esa-sceva/satcom-chunk-collection huggingface dataset:
# Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol
## 3 Results  
This study addressed the problem of automatic determination of whether a set of MRI scans resulting from prostate visualization presents a healthy patient or PCa. In order to verify whether such functionality can be obtained, three experiment scenarios were performed. All of them took advantage of the texture analysis of the region of interest, but first, the prostate central zone was analysed, then the peripheral zone was considered and, finally, those two regions were merged together for textural analysis. For each scan, all textural features presented in Section 2.2 were calculated applying all considered normalization and quantization approaches. Large numbers (6678 for the central zone, 4898 for peripheral, and 6718 for both regions) between feature correlations were determined, and those with repetitive information were neglected (it was around 60,000/54,000/64,000 features for the central, peripheral zone, and both regions, respectively). From the remaining features, we evaluated which pairs allowed for binary classification of the dataset. We applied the LOO methodology. Moreover, the data were imbalanced; hence, to deal with this problem, the SVM model was trained with the balanced parameter turned on; moreover, we decided to use the specificity, sensitivity, and F1-score as a metrics, as they better reflected the classifier performance in the case of unbalanced data. For SVM, the linear kernel was chosen with the regularization factor equal to 10.  
Tables 2-4 gather the 10 best results recorded in each case. A model performance is presented in each row with the names of the textural features (columns Feature 1, Feature 2) used to train this model. From the outcomes, we can notice that, regardless of the considered region, it was possible to recognize between the healthy and ill prostate tissue with at least 0.70 F1-score. Using the features derived from the central zone, more precise results were achieved with the best score of F1-score equal to almost 0.77 with very high recall equal to 0.70 and precision equal to 0.85. The better performance of the texture analysis when using the central zone may be due to two factors. First, the central zone shape was circular, which made the feature calculation similar in each direction. Second, this region was larger; thus, it is easier to find some characteristic features there. The deterioration of results when both regions were treated as one using features calculated from the region boundary made the classifier less confident.  
The texture feature name starts with Y, reflecting that the Y channel from the YUV colour space was used for calculation. The following letter corresponded to the data normalization technique followed by the number of bits used for quantized data representation. After this prefix, the texture analysis method acronym was used followed by the information of direction calculation and the distance feature applied (if used) and terminating with the feature name.
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics were used to characterize the study population. All the outcome measures were normally distributed and presented with means and standard deviations. The study population was stratified by type of headache: migraine, TTH or headache attributed to a TMD. For each subgroup, and for each outcome measure, a general linear modeling with repeated measures (GLM-RM) analysis was applied. This analysis shows whether (1) there are differences in the outcome measures between the TMD complaints and the headache complaints (within-subjects effect), (2) there are changes in the complaints over time (within-subjects effect), and (3) there is evidence for a different effect in change over time between the TMD complaints and the headache complaints. When changes over time are found, and no difference in effect over time between the TMD and headache complaints is shown, this is interpreted as 'a comparable course of TMD complaints and headache complaints overtime'. Analysis of the data was performed using the Statistical Package for the Social Sciences (IBM SPSS) version 24.0 (SPSS Corp, Chicago, IL, USA). In case of missing outcomes of the intermediate time points (i.e., the 4-week and the 8-week measurement), multiple imputation was applied using a linear regression model for scale variables. To account for an increased type-I error rate associated with multiple comparisons, we used Simple Interactive Statistical Analysis ([[http://www.quantitativeskills.com/sisa/calculations/bonfer.htm](http://www.quantitativeskills.com/sisa/calculations/bonfer.htm)]([http://www.quantitativeskills.com/sisa/calculations/bonfer.htm](http://www.quantitativeskills.com/sisa/calculations/bonfer.htm)) accessed on 21 August 2021) to calculate an adjusted alpha for each headache subgroup. This adjusted Bonferroni correction is based on the mean correlation between the outcome measures, which was calculated using R statistical software. These correlations and the adjusted alphas were r = 0.018 and \(p\) < 0.018 for TMD-pain patients with migraine, r = 0.024 and \(p\) < 0.024 for patients with TTH and r = 0.017 and \(p\) > 0.017 for patients with headache attributed to TMD [20].  
For the DS, a clinically significant change is suggested to be 2 points [21]. A post hoc power calculation was performed using G*power [22] to check if the number of patients was sufficient to show a clinically significant change based on the estimates presented in this study.
