esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Nitrogen and Phosphorus Removal
## 4 Discussion
### Bioreactor Performance: East Bioreactor (EB) Versus West bioreactor (WB)  
Superior nutrient removal performance was observed from the WB compared to EB for both concentration reductions and mass load removals (Figures 4-6 and Table 3). As designed, the bioreactors were intended to be replicates of one another; however, observations during the study period made it clear that as-built features resulted in unequal hydraulic loading. Notably, greater runoff volumes flowed through WB compared to EB for 14 out of the 15 storms analyzed for nutrient mass loads, and on average WB received 28% more runoff during each event than EB. Since the WB outflow sampling structure had larger volumes of water passing through it compared to the EB outflow, a potential explanation is that increased water mounting occurred in the WB than EB, before drainage could occur. This additional mounting would have resulted in a greater volume of the top woodchip layers fluctuating between saturated and unsaturated conditions.  
The fluctuating water level in the WB is comparable to woodchip bioreactor studies on drying-rewetting cycles (DRW), where a bioreactor is drained and air is allowed to fill the once saturated spaces between the woodchips, overall increasing nitrate removal [44; 45]. These conditions in soil systems are known to enhance decomposition of organic matter, making C and N available, which can accelerate rates of soil metabolic processes [46; 47; 48]. Results from a DRW event was first reported in a woodchip bioreactor column study from [PERSON] [49]. Researchers saw increased nitrate removal following a 96-hr drying time and the capacity for dissolved P removal in the woodchips also increased. It is thought that DRW cycles cause decomposition of the woodchips to occur in the presence of oxygen, which increases dissolved organic C (DOC) and can then be used by microbes in denitrification. In this study where more woodchips in WB are saturated during storm flow and then subsequently dry out, the DRW may explain a difference in performance, leading to more DOC available for rapid denitrification. For future studies, modification of the outlet structures from each bed could be used to control outflow and retention time to investigate whether more water mounting in the WB does indeed occur. While woodchips have been shown to be an effective C source for \(\text{NO}_{3}{}^{-}\) removal for at least 15 years of continuous bioreactor operation [50], decomposition of woodchips is a concern especially if DRW cycles may enhance this process.
###### Abstract  
Dynamic Mode Decomposition (DMD) is a data-driven method to analyze the dynamics, first applied to fluid dynamics. It extracts modes and their corresponding eigenvalues, where the modes are spatial fields that identify coherent structures in the flow and the eigenvalues describe the temporal growth/decay rates and oscillation frequencies for each mode. The recently introduced compressed sensing DMD (csDMD) reduces computation times and also has the ability to deal with sub-sampled datasets. In this paper, we present a similar technique based on discrete cosine transform to reconstruct the fully-sampled dataset (as opposed to DMD modes as in csDMD) from sub-sampled noisy and gappy data using \(l_{1}\) minimization. The proposed method was benchmarked against csDMD in terms of denoising and gap-filling using three datasets. The first was the 2-D time-resolved plot of a double gyre oscillator which has about nine oscillatory modes. The second dataset was derived from a Duffing oscillator. This dataset has several modes associated with complex eigenvalues which makes them oscillatory. The third dataset was taken from the 2-D simulation of a wake behind a cylinder at Re = 100 and was used for investigating the effect of changing various parameters on reconstruction error. The Duffing and 2-D wake datasets were tested in presence of noise and rectangular gaps. While the performance for the double-gyre dataset is comparable to csDMD, the proposed method performs substantially better (lower reconstruction error) for the dataset derived from the Duffing equation and also, the 2-D wake dataset according to the defined reconstruction error metrics.  
