esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sites and Application
#### 2.3.1 Commercial Mitigation Mussel Farm  
The concept of the mitigation mussel farm is based on mass balance principles of inorganic nutrients which are available in the marine environment and are transformed into phytoplankton, which is then immobilized by the filter feeding activity of suspended mussels [35]. Subsequently, when the mussels are harvested, the nutrients stored within the mussels are removed from the marine environment [13]. Harvested mussels can be used as a high-quality protein source and can be sold commercially. Since the mitigation cultivation strategy differs from the cultivation for human consumption, mussel products need to be sold for other purposes (e.g., mussel meal or fertilizer).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Division** & **Ecosystem Service Classes** & **Description** \\ \hline \multirow{6}{*}{Provisioning} & Wild plant outputs & Harvest and number of species (e.g., algae) for consumption \\  & Wild animal output & Harvest and number of species (e.g., fish) for consumption \\  & Aquaculture & Total and key market species landings (e.g., fish seafood) \\  & Materials for processing and agriculture & Harvested amount and value (fodder, fiber, seaweed, and fertilizer) \\  & Physical and bioenergy & Amount of abiotic and biotic energy resources (wind or wave energy, solar panels, and methane production) \\  & Navigation and waterways & Shipping (international ferries and cruisers) and water-based local public transport \\  & Harbors and maritime industries & Areas with sea access \\ \hline \multirow{6}{*}{Regulation Maintenance} & Burial of nutrients and organic matter & Sediment accumulation rate \\  & Nutrient removal & N-denitrification \\  & Primary productivity & Nutrient and ChLa concentration \\  & Water transparency & Secchi depth \\  & Matter transformation & Water residence time \\  & Oxygen provision & (Bottom) oxygen concentration (above 6 mg/L) \\  & Pest control & Potentially harmful algae blooms and number of alien species \\  & Nursery grounds & Size and number of nursery areas \\  & Habitat biodiversity & Size and number of habitats \\  & Mass stabilization & Extension of macrophyte habitats \\  & Flood protection & Buffering capacity for high water levels, wave attenuation, and current reduction \\ \hline \multirow{6}{*}{Cultural Attractiveness for seaside housing Experimental use Scientific and educational and educational and exhibition Economic development Number of jobs and amount of total income} \\ \end{tabular}
\end{table}
Table 3: Ecosystem service classes and the associated descriptions which were chosen for the ecosystem service assessment.
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 4 Results and Analysis
### Related Work  
Several recent deep learning-based studies have addressed ECG arrhythmia classification using the SPH dataset.  
* [PERSON] et al. (2021) [19] proposed a hybrid CNN-LSTM architecture and reported an average F1-score of 94.2% on a multi-label classification task involving 11 rhythm classes.
* [PERSON] et al. (2024) [20] introduced a residual attention-based convolutional network that achieved over 96% accuracy and 95% F1-score across six arrhythmias.
* [PERSON] et al. (2023) [21] developed a temporal CNN with dilated convolutions, reaching 92.4% accuracy and 90.6% F1-score.
* [PERSON] et al. (2020) [22] employed a bidirectional LSTM model and reported F1-scores above 94%, though the method required considerable computational time and resources.  
Compared to these deep learning approaches, our best-performing models--particularly UMAP-KNN--achieved more modest F1-scores, ranging from 63% to 95% depending on the rhythm class. As illustrated in Figure 5, UMAP-KNN outperformed all logistic regression variants in challenging and imbalanced classes such as SR, AFIB, and ST, despite its conceptual simplicity.  
Figure 4: Confusion matrix of UMAP-KNN on SB.  
While deep learning models remain superior in terms of raw accuracy and generalization across classes, they come with significant limitations: high training costs, limited interpretability, and reduced feasibility for real-time deployment. In contrast, our approach offers a favorable trade-off between explainability, computational efficiency, and classification performance, especially in clinical decision support systems where transparency is essential.