## 2 Fundamental of PIM-Based Ground Subsidence Prediction  
PIM is a coal mining subsidence prediction method which is based on the stochastic medium theory. Because of the advantages, such as simple parameters and clear physical definition, the method has been widely used for predicting ground subsidence, tilt, curvature, horizontal displacement and deformation. Figure 1 shows a typical Cartesian coordinate system which is used for PIM-based coal mining subsidence prediction. The region of OABC shown in the figure denotes a mining area, \(S_{1}\) is the deviation of the inflection point in the strike direction, and \(S_{2}\) and \(S_{3}\) are the deviations of the inflection point in the lower and upper parts of the dip direction, respectively. All of those three deviations of the inflection point are related to mining depth, and can be calculated by [3]:  
\[S_{i}=kH_{i}\ \ \ \ \ (i=1,2,3) \tag{1}\]  
where \(k\) is inflection point offset coefficient, and \(H_{i}\) is the average mining depth in the corresponding direction. Once the deviations of the inflection point are obtained, the calculation area (O'A'B'C'), which is denoted by \(D\) in the figure, can be determined from the mining area. Before the prediction of mining-induced subsidence, a coordinate system should be established. In general, the principles of coordinate axes are as follows: (a) the \(x\) axis is parallel to the strike direction of the coal seam and the right side of the strike direction is the positive direction of the \(x\) axis; (b) the \(y\) axis is parallel to the dip direction of the coal seam and the upper part of the dip direction is the positive direction of the \(y\) axis. In addition, the lower left corner of the mining area is taken as the origin of the coordinate system.  
Figure 1: Coordinate system used by PIM-based coal mining subsidence prediction. DD and SD denote dip direction and strike direction respectively.  
Based on the PIM and the coordinate system established above, coal mining-induced ground subsidence can be obtained by [30]:  
\[W(\varepsilon,\eta)=W_{cm}\cdot\iint\limits_{D}\frac{1}{r^{2}}\cdot e^{-\pi\frac {(x-x)^{2}+(y-y)^{2}}{r^{2}}}dxdy \tag{2}\]  
where \(W(\varepsilon,\eta)\) is the coal mining-induced subsidence at the ground point with the coordinate of (\(\varepsilon,\eta\)); (\(x\),\(y\)) is the planner coordinate of the mining unit, and \(r\) is the main influence radius of the mining area. This last can be related to the mining depth \(H\) by:  
\[r=\frac{H}{\tan\beta} \tag{3}\]  
where \(\tan\beta\) is the tangent of the main effect angle, and \(W_{cm}\) is the maximum ground subsidence at critical mining. This last can be written as:  
\[W_{cm}=m\cdot q\cdot\cos\alpha \tag{4}\]  
where \(m\) is mining thickness; \(q\) is the subsidence factor; and \(\alpha\) is the dip angle of the coal seam.
## 1 Introduction  
With the exponential growth of wireless Internet-of-Things (IoT) sensors and ultra-reliable requirement in the next-generation cellular networks, the global mobile data traffic is expected to reach about 1 zettabyte by 2022 according to Cisco's forecast [1]. To meet the demands of higher data traffic in wireless links either in fixed sensors for IoT networks or mobile devices in cellular networks, the network infrastructure inevitably will need to expand dramatically, which will result in tremendous escalation of energy consumption and backhaul traffic. Energy efficiency and spectral efficiency are therefore critical issues when designing next-generation wireless communication systems with enabling quality-of-service (QoS) guarantees for radio devices with considerations of efficient power consumption [2, 3].  
Enhancing the cell density is one of the approaches to meet the need of high date rate under limited bandwidth in centralized networks. Heterogeneous networks (HetNets) have therefore emerged as a standard part of future mobile networks to improve the system capacity and energy efficiency through more flexible design of transmission power allocation and smaller coverage sizes by densely deployed small base stations (SBSs) [3, 4, 5]. However, the interference problem caused from various SBSs is the primary challenge for effective system capacity improvement.
