esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 6 Evaluation Results and Discussion
### Evaluation of XRey  
This subsection presents the results of the evaluation performed on the models trained on the corpus _XRey_. For that purpose, the following metrics were used: the MOS estimation by MOSnet and NISQA, the fraction of correctly aligned sentences, and the fraction of correctly aligned characters.  
The results of these metrics can be found in Figure 5.  
The results presented in Figure 5 provide clear evidence of substantial improvement across all four measured metrics following the exclusion of audio files with high variability from the training set. It is worth mentioning that, even in the case of the most-drastic ablation, where 50% of the training data were excluded (removing the SNR and utterance speed), the resulting metrics displayed noteworthy enhancement.  
Regarding the two selected MOS estimator models, it can be observed that MOSnet was significantly more generous than NISQA in this environment. Nevertheless, these MOS values indicated a considerable quality, especially for MOSnet, taking into account that the training audio files were recorded in the third quarter of the 20 th Century.  
In the case of the fraction of aligned sentences, it can be seen that it reached values near 100% in the early stages of training, showing that this metric was not really suitable for comparing the quality of a given voice-cloning system. The aligner was, however, performing in the way it was conceived of, that is trying to correctly align the highest possible number of sentences.  
In contrast, the fraction of aligned characters did indeed show a rising trend while the training progressed. This metric, as is shown in the measurements for the other training configurations, rarely reached values near 100%, since not every input character should  
Figure 5: Fraction of aligned characters (**above left**) and sentences (**above right**) and the estimated MOS values obtained from NISQA (**below left**) and MOSnet (**below right**) for audio generated for each iteration for the speaker _XRey_.  
have a direct effect on a particular step of the output spectrogram, particularly in the case of spaces or punctuation marks.  
As the main conclusion to this case, in which low-quality audios were involved, it can be stated that excluding the audios with the higher variability of the SNR and speed levels helped not only improve the quality of the models, but also reached better MOS estimator metrics faster, even though the training data were composed of less than 3 h of audio content.
## 5 Conclusions  
This study proposes a new PQUM-DNS model, which is an intelligent device management system that combines pruning, quantization, meta-learning, anomaly detection, prediction using AEs, adaptive model retraining, and edge inference. This system effectively reduces the manual labor, provides fault notifications, prevents downtime, decreases model computational resources, accelerates the model inference speed, and enables edge inference.  
The system is suitable for various factory scenarios and types of machine equipment and process states. Compared with general DAEs, the system achieves a similar AUC while reducing the training data by approximately 75%. The average RMSE of the predictive degradation degree is 0.037 for Holt-Winters, retraining is conducted using 75% fewer data with similar AUC performance, and the model size is reduced by approximately 60% through pruning and quantization. The proposed system can be deployed on lightweight edge devices, such as a Raspberry Pi, enabling real-time anomaly detection and prediction. The system demonstrates superior performance, thereby realizing intelligent equipment management and maintenance.  
Conceptualization, Y.-C.Y.; methodology, Y.-C.Y. and S.-W.C.; software, Y.-C.Y.; validation, Y.-C.Y.; formal analysis, Y.-C.Y.; investigation, Y.-C.Y.; resources, C.-Y.L. and S.-W.C.; data curation, Y.-C.Y.; writing--original draft preparation, Y.-C.Y.; writing--review and editing, C.-Y.L. and S.-R.Y.; visualization, Y.-C.Y.; supervision, C.-Y.L., J.-T.C. and S.-W.C.; project administration, C.-Y.L., Y.-C.Y. and S.-R.Y.; funding acquisition, Y.-C.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This data can be found here: [[https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets-and-download](https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets-and-download)]([https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets-and-download](https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets-and-download)), accessed on 1 June 2023 (Paderborn University Bearing Dataset) and [[https://archive.ics.uci.edu/dataset/179/second](https://archive.ics.uci.edu/dataset/179/second)]([https://archive.ics.uci.edu/dataset/179/second](https://archive.ics.uci.edu/dataset/179/second)), accessed on 1 June 2023 (SECOM Dataset).  
Thanks for the data support from Advantech Taipei headquarters and the technical support from SI2 Laboratory at National Yang Ming Chiao Tung University.  
The authors declare no conflict of interest.  
Figure 18: Comparison of PQUM-DNS and DAE model sizes for different data types.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 7 Evaluation
### Research Questions  
In this section, we present the research questions and elaborate on our motivations to evaluate them:  
* _Question 1: What is the performance overhead of reconfigured features during normal operation of cloud-native applications compared to native features supported by Kubernetes?_ One of the goals of our vendor-agnostic reconfiguration tactics and controller is to reduce the operational cost of multicluster management. However, if the reconfigured feature has a significant performance overhead on the normal operation of the cluster or application, this would outweigh the benefits of vendor-agnostic reconfiguration. So, we need reconfigured features to have as little impact on performance as possible in comparison to a Kubernetes cluster where the features are configured as instructed in the official K8s documentation [5].
