esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
###### Abstract  
One of the main effects of climate change is rising sea levels, which presents challenges due to its geographically heterogenous nature. Often, contradictory results arise from examining different sources of measurement and time spans. This study addresses these issues by analysing both long-term (1995-2022) and decadal (2000-2009 and 2010-2019) sea-level trends in the Baltic Sea. Two independent sources of data, which consist of 13 tide gauge (TG) stations and multi-mission along-track satellite altimetry (SA), are utilized to calculate sea-level trends using the ordinary least-squares method. Given that the Baltic Sea is influenced by geographically varying vertical land motion (VLM), both relative sea level (RSL) and absolute sea level (ASL) trends were examined for the long-term assessment. The results for the long-term ASL show estimates for TG and SA to be 3.3 mm/yr and 3.9 mm/yr, respectively, indicating agreement between sources. Additionally, the comparison of long-term RSL ranges from \(-2\) to 4.5 mm/yr, while ASL varies between 2 and 5.4 mm/yr, as expected due to the VLM. Spatial variation in long-term ASL trends is observed, with higher rates in the northern and eastern regions. Decadal sea-level trends show higher rates, particularly the decade 2000-2009. Comparison with other available sea-level datasets (gridded models) yields comparable results. Therefore, this study evaluates the ability of SA as a reliable source for determining regional sea-level trends in comparison with TG data.  
ab +
Footnote †: journal: Remote Sens.  
**2024**, 16, 760. [[https://doi.org/10.3390/rs16050760](https://doi.org/10.3390/rs16050760)]([https://doi.org/10.3390/rs16050760](https://doi.org/10.3390/rs16050760))
## 3 Results and Discussion
### Microstructural Properties of R. typhina Fruits Using SEM Analysis
#### 3.2.1 UAE Applied to Dried R. typhina Fruits  
A total of 15 experiments were run using UAE, according to different tested parameters, solvent concentrations [40%, 55%, 70%], solvent/solid ratios [10/1, 15/1, 20/1, \(v/w\)], and ultrasonic extraction times [10 min, 20 min, 30 min]. For details regarding the experimental conditions and significance of the experimental run code, please refer to Table 2.  
The total contents of the investigated bioactive compounds (TAC, TPC, TTC, TFC, and carotenoids) from different UAE extracts are shown in Table 4.  
Regarding the ethanol concentration, in most experimental runs, lower concentrations, in particular 55% aqueous ethanol, produced higher amounts of compounds. Much lower concentrations of 40% were effective in association with a higher extraction time. Regarding the influence of solvent/solid ratio (\(v/w\)) on the content of the studied compounds, anthocyanins were better extracted at 10/1 (\(v/w\)), while the other studied phenolic compounds were better extracted at higher ratios, meaning more diluted mixtures were subjected to UAE. Regarding the UAE time, tannins, phenolics, flavonoids, and carotenoids were extracted in higher amounts at longer times, while high amounts of anthocyanins were recovered within 10 min. Kruskal-Wallis test and [PERSON]'s test showed significant/marginal-significant differences in mean values for some of the analyzed compounds in relation to the solvent concentration (TAC, \(p\) = 0.0063, and TFC, \(p\) = 0.0812), extraction time (TTC, \(p\) = 0.0313), and solvent/solid ratio (TPC, TFC and carotenoids, \(p\) = 0.0024, 0.0366, and 0.0126, respectively). Figure 3 illustrates the Boxplot representations of the contents of the investigated compounds in relation to the tested UAE parameters, with only the significant results being represented. The TAC mean value was significantly (\(p\) = 0.0032 *) higher when 55% ethanol was used compared to when 70% ethanol was used, and marginally significantly (\(p\) = 0.0849) lower for 40% compared to 55% ethanol solutions. The average content of flavonoids was significantly higher for 55% ethanol compared to 70% ethanol (\(p\) = 0.0440) and for the 20/1 \(v/w\) ratio compared to the 10/1 \(v/w\) ratio (\(p\) = 0.0171 *). Although a higher ethanol concentration is often associated with a better extraction yield, there are studies reporting that a too-concentrated ethanolic solution may decrease the quantity of extracted phenolics [67]. High extraction time has a great contribution to tannin recovery, as reported by other authors [68]. The results for carotenoids indicated better UAE when a higher solvent/solid ratio was used, similar to other reported studies [69].
