esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation and Trends
The horizontal line in the boxplot corresponds to the median, and the ranges represent the 1 st and 3 rd quartiles, respectively. The average values for cities, for the annual sum of precipitation in the current climate, in both ensemble models and observations are very similar, reaching roughly 627 mm. Also, regarding the variability by cities, represented by the violin plot range, the disparity between the observations and model ensemble is small, occurring during the 3 rd quartile, for about 10 mm. In both scenarios, trend lines for the 2030 horizon suggest an increase in the precipitation sum, with the RCP4.5 scenario being more dynamic. This increase is predicted to be roughly 674 mm. Substantial differences are predicted for the 2050 horizon, where scenario RCP8.5 tends to be more dynamic than RCP4.5. The average value for all cities in the RCP8.5 is about 700 mm compared to 680 mm in the RCP4.5 scenario. Small differences between the models' ensemble and observations in the current climate (2010) are visible regarding the number of days with precipitation. For these observations, the average value for the cities is 105.5 days compared with 105 days for the models (Figure 10). The spread of the values for observations ranges from 96 to 111.5 days. Both scenarios predict a slight increase in the future climate, with a more dynamic trend in the RCP8.5 scenario. In the next horizon, a further increase is predicted for both scenarios by approximately 2.5 days. The number of days with extreme precipitation exceeding 10 mm per day for the current climate ranges from 13.7 days for the 1 st quartile to 16.8 for the 3 rd quartile of observations. Model ensembles for the analyzed emission scenarios, in terms of spread values, are quite similar, with differences at approx. 0.05 days (Figure 10). For the horizon of 2030, an identical increase is expected for both scenarios. A small difference in terms of disparity by location is also expected. An average increase of up to 16.3 days is predicted for all cities. In the second time horizon to the year 2050, a more dynamic increase is expected for scenario RCP8.5 up to approx. 18.5 days, compared to a smaller increase in scenario RCP4.5 for up to approx. 17.4 days. Figure 10: _Cont._
## 4 Experimental  
In this paper, discharges of different intensity or magnitude are generated by increasing the voltage applied to a needle-plane air gap configuration. To emulate pressure conditions in aircraft systems, the discharges are generated inside a low-pressure chamber in the pressure range of 10-100 kPa at 20 \({}^{\circ}\)C.  
The stainless-steel low-pressure chamber consists of a 375 mm \(\times\) 260 mm (height \(\times\) diameter) cylindrical vessel and a methacrylate lid that allows the photographs to be wirelessly transmitted to a computer located near the chamber. The chamber is grounded during the experiments. The low-pressure chamber contains the smartphone used to take the long exposure photographs and the needle-plane gap, as shown in Figure 4.  
The pressure inside the chamber was changed using a vacuum pump (BA-1, 1/4 HP, 85 L/min, Bacoeng, Suzhou, China). The ac voltage was changed by means of an adjustable ac high-voltage source (TecnolabRD6, 0-14.4 kV\({}_{\text{peak}}\), 600 VA, dp Electronica SL, Barcelona, Spain).  
The applied voltage and leakage current were acquired synchronously using a high-performance oscilloscope (MDO3024, 200 MHz, 2.5 Msamples/s, Tektronix, Beaverton, OR, USA). A high-voltage probe (CT4028, 0-39 kV\({}_{\text{peak}}\), reduction ratio 1000:1, accuracy \(<\) 3%, dc to 220 MHz, Electronics, Yorba Linda, CA, USA) was used to adapt the high voltage to the requirements of the oscilloscope. The leakage current circulating through the ground conductor (see Figure 4) was measured using a Hall effect current probe (TCP0030A, dc to >120 MHz, accuracy 1 mA, Tektronix, Beaverton, OR, USA), which was placed around the ground wire to detect the current of the discharges travelling from the high-voltage electrode to the ground terminal.  
Figure 4: (**a**) Sketch of the experimental setup used to reduce the pressure and generate electric discharges. (**b**) Needle-plane air gap (stainless steel, diameter = 0.8 mm).  