* _Question 2: What is the disruption impact on running applications when reconfiguring?_ For Kubernetes production clusters, we want the controllers to have less impact on running applications or services when executing reconfiguration tactics to ensure the normal operation of the business.  
* _Question 3_: _What is the time to reconfigure features in a newly created cluster without any application running?_ In the context of cloud bursting, K8s clusters must be set up only when there is an imminent workload peak. As such, clusters must be launched and configured as quickly as possible.
## 4 Hyperspectral Image Destriping Method
### Discussion
#### 5.4.2 Spectral Analysis  
In this part, we further prove that our method can effectively preserve important spectral information before and after correction. Figures 24 and 25 show the comparison of the spectral curves of water and vegetation with the methods based on the same paradigm.  
Figure 23: Our method. (**a**) Band 1; (**b**) band 5; (**c**) band 10; (**d**) band 15; (**e**) band 20; (**f**) band 25; (**g**) band 30.  
According to the water and vegetation spectral curves, the moment matching [see Figures 24a and 25a] and MMUTV [see Figures 24e and 25e] have a large change of the water and vegetation spectral curves. The UTV method [see Figures 15c and 24c] affects the peaks of some bands, and the corrected vegetation spectral curve was lower than the original one. Other methods can better maintain the original spectral curve. The peaks and troughs of the waves are consistent with the original ones.  
Figure 26 shows the comparison of the spectral curves of water with methods based on the different paradigm. The spectral curves of all methods are basically consistent with the original spectral curves. However, WFAF has a smoothing effect, and some of its peaks are weakened.  
Figure 24: Spectral curve result of OHS (water). (**a**) Original image and moment matching spectral curve; (**b**) original image and adaptive moment matching; (**c**) original image and unidirectional total variational (UTV); (**d**) original image and MUTV; (**e**) original image and MMUTV; (**f**) original image and our method.  
Figure 25: Spectral curve result of OHS (vegetation). (**a**) Original image and moment matching spectral curve; (**b**) original image and adaptive moment matching; (**c**)original image and UTV; (**d**) original image and MUTV; (**e**) original image and MMUTV; (**f**) original image and our method.  
Figure 27 shows the comparison of the spectral curves of vegetation of hyperspectral images. Since the method of hyperspectral stripe noise removal comprehensively considers the spectral correlation, the corrected spectral curve is not completely consistent with the original curve. Among them, LRTD makes the spectral curve too smooth, thus damaging its spectral information, and destroying the correlation between the first 15 bands. LRSID well maintains spectral correlation, but it cannot completely remove noise. Since our corrected curve is the same as the original curve, the spectral information of all bands is well preserved. Although the spectral information of some bands deviates from the original one, most of the spectral information is also preserved.
# Protozoa as the \"Underdogs\" for Microbiological Quality Evaluation of Fresh Vegetables
_Cyclospora cayetanensis_ is a human coccidia parasite that causes cyclosporias, a disease that causes acute diarrhea and other gastroenteritis symptoms. Humans are the only known host for _C. cayetanensis_ oocysts, and their zoonotic role remains to be determined [29, 30]. Two of the main reasons for the biological success of these parasites are their low infectious dose and their excretion in feces into the environment as resistant stages (cysts or oocysts) [31, 32]. The (oo)cysts possess the ability to survive for a long period of time (weeks, months, or even years) in the environment, in extreme conditions of temperature and humidity, due to their structure, which protects them in exogenous stages. This allows the parasites to resist the common inactivation methods used for bacteria and viruses, such as heating, irradiation, and chemical disinfection [33, 34, 35, 36, 37, 38]. Fresh vegetables may be contaminated at several steps along the food production chain, such as during crop production, harvesting, and processing, or directly by infected food handlers. Potential sources include water (irrigation water or water used for washing produce), wastewater discharge, soil contaminated with fecal waste from warm-blooded animals/livestock, organic fertilizers, and human handling during downstream processing and packaging steps [5, 38, 39, 40, 41]. In a recent work performed by our team [42], within the scope of the SafeConsume project ([[https://safeconsume.eu/](https://safeconsume.eu/)]([https://safeconsume.eu/](https://safeconsume.eu/)), accessed on 11 July 2022), we designed a laboratory approach for the detection of _T. gondii_ oocysts in vegetables and berry fruits based on the knowledge gained with Method 1623.1/EPA for the detection of the _Cryptosporidium_ oocyst and _Giardia_ cyst [43; 44]. Moreover, we also described a new recombinant _T. gondii_ oocyst wall protein 1-derived fragment, rTgOWPI-f. Strong microscopic evidence of the ability of rabbit polyclonal antibodies, anti-rTgOWP1-f, to identify environmental _T. gondii_ oocysts, suggests rTgOWPI-f may be a potential biomarker for the detection of environmental oocysts [45]. Our studies clearly indicate that consumption of fresh produce may be a source of _T. gondii_ infection in humans and a potential risk for consumers. However, technical refinement is still required for routine application at the industrial level or for food testing in laboratories for the detection of _T. gondii_ oocysts. Taking into account the experience and learning obtained from the SafeConsume work package 2 (WP2) laboratory studies, and the \"One Health\" approach, a parallel preliminary study was undertaken in order to assess the microbiological quality of fresh vegetables, highlighting not only bacteria (_E. coli_, _L. monocytogenes_, and _Salmonella_ spp.) covered by the legislation [17], but also the zoonotic protozoa _G. duodenalis_ and _Cryptosporidium_ spp.
