All the proposed schemes have increased APD as they avoid the shortest route to the sink to avoid collision and void holes. C-DBR performs better than A-DBR due to clustering in the network. Additionally, the transmission distance decreases, which ultimately increases the APD of the proposed scheme C-DBR. A-DBR adjusts the transmission range to reduce the void hole problem. Thus, the packet drop ratio decreases, which ultimately results in increased APD. The proposed schemes B-DBR and CA-DBR perform better than other algorithms as they are also selecting forwarders on the basis of depth along with backward transmission and collision avoidance.
## 3 SAT-Hadoop-Processor
### Parallel RS Data Ingestion  
We used parallel programming libraries to optimize the ingestion process execution time, notably, the Linux Background Processing (LBP), GNU Parallel, Python parallel, and  
Figure 3: The OpenStack architecture for the parallel RS data processing.  
the multi-threads of Java. The LPB is a process that is started from a shell and then executes independently using this symbol (\(\mathbf{\kappa}\)) after the CMD; the same terminal will be instantly available to run further CMDs.  
The GNU Parallel is used to compile and run CMDs parallel to the same CMD with several arguments, whether filenames, usernames, etc. It provides shorthand references to many of the most common operations, mainly the input lines, sources, etc. It can also replace xargs or feed CMDs from its input sources to several Bash instances. CMD 4 shows that the parallel execution exploits 95% of the hardware in the subset script using the GNU parallel library: **CMD 4**: Bash subset_script.sh | parallel--load 95%--noswap '[].'  
The Python parallel is a library that simultaneously executes several processes or scripts in multiple processors in the same computer or cluster. It is intended to decrease and optimize the total processing time. CMD 5 illustrates how to execute many functions simultaneously in Python using the Python parallel library: **CMD 5**: Th = threading.Thread(Functions); Th.start(); Th.join(). Java Multithreading is a Java option that permits parallel execution of two or more program parts to maximize hardware capacities.  
Figure 4 summarizes the input format and the output format of the six steps of pre-processing. The ingestion layer was conceived for RSBD pre-processing; it can hold a colossal input data volume and extract the information needed from satellite data. As illustrated in Figure 1, this ingestion layer was developed by some detached and interconnected scripts: Java, Python, and Bash are the primary programming languages employed in coding.  
Figure 4: The architecture of the RS data-parallel processing.  
The Bash was used to connect automatically, download RSBD from various sources, and manage many files. Python scripts are mostly used to extract, serialize, and deserialize the final output datasets. Lastly, the Java application aggregates, calls, run all the established scripts and connects with MySQL DB to select parameters and insert benchmarking monitoring results.  
The mechanism of how the layer functions is that in parallel (LPB), the RSBD is downloaded from several links using Wget. A Bash script decompresses in parallel (GNU parallel) the collected data using the Tar, Unzip, and Gunzip libraries. Afterward, a Bash script parallel (LBP) filters the data. Another Bash script converts in parallel (GNU parallel) the BUFR, Bin, and GRIB data to the CSV format using the BUFRextract (BUFFXC) and the pybuff_ecmwf (ECMWF). The converted data are subset and extracted in parallel (Python parallel) using the h5 py and Pyhdf libraries. Finally, the final CSV output is loaded and integrated into the Hadoop system.
## 3 Results
### Fault Prognosis  
After fault detection and diagnosis, the third phase required to accomplish the whole condition monitoring of a signal is the fault prognosis, which implies making a prediction about the future signal trends, beyond the end of real data, with a certain level of probabilistic confidence, with the goal of estimating the component's Remaining Useful Life (RUL). This operation represents the key point to identify the optimal time to replace a mechanical part, in the trade-off between the cost minimization due to the intervention on the system and the cost of the new component, fully exploiting the service life of the one to remove on one hand, and the major cost to bear in case of a catastrophic failure occurs on the other, leading to a harmful stop of production, which is absolutely to be prevented.  
Prognosis issues are often carried out making use of advanced data-driven techniques belonging to the domain of AI and specifically of Machine Learning, which are programmatically beyond the scope and the intention of this paper. This section is meant for providing a first glance at that field, identifying the most recommended statistical parameters for prognostic considerations on the IMS dataset.  
As stated before, the bearings' end of life were conventionally determined through a lubricant-based analysis; however, it is still feasible to determine how reliable information extrapolated with statistical operators may be about the behavior of the damaged components if brought to the extreme of degradation.  
For this reason, the previously mentioned parameters of Correlation, Monotonicity and Robustness, typical indicators used in health management, have been computed on the statistical tools, similarly to what was conducted in [20], at first on every second of acquisition, then averaging the results on the entire test duration and finally obtaining a single quantity for each statistical parameter and each bearing as the average of the three indexes, in the same way adopted for [PERSON]'s parameters and Detectivity.  
Histograms depicted in Figure 49 clearly suggest that Mean and Skewness are not proper instruments for prognostic operations, as they were not so useful for fault detection either, while RMS value, Variance, Standard Deviation and Peak-to-Peak prove to be the most reliable quantities to be used as a solid base for future estimations. It is interesting to notice that, as may be expected, the parameters evaluated on the signals coming from  
\begin{table}
\begin{tabular}{c c c} \hline
**Technique** & **Bearing** & **Kurtosis** \\ \hline \multirow{2}{*}{Wavelets + SES} & 3 & 22.9 (Figure 30) \\ \cline{2-3}  & 4 & 17.5 (Figure 31) \\ \hline \multirow{2}{*}{FIR Filter + SES} & 3 & 51.2 (Figure 34) \\ \cline{2-3}  & 4 & 38.6 (Figure 36) \\ \hline \multirow{2}{*}{CPW + SES} & 3 & 86.0 (Figure 38) \\ \cline{2-3}  & 4 & 81.7 (Figure 40) \\ \hline \multirow{2}{*}{IES} & 3 & 65.7 (Figure 45) \\ \cline{2-3}  & 4 & 58.6 (Figure 46) \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._the two damaged bearings perform better as prognostic tools than when applied on the healthy components.