It is well known that discharges emit near-UV and visible light. In this work, they were acquired by means of a high-resolution back-illuminated CMOS imaging sensor (sensor size 8.0 mm, cell size 0.8 \(\upmu\)m \(\times\) 0.8 \(\upmu\)m, 48 Mpixels, lens focal 17.9 mm, raw format images, IMX586, Sony, Tokyo, Japan). This type of sensor was used because it is known to be sensitive to both visible and UV radiation [38; 39]. A computer connected to a Wi-Fi router wirelessly controlled the CMOS image sensor, so images could be taken from inside the chamber while it was pressurized. Due to the low intensity of the discharges, to improve the sensitivity of the images, the camera was operated in the long exposure mode (exposure time of 32 s, ISO 800, manual focus).  
Corona discharges were generated using a needle-plane air gap, because this geometry is one of the reference gaps used in high-voltage applications [40]. It consists of a 0.8 mm diameter cylindrical stainless-steel needle with a 70 mm long air gap.
## 2 Scanning Electron Microscopy and Mineral Characterization
### Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN)
\begin{table}
\begin{tabular}{c c c c} \hline
**Investigation** & **Electron Microprobe** & **XRD** & **QEMSCAN** \\ \hline Mineral texture & \(\blacktriangle\) & \(\bullet\) & \(\blacktriangledown\)\(\blacktriangledown\)\(\blacktriangledown\) \\ Mineral distribution and associations & \(\blacktriangle\) & \(\bullet\) & \(\blacktriangledown\)\(\blacktriangledown\) \\ Mineral-specific particle size information & \(\blacktriangle\) & \(\blacktriangle\) & \(\blacktriangledown\)\(\blacktriangledown\) \\ Mineral abundance & \(\bullet\) & \(\blacktriangledown\)\(\blacktriangledown\) & \(\blacktriangledown\)\(\blacktriangledown\) \\ Amorphous minerals (geothite, silica) & \(\blacktriangledangledown\)\(\blacktriangledown\) & \(\bullet\) & \(\blacktriangledown\)\(\blacktriangledown\) \\ Distribution of minor metals within minerals & \(\blacktriangledangledown\)\(\blacktriangledown\) & \(\bullet\) & \(\blacktriangledown\) \\ Crystallinity (clay, silica, geothite, and limonite) & \(\blacktriangledangledown\)\(\blacktriangledown\) & \(\blacktriangledown\)\(\blacktriangledown\) & \(\bullet\) \\ \hline \end{tabular}
\end{table} 
Table 1: Techniques used for investigating mineralogy and their comparison. \(\blacktriangledown\) indicates good, \(\bullet\) represents poor, while \(\blacktriangle\) suggests it is possible but not recommended [130]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analytical Methods/Techniques** & **Year** & **Minerals/Materials** & **References** \\ \hline SEM/SDD-EDS, EPMA-WDS, & 2023 & Major and minor elements in minerals and rocks & [134] \\ \hline SEM–EDS, XRD & 2022 & Constituent minerals in shales & [135] \\ \hline SEM, TEM & 2018 & Microbial biofilms, mineral precipitation & [136] \\ \hline SEM-BSE, TMR, SMH & 2018 & Mineral content in enamel lesions & [137] \\ \hline SEM, \(\mu\)XRF, LWIR, SAM & 2020 & Quartz, olivine, kyanite, and diopside & [138] \\ \hline SEM–EDS, Raman Spectroscopy & 2019 & Asbestos & [139] \\ \hline SEM–EDX, XPS, XRD, FT-IR, UV & 2020 & Kaolin, illite, gibbsite, and quartz & [140] \\ \hline SEM, XRD, TGA, IR, TXRF & 2022 & Mineral constituents in human renal calculi & [141] \\ \hline SEM-FIB & 2021 & Mineralized bone & [142] \\ \hline SEM–EDS & 2021 & Mineralizing fluids, sedimentary brines & [143] \\ \hline SEM-FIB, \(\mu\)CT, XLH & 2020 & Crossfibrillar mineral tessellation & [144] \\ \hline SEM, BSE, EDS, & 2022 & Sandstone & [145] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recent SEM applications focusing on mineral identification, quantification, and characterization. Figure 8: QEMSCAN analysis indicating mineral distribution in four different zones [130]. CC-BY.