dynamic mode decomposition; gappy data reconstruction; denoising; compressed sensing +
Footnote †: journal: Applied Mechanics and Physics Letters
## 2 Spacecraft Attitude Dynamics, Determination and Control
### Attitude Determination and Control Architecture
When the eclipse flag is active, the proportional term of the control in Equation (9) is set equal to zero, and the control is only proportional to the angular velocity to maintain the inertial position of the spacecraft until the Sun is back in view. A wrong eclipse condition would not set the spacecraft in danger since, after a predefined eclipse windows time, the system would receive a warning and a complete Sun searching rotation would be performed. The ADCS shall be also equipped with angular rate sensors, such as gyroscopes, or magnetometers with angular rate estimation methods [31]. This is necessary to have an estimation of the angular velocity of the spacecraft, \(\omega\), which is needed to compute the derivative action of the control in Equation (9). The control section processes the Sun angle, the angular rates, and the eclipse flag to obtain the control torque capable to minimize \(\alpha_{S}\). The control algorithm assesses the variation of \(\alpha_{S}\) and modulates the proportional term of the control with the modulation vector, \(\hat{\mathbf{m}}\). The modulation vector selects the body axis around which the rotation shall be performed to minimize \(\alpha_{S}\) as:  
\[\hat{\mathbf{m}  
in front of the primary Solar arrays, the pulsed modulation is maintained with the last commanded direction to perform even a full rotation until the Sun is in view of the Solar panels. In this case, to account for possible eclipses not detected, the Sun searching rotation would be performed up to two times around two different axes. If the Sun is not yet in view, the system is posed in eclipse mode with a warning, until the Sun is visible again or the maximum eclipse time has passed. The specific algorithms implementation shall consider the noise in the measurements and the disturbances affecting the dynamics. Hence, the Sun angle variation is detected over a finite interval of time, to be synchronized with the pulse frequency, to have an integral overview of the dynamics.
# Post-Filtering of Noisy Images Compressed by HEIF
###### Abstract  
Modern imaging systems produce a great volume of image data. In many practical situations, it is necessary to compress them for faster transferring or more efficient storage. Then, a compression has to be applied. If images are noisy, lossless compression is almost useless, and lossy compression is characterized by a specific noise filtering effect that depends on the image, noise, and coder properties. Here, we considered a modern HEIF coder applied to grayscale (component) images of different complexity corrupted by additive white Gaussian noise. It has recently been shown that an optimal operation point (OOP) might exist in this case. Note that the OOP is a value of quality factor where the compressed image quality (according to a used quality metric) is the closest to the corresponding noise-free image. The lossy compression of noisy images leads to both noise reduction and distortions introduced into the information component, thus, a compromise should be found between the compressed image quality and compression ratio attained. The OOP is one possible compromise, if it exists, for a given noisy image. However, it has also recently been demonstrated that the compressed image quality can be significantly improved if post-filtering is applied under the condition that the quality factor is slightly larger than the one corresponding to the OOP. Therefore, we considered the efficiency of post-filtering where a block-matching 3-dimensional (BM3D) filter was applied. It was shown that the positive effect of such post-filtering could reach a few dB in terms of the PSNR and PSNR-HVS-M metrics. The largest benefits took place for simple structure images and a high intensity of noise. It was also demonstrated that the filter parameters have to be adapted to the properties of residual noise that become more non-Gaussian if the compression ratio increases. Practical recommendations on the use of compression parameters and post-filtering are given.  