## 2 Materials and Methods  
### Study Region  
As shown in Figure 1a, the road project (red block) is situated in the permafrost region of the Great Khingan Mountains, in northeast China. The longitude of this region is between 118 deg12'01'' E and 127 deg10'24'' E, and the latitude is between 46 deg58'02'' N and 53 deg13'37'' N [3]. This project is the middle connecting section of National Highway 332 Gagadaki Ali River Section Highway and Gen River Labudalin Section Highway. The starting point is situated in the west of Alihe Town at K0+000, and the ending point is situated in the west of Kubuchun Forest Farm at K116+700. The total length of this highway is 116.7 km. The main line is constructed according to the standard of Class I highway, with a design speed of 80 kilometers per hour and a subgrade width of 12.75 m.  
Permanfrost sections are mainly between K41+910 and K116+700. This section is covered with dense vegetation. The natural permafrost table is shallow and the volume ice content is high, so it is extremely sensitive to temperature changes. The subgrade construction here very easy to damage the underlying permafrost, which will affect the stability of the permafrost subgrade. In this section, as shown in Figure 1b, the permafrost subgrade section (red dot) at K67+300 is the object of our on-site observation.  
The K67+300 subgrade section has a latitude of 50\({}^{\circ}\)30\({}^{\prime}\)18\({}^{\prime\prime}\) N, a longitude of 122\({}^{\circ}\)55\({}^{\prime}\)30\({}^{\prime\prime}\) E, and an altitude of 574.45 m, as shown in Figure 2. It is characterized by dramatic changes in temperature, short summer, rapid cooling in autumn, cold winter, groundwater development, and abundant precipitation. According to the field observation data, the annual average temperature is between \(-\)2.7 \({}^{\circ}\)C and \(-\)1 \({}^{\circ}\)C. The highest temperature in a year is 30 \({}^{\circ}\)C, the lowest temperature is \(-\)40 \({}^{\circ}\)C, and the annual temperature difference reaches 70 \({}^{\circ}\)C. The average annual precipitation is 460 mm, which is mainly concentrated in summer [24]. The freezing period lasts from October to May of the next year. The annual sunshine hours are 2500 to 3100 h, and the sunshine rate is 65 to 71% [7].  
According to the engineering geological survey report, the surface layer of the proposed subgrade is covered with 0.5 m thick clay, and the underlayer is gravelly soil. The depth of the permafrost is shallow, with the permafrost table being 0.9 m, and the lower limit is 4 m. Permafrost is mainly attached to gravelly soil. The type of frozen soil is mainly ice-rich frozen soil.  
Figure 1: Map of the study region. (**a**) The location of the permafrost region of the Greater Khingan Mountains (red square) in China; (**b**) the location of the research project in the permafrost region of the Greater Khingan Mountains.
# Improvements of the xAAL Home Automation System
## 3 The xAAL Devices Behavior and xAAL Schemas
### Definition of a Device  
A _device_ has a dev_type and an address.  
* dev_type: This references the _schema_ and defines the type of the device. It is hard-coded into the device.
* This schema identifier is a string consisting of a pair of words separated by a dot dev_type:_class.variant_
* The first word refers to a class of device type (e.g., lamp, switch, thermometer, power plug).
* The second word refers to a variant of a given class (e.g., within the lighting class one may have an on-off lamp, a lamp with dimmer, an RGB lamp). The second word may also refer to a schema extension by a manufacturer (cf. Section 3.3).
* The keyword \"any\" is reserved and acts as a wildcard for requests. So that the identifier \"any.any\" is reserved and refers to all variants of all classes within request messages. This is a _virtual_ type. It is not allowed to define a schema named \"any.any\". No one can claim to be
of the type \"any.any\". Devices having no dedicated attribute, method or notification may use the _concrete_ type \"basic.basic\". * For example, the pair \"lamp.any\" means all variants of the class \"lamp\". This is also an abstract type. Remember that the concrete type \"lamp.basic\" is provided to describe basic features common to all lamps. * We reserve a special name: \"experimental\", for the class, as well as for variants. This designates concrete types. Associated schema, if written, should not be distributed outside the testing platform (e.g., when someone makes a device but has not got a standard name for its type.)