## 4 The Research Results
### Selection of the Rank of Downtime Categories and Downtime Periods
The other downtime categories were comparable in terms of the order of magnitude of the downtime. It concerns both \(UT_{K}\) = (10 \(\div\) 17)% and \(V_{K}\) = (0.83 \(\div\) 1.38). In the next part of the empirical data analysis, the dependence of the coefficient of unavailability of categorized buses on their annual service life was checked. For this purpose, the Spearman correlation analysis was conducted, the results of which are presented in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**L**} & \multicolumn{2}{c}{**Downtime Category**} & \multirow{2}{*}{**x(NB)**} & \multirow{2}{*}{**x(BCZ)**} & \multirow{2}{*}{**x(ON)**} & \multirow{2}{*}{**x(NW)**} & \multirow{2}{*}{**x(INN)**} & \multirow{2}{*}{**x(WS)**} & \multirow{2}{*}{**R\({}_{\rm s}\)**} \\ \cline{2-3} \cline{8-10}  & \multicolumn{2}{c}{**Bus Make**} & & & & & & & \\ \hline
1 & A & 1 & 3 & 4 & 2 & 5 & 6 & \(-\)0.88 \\
2 & C & 1 & 3 & 5 & 4 & 5 & 6 & \(-\)0.90 \\
3 & F & 1 & 3 & 4 & 2 & 5 & 6 & \(-\)1.00 \\
4 & B & 1 & 2 & 3 & 4 & 5 & 6 & \(-\)1.00 \\
5 & D & 1 & 2 & 3 & 4 & 5 & 6 & \(-\)1.00 \\
6 & E & 1 & 2 & 3 & 4 & 5 & 6 & \(-\)0.94 \\
7 & G & 1 & 2 & 4 & 5 & 3 & 6 & \(-\)0.61 \\  & x\({}_{\rm me}\) & & 1 & 2.93 & 3.71 & 3.57 & 4.71 & 6 & R\({}_{\rm s,me}\) = \(-\)0.90 \\  & X\({}_{\rm est}\) & & 1 & 2 & 4 & 3 & 5 & 6 & R\({}_{\rm s,me}\) = \(-\)0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Downtime ranks and Spearman’s rank correlation coefficient between categorized unavailability and downtime category. The results of the Spearman's rank correlation analysis showed for all the tested buses, except for the F model, that with the increase in vehicle usage time, the share of each category of downtime in the total vehicle downtime changed. For category NB--\"current repairs\", the impact of operating time was positive. This applied to the following vehicles: make A (R = 0.41), make B (R = 0.49), make C (R = 0.30), and make G (R = 0.26). A similar relationship was observed in the case of the WS category--\"engine replacement\". This applied to the following brands: A (R = 0.46), D (R = 0.36), and E (R = 0.18). For the ON category--\"pending repair\", the impact of calendar time was negative. Spearman's coefficients were A (R = \(-\)0.65), B (R = \(-\)0.40), C (R = \(-\)0.44), E (R = \(-\)0.22), and G (R = \(-\)0.25). A similar relationship occurred in the case of the INN category--\"other\", wherein the Spearman coefficients were A (R = \(-\)0.48), B (R = \(-\)0.59), E (R = \(-\)0.13), D (R = \(-\)0.26), and G (R = \(-\)0.40). On the other hand, the BCZ category--\"no spare parts\" was correlated with the service life in a different way. This relationship was positive for B model vehicles (R = 0.36) and negative for D (R = \(-\)0.42).
# Investigation on Residual Stress Loss during Laser
## 5 Conclusions  
A finite element model was developed to predict the residual stress distribution induced in 316 L stainless steel subjected to LPT. The effects of laser power density and laser spot radius on the residual stress loss were discussed, which may facilitate the optimization of the LPT procedure to obtain a more homogeneous surface refinement. The conclusions are as follows.  
1. A simulation model was proposed and the deformation, as well as the stress distribution of the specimen, was investigated. The profile of micro dimple calculated by the simulation agrees well with the measured ones in experiment.
2. An evaluation method of residual stress field was established. The residual compressive stress, the magnitude of residual stress loss, and the affecting zone size of residual stress loss can be characterized quantitatively by this method.
3. When the laser power density increases from 2.21 GW/cm\({}^{2}\) to 13.80 GW/cm\({}^{2}\), the magnitude of the residual stress loss increases from 46.2 MPa to 876.1 MPa along the radial direction, and from 132.1 MPa to 1297.5 MPa along the depth direction, respectively. Meanwhile, the affecting zone size of the residual stress loss increases from 59.2 \(\upmu\)m to 425.2 \(\upmu\)m along the radial direction and from 19.7 \(\upmu\)m to 274.4 \(\upmu\)m along the depth direction, respectively. When the laser power density  
Figure 16: The effect of laser spot radius on the size of the residual stress loss (\(I_{0}=2.21\) GW/cm\({}^{2}\)).  
exceeds a certain threshold, the residual stress on the surface changes from compressive stress to tensile stress.