image lossy compression; optimal operation point; HEIF; additive white Gaussian noise; post-filtering 2025  
152939
## 3 Results
### Results of FDA Patient Classification, Based on Demographic and Anthropometric Parameters  
In our research, a two-step approximation has been applied. First the possibility of two-group categorization (non-OSA vs. OSA), then the applicability of basic demographic and anthropometric parameters was tested for a more detailed classification. Results of OSA prediction by age, gender and BMI using FDA are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Real OSA Categorization**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-6}  & & **Non-OSA** & **Mild OSA** & **Severe-** & \\  & & **(n = 36)** & **(n = 32)** & **Severe OSA** & \\  & & & **(n = 32)** & \\ \hline \multirow{3}{*}{Total score of the Berlin questionnaire (points)} & \textless{6 point} & 15 (42\%) & 18 (56\%) & 14 (44\%) & 47 \\ \cline{2-6}  & 6–8 point & 14 (39\%) & 7 (22\%) & 7 (22\%) & 28 \\ \cline{1-1} \cline{2-6}  & \(\geq\)9 point & 7 (19\%) & 7 (22\%) & 11 (34\%) & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of Berlin questionnaire scores by OSA severity subgroups (the table shows the number of cases per 100 patients and the percentage distribution of scores within each OSA category). OSA: obstructive sleep apnoea.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Real OSA Categorization**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-6}  & & **Non-OSA** & **Mild OSA** & **Severe-** & \\  & & **(n = 36)** & **(n = 32)** & **Severe OSA** & \\  & & & **(n = 32)** & **Severe OSA** & \\ \hline \multirow{3}{*}{Total score of \(\textless{8 point}\)} & \textless{8 point} & 28 (77\%) & 13 (40\%) & 3 (9\%) & 44 \\ \cline{2-6}  & 8–10 point & 4 (11\%) & 6 (19\%) & 6 (19\%) & 16 \\ \cline{1-1} \cline{2-6}  & 11–14 point & 4 (12\%) & 9 (28\%) & 14 (44\%) & 27 \\ \cline{1-1} \cline{2-6}  & \(>\)14 point & 0 & 4 (13\%) & 9 (28\%) & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contingency table of frequency distribution of patients, categorized by OSA severity according to AHI values and OSA probability, according to total score of the Epworth questionnaire (the table shows the number of cases per 100 patients and the percentage distribution of scores within each OSA category). OSA: obstructive sleep apnoea.  
A combined prediction by age, gender and BMI, resulted in an 81% success rate for categorizing patients into OSA or non-OSA groups. Non-OSA categorization was successful 29 times out of 36 (80.55%), while OSA categorization yielded a true positive 52 times out of 64 (81.25%).  
AI Supported Three Category OSA Prediction Based on Basic Anthropometric Parameters Grouping Patients into Mild, Moderately Severe-Severe OSA and Non-OSA Groups  
The results of AI-supported OSA prediction for severity categories by age, gender and BMI using AI are presented in Table 5.  
When OSA was categorized by severity according to BMI, gender and age, 64% of the results were correct. Prediction of the non-OSA category was correct in 75%, mild OSA in 56.25% and moderately severe-severe OSA in 59.37%.
# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
###### Abstract  
In a densely distributed mobile crowdsourcing system, data collected by neighboring participants often exhibit strong spatial correlations. By exploiting this property, one may employ a portion of the users as active participants and set the other users as idling ones without compromising the quality of sensing or the connectivity of the network. In this work, two participant selection questions are considered: (a) how to recruit an optimal number of users as active participants to guarantee that the overall sensing data integrity is kept above a preset threshold; and (b) how to recruit an optimal number of participants with some inaccurate data so that the fairness of selection and resource conservation can be achieved while maintaining sufficient sensing data integrity. For question (a), we propose a novel task-centric approach to explicitly exploit data correlation among participants. This subset selection problem is regarded as a constrained optimization problem and we propose an efficient polynomial time algorithm to solve it. For question (b), we formulate this set partitioning problem as a constrained min-max optimization problem. A solution using an improved version of the polynomial time algorithm is proposed based on (a). We validate these algorithms using a publicly available Intel-Berkeley lab sensing dataset and satisfactory performance is achieved.  