* Address: The device ID, unique on the bus.
* This device identifier is a UUID (RFC 4122 [25]), a 128-bit random number.
* Addresses are self-assigned. There is no naming service anywhere. A device does not request any entity to get an address. Concretely, addresses may be assigned:
* Either this is hard-coded in the factory.
* Auto-generated (random) at the time of installation. However, it is recommended that this address remain persistent (i.e., please save it, if possible, during power breaks).
* There is in principle very high probability of having no collision of UUIDs. However, it is technically possible to verify that a UUID is not already used on the bus by a kind of \"Gratuitous ARP\", i.e., by is_alive requests to ensure that no one else already uses this address.
* However, certainly not assigned by a bus supervisor/coordinator or something like that.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Methodology
#### 2.4.2 Partial Least Square Regression (PLSR)  
PLSR is a machine learning technique that can convert a large number of correlated predictors to a small number of uncorrelated predictors and then perform analysis on the reduced number of uncorrelated predictors [71]. As discussed above, we assume that the independent variables in linear regression models are not correlated with each other, and any presence of correlation can make linear regression models unsuitable for handling data with high dimensions and correlated independent variables. If there are more potential predictors and a possibility of multicollinearity in predictors, it is highly likely that MLR will have overfitting issues. In such cases, the model will have poor performance with new data [72]. PLSR characterizes the factors which can account for more variations in the dependent variable. PLSR is more focused on predicting the dependent variable (corn yield in this case) as compared to understanding the underlying relationship between independent variables (predictors) [73].
## 3 Results and Discussion
### Comparison EDM and KSD Model
The formation of the four local maxima is caused by the given air supply. According to the KSD model, the water concentration results from the faster outward diffusing hydrogen at the outer edges of the flame, which reacts earlier with the inflowing air to form water than in the EDM model. In calculation by EDM model, a hydrogen jet enclosed by the air is formed and reacts uniformly to water. The local minima caused by the inflowing cold air jet are also represented in the temperature distribution as well as a decrease in the O\({}_{2}\) fractions at the outer air nozzle diameter. The increase in O\({}_{2}\) concentration and decrease of H\({}_{2}\)O correlates with the maximum temperature along the central axis (Figure 6). Since for the KSD model the hydrogen is completely consumed at a height of 7 mm, the peak temperature is shifted downstream towards the gas inlet, whereas for the results from the EDM model, H\({}_{2}\) is not entirely used until a height of \(z=12\) mm. However, for completeness, it is noted that the maximum flame temperatures compared to the adiabatic flame temperature is overestimated by 4%. This could be due to the assumptions that radiation is neglected, and the adiabatic calculations lead to higher temperatures. Another possible factor can be the infinitely fast chemistry assumption. Under the selected conditions the diffusive and chemical time scales have the same order of magnitude which can lead to a deviation in the mixing and burning behavior compared to finite-rate chemistry. Last but not least the thermal diffusion impact is neglected in this study which can lead to a variance of the flame speed of up to 10% and therefore also impacts the temperature development. Both possible reasons can also lead to an over prediction of H\({}_{2}\) and an under prediction of N\({}_{2}\) at the centerline of about 20%.
# Damage Characteristics of Thermally Deteriorated Carbonate Rocks: A Review
## 2 Thermal Dilatancy and Alteration in Rock Fabric
### Thermal Decomposition of Calcite and Dolomite
\begin{table}
\begin{tabular}{c c c} \hline
**Mineral Type** & **Major Developments** & **Reference** \\ \hline Calcite & The rate of mass loss was studied using isothermal and dynamic methods. & [34] \\ \hline Calcite & A comparative study was conducted using isothermal–dynamic techniques and & [35] \\  & thermogravimetric analysis. & \\ \hline Calcite & Morphological variations were studied in polycrystalline CaCO\({}_{3}\) under temperature & [36] \\  & and pressure. & \\ \hline Dolomite & Thermal decomposition and weight loss analysis was carried out under non-isothermal & [37] \\  & conditions using in situ X-ray diffraction and thermogravimetry. & \\ \hline Calcite & Reaction rate constants were determined based on the grain model using thin & [38] \\  & slab-type pellets. & \\ \hline Calcite & Thermal decomposition was analyzed using dynamic X-ray diffraction under the effect of & [39] \\  & steam and CO\({}_{2}\). & \\ \hline Dolomite & Prediction of rate of reaction using stoichiometric analysis and thermogravimetric analysis. & [27] \\ \hline Calcite & Thermal decomposition was investigated using thermogravimetric analysis subjected to & [40] \\  & non-isothermal conditions. & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of major developments regarding the thermal decomposition of primary carbonate minerals.