4. Laser spot radius has a significant effect on residual stress loss. Large laser spot radius leads to more obvious residual stress loss. In this study, when the laser spot radius increases from 100 \(\upmu\)m to 400 \(\upmu\)m, the thickness and the width of the residual compressive stress layer increases from 175 \(\upmu\)m to 565 \(\upmu\)m and from 70 \(\upmu\)m to 315 \(\upmu\)m, respectively. However, the maximum residual compressive stress decreases from \(-\)495 MPa to \(-\)125 MPa along radial direction and decreases from \(-\)495 MPa to \(-\)350 MPa along depth direction, respectively.  
Conceptualization, K.L.; methodology, K.L.; software, Y.W. and Y.C.; validation, K.L., Y.W.; formal analysis, K.L.; investigation, Y.W.; resources, Y.C.; writing--original draft preparation, K.L.; writing--review and editing, J.H.; visualization, K.L.; supervision, K.L. and J.H.; project administration, K.L.; funding acquisition, K.L. and J.H.  
This research was funded by the National Natural Science Foundation of China, grant number 51605296 and the Fundamental Research Funds for the Central Universities, grant number 2232018D3-04, 2232018A3-08.  
The authors declare no conflict of interest.
## 3 Localised Information Retrieval with Query Resolution
### Balancing of Energy Consumption and Rotations of a Sink Proxy  
Query content varies according to users' interests, and the group of sensor nodes responding to a query varies with the query contents. This query diversity generally enables localised data collection to not only alleviate the overuse of energy at nodes near the sink node, but also to avoid overuse of energy at particular sensor nodes, such as sink proxies, achieving distributed energy consumption among sensor nodes.  
In the case many users have same query interest at different times, a fixed sink proxy will result in an energy overuse at the sink proxy corresponding to these queries. This is because that the multihop relay features of wireless sensor networks require nodes near (in hop-distance) the sink proxy relay packets from nodes further away from the sink proxy [30, 31]. To avoid such overuse of energy at a sink proxy, a rotation operation on the sink proxy is utilised. When a sink node receives a query that asks for the same content as that of the previous query, it will assign a new sink proxy randomly in the query area, in order to distribute the energy among the local sensor nodes corresponding to the query. We use LDQD-random and LSDC-random to generally represent the approaches that use randomly selected nodes as sink proxies for LDQD and LSDC.
## 3 Results
#### 3.2.3 Hydrogen Storage Properties  
The kinetics of the hydrogen activation of the Ti\({}_{1.26}\)V\({}_{0.315}\)(FeV)\({}_{0.315}\)Cr\({}_{1.11}\) + 4% Zr\({}_{7}\)Ni\({}_{10}\) and co-melt Ti\({}_{1.26}\)V\({}_{0.63}\)Cr\({}_{1.11}\) + 4% Zr\({}_{7}\)Ni\({}_{10}\) are presented in Figure 9. The main difference is clearly that the alloy containing ferrovanadium presents a long incubation time, while hydrogen absorption starts immediately for the alloy with pure vanadium. For both alloys, the full capacity is reached in about 200 s after the reaction started. The maximum capacity of ferrovanadium alloy (3.2 wt % of hydrogen) is slightly smaller than its pure vanadium counterpart (3.4 wt %).  
From Figures 4 and 9 we see that replacement of vanadium by ferrovanadium made the first hydrogenation starts only after an incubation time. Once the reaction starts, the intrinsic kinetics is almost the same as for the alloy without ferrovanadium. The reason of appearance of this incubation time is still not clear. One possible reason may be the nature of the surface of the particles. Alloys that are containing iron may have a thicker oxide on surface, thus making the incubation time longer. But, this hypothesis has to be confirmed by further tests.  
Figure 10 exhibits the Pressure Composition desorption isotherms (PCT) of Ti\({}_{1.26}\)V\({}_{0.63}\)Cr\({}_{1.11}\) + 4%Zr\({}_{7}\)Ni\({}_{10}\) recorded at 80, 100 and 130 \({}^{\circ}\)C. Once again, as the temperature increases, so does the equilibrium pressure. A Van't Hoff plot has been made using the pressure value at the middle of the plateau. The formation enthalpy of the hydride has been determined to be \(-\)38 kJ\(\cdot\)(mol\(\cdot\)H\({}_{2}\))\({}^{-1}\) and its associated entropy \(-\)138 J\(\cdot\)(mol\(\cdot\)H\({}_{2}\))\({}^{-1}\).K\({}^{-1}\). These values are very close to the ones found for Ti\({}_{1.26}\)V\({}_{0.63}\)Cr\({}_{1.11}\) + 4% Zr\({}_{7}\)Ni\({}_{10}\) hydride, which were determined to be \(-\)38 kJ\(\cdot\)(mol\(\cdot\)H\({}_{2}\))\({}^{-1}\) and its associated entropy \(-\)125 J\(\cdot\)(mol\(\cdot\)H\({}_{2}\))\({}^{-1}\).K\({}^{-1}\)[13].