## 3 Avocado's Composition in Nutrients and Bioactives
### General Composition/Chemical Composition
#### 3.1.1 Avocado Fruit Composition  
Avocado's nutritional composition has been proposed to provide several health benefits on consumers, and it consists mainly of a high lipid content and considerable amounts of proteins, dietary fiber, and minerals, while it is a fruit that owns a relatively low content in carbohydrates [32; 33]. C7 sugars, such as D-mannoheptulose and perseitol, were identified as the dominating sugars in specific avocado cultivars. These cultivars displayed higher quantities of C7 sugars rather than fructose and glucose and were associated with a sweet sensory attribute. Current studies suggest that C7 sugars play a significant role in carbohydrate storage and translocation in avocados [34]. Moreover, the most used part of the fruit, the pulp, has a low content of carbohydrates as shown in Table 1 (4.77-8.89 g/100 g), while avocado by-products have the highest content of carbohydrates (67.5 g/100 g).  
Avocados are known to possess an extremely low glycemic index and load because of their low carbohydrate content. Such characteristics are associated with enhanced metabolic health, especially in terms of glycemic response. Numerous studies that have been conducted that show that avocado consumption improves weight management and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nutrients \({}^{*}\)** & **Avocado Fruit \({}^{1}\)** & **Avocado By-Products \({}^{1}\)** \\ \hline Ash & 0.57–10.80 & 1.6 \\ Carbohydrates & 4.77–8.89 & 67.5 \\ Lipids & 51.83–72.34 & 1.41–4.12 \\ Protein & 4.80–8.61 & 3.4 \\ Total dietary fiber & 6.03–7.24 & 21.6 \\ Total insoluble fiber & - & 18.7 \\ Vitamins & 0.02–0.04 & 1.3–6 \\ References & [25; 26; 27; 28; 29; 30] & [26; 31] \\ \hline \hline \end{tabular} \({}^{*}\) Data are a combination of the ones presented in [25; 26; 27; 28; 29; 30; 31]. \({}^{1}\) Estimation of typical gross composition expressed as grams of total compound per 100 g of avocado weight (g 100 g\({}^{-1}\))  
\end{table}
Table 1: Proximate nutritional composition of avocado’s fruit and its by-products.  
metabolic health. Furthermore, the observed correlation between avocado intake and diabetes prevention in various studies could be ascribable to a plethora of possible causes, but mainly due to low levels of glucose and sucrose. In addition, avocado's principal sugar known as D-mannoheptulose, which is a unique seven-carbon sugar, that could assist blood glucose regulation, could lead us to fully elucidate this fruit's association to diabetes management and/or diminishment [35]. However, studies emphasize the significance of balancing lipids, sugars, and carbohydrates for avocados' sensory flavor profile, a factor that should be taken into serious consideration beforehand [36].
# Pigment Dyeing of Atmospheric Pressure Plasma-Treated Cotton Fabric
###### Abstract  
In this work, the effect of atmospheric pressure plasma treatment on improving the application of pigment dyeing in cotton was investigated. The colour-related properties such as (i) colour yield; (ii) colour levelness; (iii) crocking fastness; and (iv) dry-cleaning fastness of the pigment-dyed cotton fabric were determined and evaluated. Atmospheric pressure plasma under different combinations of operational parameters was used for treating 100% ready-for-dyeing cotton fabric. The atmospheric pressure plasma-treated cotton fabric was dyed with yellow pigment by the pad-dyeing method, and the pigment-dyeing solution concentrations were 1 g/L and 5 g/L. Experimental results revealed that colour yield of the yellow pigment-dyed cotton sample improved. The crocking and dry-cleaning fastness properties also improved. In addition, prediction model equations were developed for 1 g/L and 5 g/L pigment dyeing based on multiple linear regression, and the measured colour yield was close to the predicted colour yield.  