## 4 Results
### Evaluation of Streamflow Simulations
#### 4.2.2 Monthly Streamflow  
The calculated daily streamflow time series using the five different precipitation inputs were aggregated to monthly time series, and the performance of the monthly streamflow simulations was evaluated. Table S2 shows that the monthly streamflow simulations were generally more accurate than the daily simulations in terms of CC and NSE, although both simulations had the same magnitudes of BIAS. Figure S1 shows that when the original IMERG and 3B42V7 data sets were used, the simulated monthly streamflow in August 2014 and July-October 2015 was considerably underestimated. However, in simulations using the corrected IMERG and 3B42V7 products, the calculated monthly hydrographs agreed well with the observed data during the entire simulation period, except for May and June 2015 when the corrected IMERG-based model run significantly overestimated the monthly streamflow. In general, the bias-corrected satellite precipitation products were feasible in historical monthly streamflow simulations in the Chindwin River and yielded comparable skill scores as the model run with the gauge-based precipitation data set. Meanwhile, simulations with the corrected 3B42V7 product generated more accurate simulated monthly streamflow than those using the corrected IMERG data set.
# Parametric Testing of Metasurface Stirrers for Metasurface Reverberation Chambers
## 2 Conclusions  
In this paper, we have analyzed the effects of introducing different types of metasurface stirrers in reverberation chambers, compared to the traditional metallic stirrer. Three metasurface stirrers have been studied: one with two unit cells arranged alternatively (#1), another one with two unit cells arranged in a chessboard-like manner (#2), and a third one with two unit cells in random arrangement (#3). From the correlation coefficient and TSCS results obtained, we can conclude that the field uniformity is somehow improved for the RC with metasurfaces stirrer #1. Since this stirrer is  
Figure 5: A comparison of the C ratio between the four stirrers.  
Figure 6: Standard deviation with respect to the mean value of maximum simulated obtained by each of the 12 steps, for a complete rotation of the stirrer.  
comprised of a metasurface with negligible volume (as compared to a metallic stirrer), the test volume of the RC is significantly extended. The volume extension is dependent on the dimensions of the mechanical stirrer.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived the idea and wrote the present manuscript. The simulations were carried out by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] All the authors participated in discussions relevant to the presented sensing system.  
**Funding:** This research was funded by [National Natural Science Foundation of China] grant number [61501227, 61071019], [Six talent peaks project in Jiangsu Province] grant number [GDZB-009], [Foundation of Key Laboratory of Radar Imaging and Microwave Photonics, NUAA, Ministry of Education] grant number [XCA17001-05], [Postdoctoral Science Foundation of China] grant number [2015M581789], [56 YAH17032 of NUAA], [Jiangsu Innovation Program for Graduate Education] grant number [KYLX16-0370], [Fundamental Research Funds for the Central Universities] grant number [NJ20160011], [MINECO-Spain] grant number [TEC2016-75650-R], [Generalitat de Catalunya] grant number [2017 SGR-1159] and FEDER funds, the [Foundation of Graduate Innovation Center in NUAA] grant numbers [kfij20170414, kfij20170411], and support from the Fundamental Research Funds for the Central Universities.  
**Acknowledgments:** [PERSON] acknowledges the China Scholarship Council (CSC) for Grant 201706830064. [PERSON] acknowledges the financial support from National Natural Science Foundation of China under Grant No.61871215.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 4 Experiment
### Experimental Datasets  
KITTI dataset: The KITTI dataset is a dataset based on real-world scenarios established by [PERSON] et al. [28] using their autonomous driving platform. It primarily consists of outdoor scenarios such as urban, rural, and highway. The visual odometry part includes 22 sequences such as 00-21, among which the sequences such as 00-10 have ground truth values, which can be used to analyze how effectively the SLAM algorithm in this work performed. The sequences 00, 02, 05-07, and 09 contain loops.  
TUM RGB-D dataset: The TUM RGB-D dataset [29] consists of indoor RGB-D sensor sequences, which are divided into several categories and can be used to evaluate SLAM methods in a variety of texture, illumination, and structural conditions. It is a benchmark for evaluating RGB-D SLAM.  