# Urban Rural Differences in Breast Cancer in New Zealand
## 4 Discussion  
The key findings from this study is that there is no evidence that rural women in New Zealand are more likely to present with advanced stage of disease or have poorer outcomes. In our two regions it appears that rural women with breast cancer are older than urban women and that there are proportionately more rural Maori than urban Maori. The cancer-specific mortality rates for rural women were similar suggesting that access to breast cancer treatment is at least as good for rural women as urban. However for Maori women with breast cancer outcomes are worse than for urban Maori. Rural Maori women do have lower rates of screen detected cancer and higher rates of metastatic disease. Overall there is a suggestion that both their breast cancer-specific mortality rate and all-cause mortality rate are not improved compared with urban Maori and may even be over 40% worse. One possible explanation for this is that deprivation may be more of an issue for Maori living in rural areas compared with non-Maori women which may have an additionally deleterious effect on survival. Similar observations have been reported from the USA. In one study socio-economic deprivation for native American Indians appeared to be more important than rurality [12] while in another study rural black American women were less likely to be screened and had more advanced disease at diagnosis [13]. Within New Zealand we know that there are health system barriers for Maori women with breast cancer. These include access to primary care and that when accessing specialist care lack of \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Groups** & \multicolumn{2}{c}{**Unadjusted Hazard**} & \multicolumn{2}{c}{**Adjusted Hazard 1**} \\  & \multicolumn{2}{c}{**Ratio (95\% CI)**} & \multicolumn{2}{c}{**Ratio (95\% CI)**} \\ \hline
**Breast cancer-specific mortality** & & & & \\ Rural women compared with urban women & 1.08 & (0.96–1.22) & 1.01 & (0.87–1.17) \\ Rural Maori women compared with urban Maori women & 1.31 & (0.97–1.76) & 1.47 & (1.00–2.16) \\ Rural European women compared with urban European women & 1.04 & (0.90–1.19) & 0.94 & (0.79–1.10) \\ \hline
**All-cause mortality** & & & & \\ Rural women compared with urban women & 1.13 & (1.03–1.24) * & 1.04 & (0.94–1.16) \\ Rural Maori women compared with urban Maori women & 1.33 & (1.05–1.68) * & 1.43 & (1.08–1.91)* \\ Rural European women compared with urban European women & 1.05 & (0.94–1.16) & 0.90 & (0.80–1.02) \\ \hline \hline \end{tabular}  
* Adjusted for age, (ethnicity), cancer stage, tumour size, grade, hormonal status (ERPR), year of diagnosis, mode of detection and comorbidity.
* \(p\)-Value < 0.05.  
\end{table} 
Table 4: Hazard ratio for mortality estimated by Cox proportional hazards model.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
###### Abstract  
Recent advances in self-supervised learning have allowed automatic speech recognition (ASR) systems to achieve state-of-the-art (SOTA) word error rates (WER) while requiring only a fraction of the labeled data needed by its predecessors. Notwithstanding, while such models achieve SOTA results in matched train/test scenarios, their performance degrades substantially when tested in unseen conditions. To overcome this problem, strategies such as data augmentation and/or domain adaptation have been explored. Available models, however, are still too large to be considered for edge speech applications on resource-constrained devices; thus, model compression tools, such as knowledge distillation, are needed. In this paper, we propose three innovations on top of the existing DistilHuBERT distillation recipe: optimize the prediction heads, employ a targeted data augmentation method for different environmental scenarios, and employ a real-time environment estimator to choose between compressed models for inference. Experiments with the LibriSpeech dataset, corrupted with varying noise types and reverberation levels, show the proposed method outperforming several benchmark methods, both original and compressed, by as much as 48.4% and 89.2% in the word error reduction rate in extremely noisy and reverberant conditions, respectively, while reducing by 50% the number of parameters. Thus, the proposed method is well suited for resource-constrained edge speech recognition applications.  
automatic speech recognition; knowledge distillation; self-supervised learning; modulation spectrum; context awareness +
Footnote †: journal: Applied sciences  
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