###### Abstract  
The present study integrates the importance-satisfaction (I-S) model and service quality performance matrix (SQPM) to examine long-term care (LTC) service demands and satisfaction improvement. Many scholars have used a single model to explore project improvement. Each model has advantages, but we think they are too subjective and suggest that it is best to integrate models to determine what should be improved. We established quality attributes of service demands based on more than two sessions of discussions and expert consultations with LTC service users (older adults). The final questionnaire was divided into three parts: a demand survey, satisfaction survey, and demographics survey, and 292 valid questionnaires were collected. The questionnaire items were summarized with means and standard deviations. In this study, if only the I-S model was used to examine LTC in Taiwan, then seven service elements of the system would need to be improved. However, if only the SQPM method was used, then 16 service elements would need to be improved. Only seven service elements were identified by both methods. When time and resources are limited, it is not feasible to take comprehensiveness into account. When many projects must be improved and it is impossible to implement them at the same time, improvement priorities need to be developed. Taiwan lacks sufficient LTC resources, so it is impossible to provide enough resources for all those who need care. To use resources efficiently, the I-S model and SQPM were integrated in this study to identify areas for improvement.  
Long-term care (LTC); importance-satisfaction (I-S) model; performance evaluation matrix (PEM); service quality performance matrix (SQPM); voice of customer (VOC)
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 7 Conclusions  
In conclusion, the results indicate that decimation improved the model performance for high-performance algorithms, such as XGBoost, ridge, and logistic regression, where they achieved accuracy levels above 96% in the ship classification scenario. Low-performing models, such as SVM and MLP, showed significant decreases in accuracy and higher standard deviations, suggesting less stability and repeatability across runs. Without decimation, models such as KNN and logistic regression achieved almost perfect accuracy (99.5%), demonstrating the benefit of reducing the data variance. In contrast, MLP shows a decline in performance, possibly owing to overfitting or poor model-data interaction.  
Decimation resulted in faster computation by reducing the number of data points, which led to faster relationships between the features and predictions, although this affected the model consistency. The preprocessing method played a key role in stabilizing the results, and this should be considered when prioritizing the accuracy and stability of the model.  
The use of smaller feature vectors, such as those derived from the Lofar signals processed with PCA and NLPCA, usually led to better results, suggesting that smaller feature sets are feasible for efficient classifiers, particularly in applications such as naval missions where known ship types are identified. Finally, XGBoost stood out as the most consistent and effective model, making it an ideal choice for various classification tasks.  
Conceptualization: L.L., T.N.F.; Software: A.A.d.S.; validation: A.A.d.S., L.L., T.N.F.; writing: A.A.d.S., L.L., T.N.F.; supervision: T.N.F.; funding acquisition: L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Brazilian Navy and CNPq (Process 312743/2023-8).  
Not applicable.  
Data Availability Statement: Data is property of Brazilian Navy.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Phase I: Greenhouse Column Study
#### 3.2.2 Cu Distribution in Leachates and Soil  
In columns planted with amelina, dissolved Cu concentration (mg/L) was maximum and significantly higher in the leachates from columns amended with 20% compost, followed by 10%, 5%, 2.5% compost, and control (Figure 2A). Concentration of Cu in the leachates from columns planted with amelina in the first two months was small but increased subsequently and became highest in the third month and decreased thereafter. The highest Cu concentration in leachate was 115 mg/L  
Figure 1: Total Cu (mg/kg) in roots (**A**), stem (**B**), leaves (**C**), pods (**D**), and seeds (**E**) of amelina and field penhycress as affected by different rates of compost during the study period. Data are reported as mean \(\pm\) S.D (\(n=3\)). Means are compared by Tukey Kramer-HSD test (\(\alpha=0.05\)). The treatment levels that are not connected by the same letter are significantly different from each other.  
in columns planted with camelina, amended at 20% compost, in the third month (Figure 2A). In field pennycress columns, dissolved Cu concentration (mg/L) followed a similar trend, with highest leaching in columns containing 20% compost, followed by those of 10%, 5%, 2.5% compost, and control (Figure 2B). Furthermore, Cu concentration in leachates from columns of field pennycress in the first two months was quite small but increased subsequently and became highest in the third month and decreased thereafter. The highest Cu concentration in leachate was 66 mg/L from columns amended with 20% compost, planted with field pennycress in the third month (Figure 2B). Cu concentration (mg/kg) in soil in all the columns (control, camelina and field pennycress; Figure 2 C,D) did not differ significantly during the study period.