mobile crowd sensing; task-centric; participant selection; data integrity; data prediction Article
## 3 Theoretical Background and Methodology
### Reliability (Dynamic) Theory  
Reliability is the probability that a system will operate for the entire duration of a mission, T\({}_{\text{m}}\), in the mode for which it was designed, given that it was working at the beginning of the mission. As known, the mathematic formulation of this concept is based on the definition of a random variable \(\theta\) that measures the probability of an item to survive from time 0 up to time T:  
\[\text{R}(\text{T})\ =\ \text{P}\cdot(\theta\ >\ \text{T}) \tag{6}\]  
When a small-time interval is considered, \(\zeta_{2}-\zeta_{1}=\varepsilon\), the probability that the system fails is called failure rate \(h(t)\)[37], and the following relationships hold [38]:  
\[h(t)=-\frac{1}{\text{R}(t)}\ \frac{d}{dt}\text{R}(t) \tag{7}\]  
which brings to the general equation of the system reliability the following:  
\[\text{R}(t)=e^{-\int_{0}^{t}h(t)\ dt} \tag{8}\]  
Dynamic reliability studies those problems for which the failure rate is dynamic, which varies according to the physical conditions in which the system operates. The concept of failure rate is involved in most resolution methods of reliability engineering such that, in many industrial applications, it is assumed to analyse a system during its useful-time period, assuming three main hypotheses, which simplify the resolution of a problem:  
* **Hp3:** During the useful-time period, there is an approximately constant failure rate with respect to time, where only random failures can occur.
* **Hp4:** The behaviour of a component can be defined by means of a dyadic operational state (on/off).  
* **Hp5:** The component works under well-defined operative conditions, similar to those observed by the original manufacturer.  
With these assumptions, the failure rate is constant and independent from time and other external factors. Therefore, Mean Time To Failure (MTTF), generally provided by the component manufacturer [39], is given, and Equation (8) can be written as follows:  
\[\mathrm{R}(t)=e^{-\frac{t}{MTTF}} \tag{9}\]  
Repairable systems represent the majority of industrial applications. In these cases, a stochastic model is characterized by another parameter, the system downtime, referred to as the total time required to repair it, during which the system is not available or operating [37]. A stochastic model of repairable systems is often following the next hypothesis:  
* **Hp6:** Downtime can be modelled with a random variable characterized by its own expected value.  
In this case, it is possible to model the repair distribution with the negative exponential distribution, characterized by Mean-Time-To-Repair that models the average time it takes to recover from the failure [37].  
For a repairable system, it is possible to calculate the percentage of time that the component is operational, the Availability (A), and this represents the probability that a system is available at a given point of time (point availability) [37; 38; 40]:  
\[\mathrm{A}=\frac{average\;up\;time}{average\;up\;time+average\;down\;time}= \frac{MTTF}{MTTF+MTTR} \tag{10}\]
## 5 PET Bottle Degradation Management Strategies
### Improving the Efficiency of Waste Management
#### 5.2.4 Monitoring and Removing Microplastics in Wastewater Treatment Plants  
Microplastics enter wastewater systems in various ways, and one significant source of their presence is the recycling of PET bottles and other plastics. Recycling has a positive impact on waste management, but this process also contributes to the emission of MP pollutants [143]. Wastewater treatment plants play a crucial role in retaining microplastics, serving as the last barrier before they enter the environment [144, 145]. Filter-based technologies, such as membrane bioreactors (MBRs) and rapid sand filtration (RSF), are highly effective in removing microplastics, achieving efficiencies of 99.99% and 97%, respectively [146]. Nevertheless, some microplastics remain in sewage sludge, which can become a secondary source of soil and groundwater contamination, posing a threat to ecosystems and public health [143, 147, 148].  
One promising solution to this problem is the application of enzymatic technologies that enable the biodegradation of PET microplastic particles. Particularly effective in this process are _Comamonas testosteronei_ bacteria. These organisms can significantly enhance the efficiency of wastewater treatment and reduce the amount of particles accumulating in sludge [149, 150].  
In addition to implementing new treatment technologies, it is equally important to monitor microplastics in wastewater and sludge. Spectroscopic methods are most often used for this purpose because they accurately identify and analyze microplastics in environmental samples [147, 151, 152]. Methods for detecting microplastics are described in detail in Section 4.  