EuRoC dataset: 11 stereo sequences from a micro air vehicle (MAV) flying in two various rooms and a sizable industrial setting are included in the latest EuRoC dataset [30]. A binocular camera was used for sampling at a sampling frequency of 20 Hz.  
Overall, these three datasets are rich in content. Different acquisition equipment is used by them, including autonomous driving equipment, handheld equipment, and flight equipment. It covers a range of scenarios, such as different moving speeds, changingillumination, motion blur, etc. These datasets possess considerable authority in this area and are capable of thoroughly evaluating the algorithms suggested in this work, taking into account the numerous SLAM application situations. Figure 5 shows examples of images included in the three datasets.
## 4 Engineering Example Application
### External Factors Affecting Slope Displacement
#### 4.2.1 The Effect of Rainfall on Landslides  
From a mechanical perspective, the infiltration of rainwater into the slope interior has several effects on slope stability. Firstly, it increases the water content of the soil, thereby raising its capacitance. Additionally, rainwater infiltration generates hydrostatic pressure and dynamic water pressure inside the slope. These pressures exert outward thrust on the slope, leading to an increase in soil sliding force along the potential sliding surface and affecting slope stability.  
Prior to rainfall, the pores between the soil particles on the slope are primarily filled with air. During the initial stage of rainfall, raindrops, carrying kinetic energy, cause the dispersion of finer soil particles on the slope surface. As rainfall persists, the water content of the soil on the slope surface gradually increases, replacing the air-filled pores with rainwater and reducing the bonding force between soil particles. Furthermore, continuous rainfall impacts the slope surface, progressively disrupting the original structural state and causing the soil to loosen until it becomes thin mud.  
As rainfall continues, water accumulates on the slope surface, resulting in increased flow and runoff intensity. When water flow interacts with the slope, dispersion occurs due to differences in soil structures. Runoff on the slope is unevenly distributed, leading to gully erosion, especially in areas with weak erosion resistance. The energy and scouring capacity of the water flow intensifies, increasing its sediment-carrying force. If the soil's erosion resistance is lower than the scouring force of the runoff, erosion pits will form. With ongoing rainfall, the scouring capacity of the runoff strengthens, widening the erosion pits and cutting downward towards the foot of the slope, forming small gullies. Continuous rainfall replenishes the slope runoff, further enhancing the water flow's erosion capacity, which is primarily concentrated in the lower half of the slope. The continuous scouring effect of the water flow gradually undercuts the soil on both sides of the gully trench, eventually leading to local soil destabilization and collapse at the foot of the slope.
# Direct Temperature Measurements of Cardiac Stent during MRI Examinations
###### Abstract  
Nowadays, Magnetic Resonance Imaging (MRI) is considered the gold standard for imaging the brain, spinal cord, musculoskeletal system, head and neck, and complex congenital heart malformations; consequentially, the number of MRI scans in patients with implantable electronic devices has simultaneously increased. During the entire length of the MRI exam, patients are exposed to electromagnetic fields with different characteristics (static, low frequency, radiofrequency fields), which are related to different risks. The scarce available literature about MRI-induced heating on cardiac stents suggests that excessive temperature rise occurs only in unfavorable cases. Ideally, RF safety assessment could be performed during the anamnestic process, but this simulation process's results are too slow to be performed before patient MRI examination. In this context, we developed a dedicated measurement set-up by focusing our target on the measurement of the heating of a cardiac stent during an MRI examination. Results for the temperature rise trend along the entire stent length during a clinical MRI protocol are shown together with the local Specific Absorption Rate (SAR) values and cumulative equivalent minutes at 43 \({}^{\circ}\)C (CEM43\({}^{\circ}\)C), in order to ensure the safety of patients with MR-conditional devices, also with a view to not inappropriately preclude their access to MRI scans. The obtained results show that the maximum temperature rise (4.12 \({}^{\circ}\)C) is within the limit of 5 \({}^{\circ}\)C stated in the stent manual for 15 min of continued scanning with the specific conditions. The maximum temperature rise was in correspondence with the stent tips and calculated SAR confirms the fact that two hotspots are present near the tips of the stent. Finally, the calculated CEM43\({}^{\circ}\)C remained well below the proposed threshold for muscle tissue.  