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 6 Evaluation Setup  
In this section, we describe the benchmark dataset of the mechanical bearings and the existing Bayesian approach that we used to compare our proposed method.
## 3 Hardware Architecture of the Proposed STFT Processor
### Hardware Architecture of the Proposed STFT Processor  
Figure 7 shows the hardware architecture of the proposed STFT processor, which supports a 0/25/50/75% variable overlap ratio to minimize the loss of information depending on the type of window used. It also supports 16/64/256/1024-point variable window lengths to provide various time-frequency resolutions. The hardware architecture of the proposed STFT processor consists of a WM, four data mapping modules (DMM1, DMM2, DMM3, DMM4), a data reordering module (DRM), five radix-4 butterfly modules (R4 BM1, R4 BM2, R4 BM3, R4 BM4, R4 BM5). The principle of operation is as follows. The input single data stream is reconstructed according to the length of the predefined window by the WM and converted into four overlapped data streams. Moreover, the four data streams are multiplied by the predefined window and input to the next stage. In the case of 1024-point STFT, the four-channel data streams reconstructed by the WM are input to R4 BM1 by a multiplexer to perform the 1024-point STFT operation. In the case of 256-point STFT, the four-channel data streams are input to R4 BM2. In the case of 64-point STFT, the four-channel data streams are input to R4 BM3. In the 16-point STFT, four channels of data streams are input to R4 BM4.
## 4 Results and Discussion
### Datasets Description  
In this paper, three datasets were used, among which the cellular traffic dataset used the Milan City cellular traffic dataset provided by Telecom Italia. It was used as part of the \"Big Data Challenge\" [29]. The dataset collected cellular traffic data for Milan City from 1 November 2013 to 1 January 2014, at a 10 min interval, and collected approximately 19 GB (62 days, 300 million records) of data. In this dataset, the city of Milan is divided into a grid-covered layer of 100 \(\times\) 100 squares, each of which is approximately 235 \(\times\) 235 m in size, which we called a cell. In each cell, three types of cellular traffic data are included, namely Internet, Call, and SMS. The dataset used in this paper was Internet.  
In addition, this paper also used two geographic datasets of Milan City. The dataset on BS information came from OpenCellID [33], which contains multiple types of information about BS, such as location (latitude and longitude), mobile country code, and the coverage of each BS. Using the geographical location information of each cell, we can map the location of each BS to the cell where the BS is located by simple preprocessing. The dataset was not only used in cellular traffic prediction, but also played a key role in the subsequent base station sleep control experiments.  
The POI information came from Google Places API [34]. POI refers to the point data in the Internet electronic map, which basically includes four attributes: name, address, coordinate, and category. In the dataset used in this paper, 13 kinds of POIs were collected, including subway stations, shops, restaurants, and parks.  
The SA of a region reflects the overall user demand degree for network services. The dataset on social activity level was obtained through Dandelion API [35]. The obtaineddata contained the information a user generated when using Twitter, such as the location and keywords.
# Intelligent Air Cutting Monitoring System for Milling Process
## 6 Conclusions  
In this paper, an intelligent air cutting monitoring system was established. The spindle load current, vibration signal, and information from controller were collected in real time, then combined with the algorithm, which is used to determine whether the cutting is effective or non-effective (air cutting). The currently executed NC program is taken to assist judgment, so the system can more accurately judge the occurrence of cutting and record each time point to calculate the processing information of each section, including processing time, cutting time and air cutting time. Additionally, the system will automatically export a txt file containing the processing information of each section for user reference. An intelligent air cutting monitoring system, with a friendly human machine interface, was built using C# language program. The experiment results showed that both judgment from the spindle load current and vibration signal methods can accurately monitor the air cutting. The monitoring system judged by the spindle load current can judge the machining status faster and more accurately, and there will be no misjudgment caused by the moment when the worktable starts or stops. However, when the cutting load is too small, so there is no change of spindle load current in the controller, the monitoring system judged by the spindle load current cannot correctly judge the machining state, while the monitoring system judged by the cutting vibration signal can still judge the machining state. Moreover, the results of verification experiments showed that the proposed system can accurately monitor the air cutting time, cutting time, machining time, and the error range is within 1 s. This air cutting information can be used as the basis for improving the machining efficiency and machine utilization rate. Compared to the other method, with the fusion of spindle load current and vibration signal, the developed air cutting monitoring system can accurately monitor the cutting/air cutting for different cutting parameters.  