pigment; atmospheric pressure plasma; cotton; colour properties; textile application Article
# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## 1 Introduction
While POS can draw more statistics that help in disambiguation, morph features obtain better data representation and linguistic knowledge that improve generalization ability. The best combination and alternative paths models based on these features can consistently overcome data sparseness and reduce OOV rates caused by limited training data. Several advantages of these linguistic features can enrich the training pipeline by proper coherent sentences, and thus improve the translation quality and accuracy. While neural machine translation (NMT) shows state-of-the-art performance on the plentiful training data, PBSMT still competitive or superior in the case of the low resource we focus on [15]. Furthermore, PBSMT and NMT achieve comparable performance on Arabic-English, and Arabic-Hebrew MT systems [16; 17]. The rest of the paper is organized as follows. Section 2 reviews previous works on Arabic-Chinese MT and factored translation systems. Section 3 presents the challenges in building MT between Arabic and Chinese and the primary methods used in our work, such as preprocessing techniques and input linguistic features. Section 4 discusses the factored MT approach. Section 5 describes the data preparation and experiment setting for the phrase-based and factored models. Section 6 explores the evaluation results in terms of BLEU scores and OOV rates, which exhibit considerable improvements by using linguistic features. Section 7 concludes this work and provides suggestions for future work.
## 4 Results and Discussion
### Comparison of the Proposed Work with the Existing Ka-Band RWG-to-MSL Transitions  
A parametric comparison of the performance results of the existing Ka-band MSL-toRWG transitions with the proposed Ka-band optimized design is shown in Table 6.
## 4 Experimental Validation
### Empirical Positioning Algorithm  
Figure 5 shows the single-frame data, including the amplitude and phase diagrams, where the horizontal axis denotes the sampling period, according to each antenna collection point, and the vertical axis represents the sampling sequence number in terms of the antenna number and order of calculating. Thus, for the single-channel data, although the amplitude of fundamental was not affected, the phase diagram was obviously affected by the presence of excess phase. The phase stability had a great influence on the accuracy and stability of subsequent positioning.  
Figure 6 shows the amplitude and phase diagrams of the single-frame data after the compensation, where it can be seen that after the comparison, the redundant phase caused by the frequency deviation was complemented, and the redundant phase was corrected well.  
To verify the effect of frequency estimation on the positioning results, the positioning results at the point (0, 0) with the compensation frequencies \(\overline{f_{0}}\) and \(\overline{f_{0}}\pm 2k\) are statistically analyzed in this paper. The estimated frequency at the point (0, 0) is 254 kHz. Table 1 shows the positioning results. The statistical results show that a 2 kHz frequency estimation deviation can cause a 0.3 m positioning deviation. Therefore, the accuracy of the compensated frequency estimation is extremely important.  
Figure 5: Amplitude and phase of the single–frame data. (**a**) Amplitude. (**b**) Phase.  
Figure 6: Amplitude and phase of the single—frame data after compensation. (**a**) Amplitude after compensation. (**b**) Phase after compensation.  
Figure 8: The static test location results for the source location of (2, 0). (**a**) Average error in the \(x\)-direction. (**b**) Average error in the \(z\)-direction. (**c**) Location distribution. (**d**) CDF.  
Figure 7: The static test location results for the source location of (0, \(-1\)). (**a**) Average error in the \(x\)-direction. (**b**) Average error in the \(z\)-direction. (**c**) Location distribution. (**d**) CDF.
## 2 Materials and Methods
### Intervention Study Design
#### 2.2.2 Anthropometric Measurements  
Body mass index (BMI) was calculated by dividing weight (kg) by standing height squared (m\({}^{2}\)). Participants were classified as underweight, normal weight, overweight and obese according to the BMI criteria for the adult population (WHO, 2021) [41]. More specifically, participants with a BMI \(<\) 18.5 were classified as underweight, normal weight with BMI between 18.5-24.9, overweight ranged between 25.0-29.9, and obese classified with BMI \(>\) 30. Body composition analysis was performed by assessing body fat (kg), muscle mass (kg), body water (kg) and bone mass (kg) with a segmental body composition analyzer (TANITA SC300), according to the body composition procedures manual. Height was measured with a stadiometer (seca 222) with an accuracy of \(\pm\)0.5 cm. Waist and hip ratio were measured with a measuring tape (seca). Waist-to-hip ratio (WHR) was estimated by dividing waist circumference to hip circumference (WHR = Waist Circumference/Hip Circumference). It is generally used as an indicator of body fat distribution and as a predictor of metabolic health risks including insulin resistance and impaired lipid profile [42].  