MRI; SAR; cardiac stent; RF deposition; exposure assessment; RF heating +
Footnote †: journal: Applied Sciences
## 4 Findings
### RQ 2: Which Factors Influence ARKit's Motion Capture Results?  
We performed factor analysis using Welch ANOVA, _t_-test analysis, and logistic regression on the dependent variable _MAE_ to answer RQ2.  
The MAE depended on the performed exercise. This dependency is visible when inspecting the respective boxplots of the MAE (Figure 10). Especially both Squat exercises (Squat, Side Squat) show significantly higher mean values than the other exercises. This observation is supported by the post-hoc analysis results of the ANOVA results. The logistic regression indicated an additional small influence of whether upper or lower body angles are considered. While the _t_-test showed an additional effect on whether the pelvic's center was moved during an exercise, this effect was not visible in the logistic regression. The impact of this factor remains inconclusive.  
**Finding 2:** The factor analysis results show that the accuracy of ARKit's human motion capture mainly depends on the performed exercise.  
While there is a slight difference between the frontal and side view data for both the wMAE and the SRCC, this difference is comparably small. The results of the side view show a 1.44\({}^{\circ}\) difference of the wMAE and a difference in the SRCC of 0.07, with the side view performing slightly better than the frontal view. These findings are supported by the factor analysis results, where no dependency of the view was measured. It also needs to be considered that the upper body angles in the side view only contained data of three subjects due to export problems, limiting the comparison's explanatory power.  
Another aspect of the device's position influence is the visibility of specific body parts. Limited visibility of body joints, such as the left side of the body in the Front Lunge, Single Leg Deadlift, and Leg Extension Crunch, or the elbow joints in the Side Squat and Squat, is associated with a higher wMAE and worse correlation results, especially in the left elbow joint. Hidden joints often led to ARKit confusing the left and right body side for the respective joints, which caused unexpected peaks in the recorded data (Figure 11). The tracking of the upper body joints worked significantly better when other body parts did not hide them, as in the remaining three exercises Jumping Jacks, Lateral Arm Raise, and Reverse Fly.  
**Finding 3:** When positioning the device, ensuring good visibility of the targeted joints improves the accuracy of the results.  
Figure 9: Results of the baseline drift analysis of the ARKit data. This is computed by minimizing the MAE by shifting the ARKit data vertically. The results show a normal distribution around 0, thus indicating no systematic baseline drift of the ARKit results.
## 3 IA for Two-Unit Parallel System with Three States
### Numerical Simulation  
In this subsection, we will use two numerical examples to show the validity of our result and check whether there exist IA fluctuations for two-unit parallel system with three states.  
**Case 3**.: _We assume the fault, delay-repair and repair rates are as follows:_  
\[\lambda_{1}=1,\;\mu_{1}=1,\;\
u_{1}=1,\] \[\lambda_{2}=1,\;\mu_{2}=1,\;\
u_{2}=1.\]  
Then following the steps in Section 3.2, we can obtain the IA shown in the Figures 5 and 6:  
**Case 4**.: _We assume the fault, delay-repair and repair rates are as follows:_  
\[\lambda_{1}=0.1,\ \mu_{1}=0.1,\ \
u_{1}=1,\] \[\lambda_{2}=1,\ \mu_{2}=1,\ \
u_{2}=1.\]  
Then we can obtain the IA as follows:  
We can easily find from Figures 5 and 6 that there exists IA fluctuation in Case 3 but Case 4 does not. According to two cases, we can draw the conclusion that IA for the two-unit parallel system with three states has IA fluctuation under certain conditions.