Due to the growing threat posed by the presence of microplastics in waters discharged into the environment, the European Union adopted Directive 2024/3019 [153], which obliges member states to implement more effective technologies for treating and monitoring microplastics in both wastewater and sewage sludge. According to this directive, wastewater treatment plants should implement additional treatment stages, known as fourth-stage treatment, aimed at removing microplastics from wastewater before it is released into the environment. Compliance with these regulations is crucial for environmental protection and public health.
## 6 Conclusions  
This study developed a comprehensive method that integrates absolute and relative GPS positioning methods, a stable regional reference frame, a seasonal ground deformation model, and an automated CPD algorithm to monitor and detect riverbed deformation in near real-time and the long-term. The method is able to detect abrupt positional-changes as minor as five millimeters in near real-time and gradual positional-changes at a couple of millimeters per day within a week. The critical components of this method are:  
1. Establishing a short-baseline GNSS network with one reference station and a group of rover stations fixed on monitoring objects;
2. Monitoring the long-term stability of the base station using PPP, a stable regional or local reference frame, and a seasonal ground deformation model;
3. Monitoring the movement of the rover antennas using the DD method in near real-time;
4. Applying an automated CPD algorithm to aid in detecting minor abrupt-displacements and slow gradual-displacements at both base and rover sites in near real-time.  
This investigation indicates that BeiDou observations can also provide few-millimeter accuracy for structural deformational monitoring. Parallel processing BeiDou and GPS datasets will improve the reliability of near real-time monitoring and minimize false alerts. The method introduced in this article can be implemented for near real-time and long-term monitoring of critical structures, such as dams, large-span bridges, and high-rise buildings. It is expected that this study will promote the applications of high-accuracy GPS and BeiDou techniques in SHM.  
[PERSON] managed the GPS network and conducted data analysis. [PERSON] and [PERSON] prepared the original draft. All authors participated in GPS network installation and operation, and edited, reviewed, and improved the manuscript.  
This research was supported by the National Science Foundation of China (No. 51829801), the China Railway 17 th Bureau Group Co., Ltd., the Key Laboratory of Urban Security and Disaster Engineering at the Beijing University of Technology, and the Key Laboratory of Soft Soil Engineering Character and Engineering Environment of Tianjin at the Tianjin Chengjian University.  
The authors appreciate graduate students [PERSON] and [PERSON] at the Beijing University of Technology and technicians at the China Railway 17 th Bureau Group Co., Ltd. for their assistance in installing and operating the GPS network.  
The authors declare no conflict of interest.
###### Abstract
For models such as SI and FR, each class has the same weight. For continuous factors such as altitude, this leads to sudden changes in the weights at the boundary of different classes, and similar factor values have completely different weights. In the same class, different factor values have the same weight, which is unreasonable [9; 32; 33]. Model quality is directly related to the accuracy of its evaluation, but the selection of influencing factors also affects landslide susceptibility evaluation results [34]. At present, popular factor screening methods include the information gain ratio [35], variance inflation factors [36], recursive feature elimination (RFE) [37], rough set [38], principal component analysis [39], Pearson correlation coefficient [40], and Spearman correlation coefficient [41]. In addition, the GeoDetector method proposed by [PERSON] et al., (2010) effectively uses spatial information of data to identify the primary factors affecting a certain phenomenon [42; 43]. This has been innovatively applied to landslide susceptibility analysis [44; 45]. This study aims to develop a hybrid optimization method for the SI model. This method optimizes the SI weight through GPR, which can avoid the irrationality of the bivariate statistical model mentioned above and improve the accuracy of landslide susceptibility assessment. In addition, the integration of GeoDetector and RFE is used to further optimize landslide influencing factors used for modeling. The area along Duwen highway in Sichuan Province, China, was used as the study area. A landslide inventory was created, and the overall performance of the SI model, SI model with optimized factors (GD-SI), and SI model with optimized factors and weights (GD-GPR-SI) were compared and analyzed.