Bone density was assessed by Dual Energy X-ray Absorption (DXA) (HOLOG EXPLORER Dexa scanner/HOLOG Discovery Dexa scanner, Hologic Inc., Marlborough, MA, USA). DXA scans included whole-body BMD T-scores and were performed at baseline and at the end of the study. The precision of DXA measurements can vary depending on factors such as the specific body region being measured, the DXA device used, and the population being studied. It is therefore certain that DXA measurements can be affectedwhen data are collected from different sites, as here. Not having cross-calibration, due to challenges posed by COVID-19 restrictions, is considered a limitation. However, taking proactive steps to address and mitigate the impact on the present study's validity enhances the credibility of our findings. Strategies to ensure quality control across the different sites included the development of standard operating procedures (SOPs), and the confirmation that all operators were adequately trained and certified in DXA measurements. In addition, harmonization techniques including standardized questionnaires and measurement procedures were applied.
## 2 Literature Review
### Ensemble Learning Approaches  
In the case of ensemble learning models, [PERSON] and [PERSON] [21] examined the effectiveness of several machine learning techniques, including support vector machines, decision trees, and random forests. They contrasted the distinct methods with an ensemble method that brought these models together. The results showed that the ensemble method outperformed the individual algorithms in terms of prediction accuracy, sensitivity, and specificity. The study also emphasized the importance of feature selection in raising the model's accuracy.  
[PERSON] et al. [22] have innovated a smart healthcare monitoring system designed to integrate multiple clinical data sources for accurate heart disease prediction. This system employs a combination of deep learning models, outperforming traditional methods in terms of accuracy. A standout feature of this system is its real-time patient data monitoring capability, facilitating timely intervention and heart disease prevention. By incorporating ECG readings, blood pressure, body temperature, and other pertinent clinical factors, the system provides precise cardiac illness prognosis.  
[PERSON] [23] delved into the realm of coronary heart disease early detection, with a specific focus on harnessing the potential of ensemble methods. They pinpointed the most effective approach for early disease detection by rigorously analyzing a spectrum of models and algorithms, including DT, RF, SVM, KNN, and artificial neural networks (ANNs). The results underscore the superiority of ensemble approaches, which seamlessly integrate multiple algorithms, yielding the highest accuracy in disease prediction. This research highlights the significance of ensemble techniques in enhancing early detection capabilities for coronary heart disease.  
[PERSON] et al. [24] introduced an approach known as swarm intelligence ensemble deep transfer learning (SI-EDTL), designed for the task of multiple vehicle detection in images captured by unmanned aerial vehicles (UAVs). This method combines the power of swarm intelligence algorithms and deep transfer learning to enhance the accuracy of vehicle detection in UAV imagery. The research demonstrated the effectiveness of SI-EDTL, offering a solution for the challenging task of detecting multiple vehicles in aerial images, which has significant applications in fields such as surveillance and autonomous navigation.  
[PERSON] et al. [25] have presented a novel approach for accurately measuring arm volume in patients with lymphedema. This method utilized a three-stage ensemble deep learning framework empowered by swarm intelligence techniques. By combining the power of deep learning and swarm intelligence, the research aimed to enhance the precision of arm volume measurement, which is crucial in the diagnosis and management of lymphedema. The proposed model demonstrated promising results, showcasing its potential to improve healthcare outcomes for individuals with lymphedema by providing more accurate and reliable measurements of arm volume.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 3 Testing Methodology
### DRAM Retention Test  
A DRAM retention test is performed by increasing the time to refresh the DRAM cells and observing the errors induced due to cell capacitor leakage. In our experiment, retention-based errors are induced by refreshing the rows with a much higher retention time than the standard retention period of 64 ms. This allows the cell capacitors to leak for an extended period of time, causing bit flips to a significant number of cells. The retention test involves the following steps:  
1. A reference data pattern is written to a complete row. We use all zeros (0x00) and all ones (0 xFF) patterns in our experiments.
2. Auto-refreshing is disabled because we want the cells to leak charge for an extended retention period without being refreshed.