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 5 Results
### Spatial Patterns of Leaf Clumping Effects on ET Estimation  
Compared to Case I, leaf clumping effects on global terrestrial ET simulation are assessed. The spatial patterns of bias errors for ET estimates among cases have consistent signs, either positive (Figure 6) or negative (Figure 7), indicating that the clumping effects are spatially consistent, despite considerable differences in other variables such as meteorology, plants, and soils. It means that leaf clumping effects are vital for the two cases. The largest biases due to neglecting leaf clumping exist in the tropical and the boreal forests (Figures 6 and 7) where high clumping is detected from the MODIS BRDF product (Figure 4). Figure 6 demonstrates that the ET values are overestimated globally if true LAI is used ignoring clumping. The reason is that overstory sunlit LAI is overestimated in Case II. Leaf clumping leads to more light penetration into the canopy and a decrease in overstory sunlit LAI. If ignoring clumping, overstory sunlit LAI would be positively biased while overstory shaded LAI would be negatively biased. Because ET of sunlit leaves is larger than that of shaded leaves per unit leaf area, the total ET is positively biased for Case II.  
Figure 7 demonstrates the converse effects of leaf clumping globally for Case III. It can be found that the detailed spatial distributions of the leaf clumping effects are relatively different in Figures 6 and 7. In Case II (Figure 6), the high clumping effects on ET do not appear where LAI is large. The reason is that the relative change in overstory shaded LAI with CI is larger at lower LAI values while the absolute value of overstory shaded LAI varies little with LAI (Equations (9) and (10)). In Case III (Figure 7), the high clumping effects appear in areas with high LAI. This is because the overstory shaded LAI would be more negatively biased with larger LAI values when the CI is the same.  
Figure 6: The spatial distribution of bias errors in global terrestrial ET estimates for Case II.
## 2 Centralized Treatment Plants
### Industrial Wastewater Remediation
#### 2.3.1 Mining Industry  
The development of an easy and successful procedure for the treatment and reuse of water from mining effluents is far from being achieved in all cases, as highlighted by [PERSON] et al. in their interesting review. They illustrated the problems connected with the presence of floatation reagents and reported the more promising treatments to remove them from reclaimed water, avoiding decline in separation efficiency or environmental contamination when reused or discharged, respectively [78].  
To generate water for reuse while recovering organic and inorganic compounds, membrane processes were applied to wastewater from the gold mining blasting stage. UF was coupled to RO treatment, and the investigation was carried out at a pilot plant placed at the mine site by using an SFP-2280 UF module with a filtration area of 77 m\({}^{2}\) and a BW30 FR 400/34i module with a filtration area of 37 m\({}^{2}\) for RO. Before filtration, wastewater was subjected to oxidation, coagulation, flocculation and sedimentation. The UF step ensures a steady operation of the RO system, which does not required cleaning during operation. The membrane retention was maintained above 90% thorough operation work with low transmembrane pressures. The permeate presented excellent quality, and it easily met the standards for cooling water and boiling solution after pH adjustment. Its reuse would reduce freshwater catchment in approximately 367,000 m\({}^{3}\)/year, also reducing the discharged volume [79].  
The application of NF alone can provide suitable water quality. NF was applied in a pilot-scale trial to reduce the ionic strength of saline bore water employed in various mining activities, and it was able to reject >95 % of divalent cations (Ca\({}^{2+}\) and Mg\({}^{2+}\)) and a portion of monovalent ions (>74% of Na\({}^{+}\) and Cl\({}^{-}\)) under the proper membrane selection and specific conditions. These rejection rates were found to be sufficient to produce water with concentrations under the threshold of 50 mg/L of calcium and magnesium and 500 mg/L of sodium, suitable to be reused in mineral processing without loss of selectivity of the reagents, no difficulties in froth flotation and tailing management operations [80].  
Strictly connected with mining activities is the formation of Acid Mine Drainage (AMD) from abandoned tailings, mainly at coal mines and pyrite areas, leading to serious pollution affecting the mine environment and the rivers and groundwater in the mine surroundings. A critical survey on the active and passive treatment techniques to remediate AMD can be found in the review paper by [PERSON] et al. [81].
## 4 Application Results  
### Patients and Clinicians' Registers  
Our application allows registering patients and clinicians. For patients, our system collects personal and relevant medical information used to identify each patient such as Health Insurance Card and clinical record numbers. In addition, personal and access information are collected for clinician register in order to provide or restrict access for some system's functionalities.