Conceptualization, S.-M.W. and C.-Y.L.; methodology, S.-M.W. and C.-Y.L.; software, C.-Y.L.; validation, C.-Y.L. and H.G.; formal analysis, S.-M.W., C.-Y.L. and H.G.; investigation, S.-M.W. and C.-Y.L.; resources, S.-M.W. and C.-Y.L.; data curation, C.-Y.L., R.-Q.T. and H.G.; writing--original draft preparation, S.-M.W. and H.G.; writing--review and editing, S.-M.W. and H.G.; visualization, C.-Y.L., R.-Q.T. and H.G.; supervision, S.-M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, grant number MOST 110-2221-E-005-063 and MOST 110-2218-E-005-018.  
Not applicable.  
The authors declare no conflict of interest.
_Article_  
**Exhaust Gas Condensation during Engine Cold Start**  
**and Application of the Dry-Wet Correction Factor**  
**[PERSON] *, [PERSON] and [PERSON]**  
European Commission--Joint Research Centre, Directorate for Energy, Transport and Climate,  
Sustainable Transport Unit, 21027 Ispra, Italy; [EMAIL_ADDRESS] (A.A.Z.);  
[EMAIL_ADDRESS] (M.C.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +39-033-278-5312  
Received: 10 May 2019; Accepted: 29 May 2019; Published: 31 May 2019  
**Featured Application: Dry-wet correction factors for diesel, gasoline, and CNG vehicles.**  
**Abstract: Gas components, like carbon monoxide (CO) and dioxide (CO\({}_{2}\)), can be measured on a wet- or dry-basis depending on whether the water is left or removed from the sample before analysis. The dry concentrations of gaseous components in the exhaust from internal combustion engines are converted to wet concentrations with conversion factors based on the combustion products and the fuel properties. Recent CO\({}_{2}\) measurements with portable emissions measurement systems (PEMS) compared to laboratory grade equipment showed differences during the first minutes after engine start. In this study we compared instruments measuring on a dry- and wet-basis using different measuring principles (non-dispersive infrared detection (NDIR) and Fourier-transform infrared spectroscopy (FTIR)) at the exhaust of gasoline, compressed natural gas (CNG), and diesel light-duty and L-category vehicles. The results showed an underestimation of the CO\({}_{2}\) and CO mass emissions up to 13% at cold start when the conversion factor is applied and not direct \"wet\" measurements are taken, raising concerns about reported CO\({}_{2}\) and CO cold start emissions in some cases. The underestimation was negligible (<1%) for CO\({}_{2}\) when the whole test (20-30 min) was considered, but not for CO (1%-10% underestimation) because the majority of emissions takes place at cold start. Exhaust gas temperature, H\({}_{2}\)O measurements and different expressions of the dry-wet corrections confirmed that the differences are due to condensation at the exhaust pipes and aftertreatment devices when the surface temperatures are lower than the dew point of the exhaust gases. The results of this study help to interpret differences when comparing instruments with different principles of operation at the same location, instruments sampling at different locations, or the same instrument measuring different driving test cycles or at different ambient temperatures (e.g., \(-7\,^{\circ}\)C).**  
**Keywords: portable emissions measurement system (PEMS); worldwide harmonized light-duty vehicles test cycle (WLTC); real driving emissions (RDE); Fourier-transform infrared spectroscopy (FTIR); non-dispersive infrared detection (NDIR); gas analyzers; measurement uncertainty; engine cold start emissions; exhaust gas condensation; tailpipe CO\({}_{2}\) measurements**