3. The rows are left idle for the specified retention time (10 s, 20 s, etc.). The cell capacitors gradually leak charge, causing bit flips in the cell data.
4. The data are read back from the rows after the retention period and sent back to the host machine. The read data are compared with the written data to detect the cells that could not hold the data for that duration as the bits are flipped.
5. The raw data are analyzed to observe the bit flips/retention errors of a DRAM module in the rows, banks, and their distributions.  
These steps are performed for all the rows in a DRAM bank and for all the banks in a module at time. To ensure that only the refresh or retention time affects the cell leakage in this test, we perform the read and write operations with the standard timing parameters. In our experiment, we perform the retention test for three different retention times: at \(t_{ret}\) = 10 s, 20 s, 50 s.
## 1 Introduction  
Industry 4.0 (I4) is understood to be a revolutionary method of production. It brings a new perspective on the synergy of production with modern technologies, the maximum possible output, and the minimum resources used (German Standardization Roadmap Industrie 4.0--Version 3, b.r.). The new industrial revolution is changing the rules of competition, as the business models are completely changing as a result of the digitization of factories and the adoption of the Internet of Things concept [1]. The modern Industry 4.0 concept was originally designed as a national initiative for the development of the German economy in 2011.  
Initially, Industry 4.0 technology was only available to large enterprises, due to high capital requirements [2]. However, in many countries, the small- and medium-sized enterprises (SMEs) represent more than 90% of all the enterprises [3]. Even such enterprises are beginning to introduce modern technologies into production gradually [4]. For this reason, it is necessary to map their situation and the degree of implementation of Industry 4.0 so that the enterprises are able to compare each other and get a closer overview of their position on the market. In relation, it is also necessary to better evaluate future steps in implementing Industry 4.0, so that they do not face unnecessary implementation difficulties, such as, by Ingaldi [5] and Turkes [6], the narrow product portfolio of SMEs, which does not guarantee full use of the efficiency of automated and autonomous production systems, the cost of obtaining money, the turbulence of the environment from the micro and macro aspect, lack of knowledge about Industry 4.0, more focus on operation at the costs of developing the company, lack of understanding of the strategic importance of industry 4.0, too few human resources, need for continued education of employees, and lack of standards.  
The paper is structured as follows: Section 1 is the introduction; Section 2 offers the theoretical background with a current review of the literature; Section 3 covers the materials and methods, describing the main objectives, methods (cluster analysis), and hypotheses; Section 4 contains the results of the analysis and includes SMEs categorization, comparison with large enterprises, and validation of results; Section 5 offers a discussion focused on the main contrubutions and limitations of the research; and Section 6 is the conclusion, which contains the main results and further research challenges.
# Development of an Automatic Low-Cost Air Quality Control System: A Radon Application
## 2 Materials and Methods
### The Prediction Algorithm
```
INPUT current radon concentration rl, current time reading t, interpolation time window INT_WD OUTPUT air flow system control command ('on' or 'off') or no command (NULL) Step 1 Set ALPHA = 0.85; (exponential moving average factor) MV_AVG_TL = 3600; (moving average time limit, 1 hour by default) RADON_LIMIT = 300; (radon limit in Bq/m\({}^{3}\)) current_slope = 0; moving_average = 0; Step 2 Recover previous data from storage: prev_t (time of the previous reading) prev_rl; (previous radon concentration) prev_ma; (previous moving average) Step 3 If previous data l= NULL and (t - prev_t) < MV_AVG_TL slope = (rl - prev_rl)/(t - prev_t) ma = ALPHA * (slope) + (1 - ALPHA * prev_ma Step 4 Save current data to storage: t rl; ma; Step 5 Set currently_over_limit = rl >= RADON_LIMIT; future_over_limit = (rl + ma*INT_WD) >= RADON_LIMIT Step 5 If currently_over_limit or future_value_over_limit then Set air_flow_command = 'on'; Else Set air_flow_command = 'off'; Step 6 Recover air flow control status from storage: air_flow_status; Step 7 If air_flow_status == NULL or air_flow_status!= air_flow_command Then OUTPUT air_flow_command Else  OUTPUT NULL
```  
**Algorithm 1**Air quality prediction algorithm using radon concentration as the air quality measure