esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Neonatal Sepsis Early Diagnosis System for Very Low Birth Weight Infants
## 3 Materials And Methods
### Data Collection
HeRO duet can acquire and analyse the electrocardiogram signal in order to compute the heart rate variability based on the R-R interval duration. A complex model based on the observations of the heart rate variability is then used to generate the HeRO index, which represents a factor associated with the increased risk of sepsis [11]. HeRO was developed as an alert system for sepsis in preterm infants in intensive care. Researchers from the University of Virginia, in fact, observing patients and noting abnormal heart rate characteristics (HRCs) before and at the clinical moment of the pathology, devised an algorithm that is based on the dependence of HRCs on variables such as the standard deviation of R-R intervals, sample asymmetry and sample entropy. Sample asymmetry refers to the relative rate of heart rate acceleration and deceleration. The entropy of the sample, instead, measures the irregularity of the sample, which can be interpreted as signalling for systemic inflammatory response syndrome (SIRS). Calculating the following information, therefore, through the final algorithm, which is based on the logistic regression adapted for repeated measures, the result is obtained, as well as the index of the increased  
Figure 1: Studied cohort. factor for the risk of sepsis in the following 24 h [20]. Precisely, based on this scientific evidence, the HeRO duet device is capable of generating a new score every hour taking into account the variation of the patient's cardiac variability in the last 12 h. The HeRO index computation procedure is described within the HeRO duet manual. The risk of contracting sepsis is classified as:  
* low if the score is lower than 1;
* intermediate if the score ranges between 1 and 2;
* high if the score is higher than 2. It is worth mentioning that patients featuring a high HeRO score are six times more likely to contract sepsis than patients with a low HeRO score value [21]. For each monitored patient, the device's display (an example view is shown in Figure 2) shows both the current score and the trend of the previous scores computed in the last five days. One HeRO duet device can simultaneously monitor up to two patients. A list of the information that is shown on the screen follows:  
* bed identifier;
* trend of the signal over time;
* graph indicating the current score;
* current score value;
* alarm symbol that appears only in correspondence with a high score;
* status indicator, which turns green in the case of the detection of the R-R intervals, grey in the opposite case.
## 3 Challenges in Monitoring Traffic Conditions in Real-time Using Loop Detector Data
### Monitoring Traffic Conditions
The speed at each state can be estimated by calculating the slope of the straight line connecting the origin and each of the traffic states. Figure 2f shows the relationship between the estimated travel times based on loop detector [18] located 2.5 miles upstream of the RSRB toll plaza and travel times reported by INRIX. As expected, the single sourced data do not adequately estimate the segment travel time. Therefore, using data from a single station is not appropriate in evaluating the effect of changes in toll plaza capacity and segment travel time. With the advent of new GPS (global positioning system) devices, collecting segment travel times has become simple, and such information is readily available from a vendor such as INRIX [19]. Figure 2: (**a**) Fundamental diagram remained within the free flow state. (**b**) Fundamental diagram entering the unstable flow state, monitoring the tail of the queue and determining affected links. (**c**) Hypothetical situation in which the queue started from the toll plaza but not reaching \(D\). (**d**) Hypothetical situation in which the queue started from the toll plaza and reaching \(D\). (**e**) Estimated speed within the queue. (**f**) Relationship between estimated travel times and observed travel times. However, monitoring changes in travel times alone will not provide the incremental capacity required at the toll plaza. Comparing the segment travel times and discharge rates also will not enable the agency to detect the onset of changes in traffic conditions leading to a marked increase in travel times: The travel time over an extended segment would not reveal the traffic becoming dense so long as the traffic remain freely flowing [17]. To this end, we developed a proxy measure to monitor changes in traffic conditions over extended freeway segments. This proxy measure can be used to determine the incremental capacity needed in response to changes in traffic conditions in real-time, as explained in the following section.
## 4 Results and Discussion
### Balanced and Imbalanced Data Stream with Incremental Drift
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Datasets** & **NoCD** & **DDM** & **EDDM** & **CUSUM** & **HDDM\_A** & **HDDM\_W** & **GMA** & **PH** & **RDDM** & **STEP** & **EWMAC** \\ \hline B-S & 1 & 4 & 6 & 5 & 7 & 10 & 1 & 8 & 3 & 9 & 11 \\ I-S & 3 & 2 & 7 & 8 & 1 & 9 & 3 & 6 & 5 & 10 & 11 \\ B-G & 8 & 4 & 1 & 6 & 7 & 10 & 8 & 3 & 5 & 11 & 1 \\ I-G & 7 & 1 & 3 & 6 & 4 & 9 & 7 & 2 & 5 & 10 & 11 \\ B-I & 5 & 7 & 2 & 4 & 8 & 9 & 5 & 1 & 3 & 10 & 11 \\ I-I & 7 & 1 & 4 & 6 & 2 & 10 & 7 & 3 & 5 & 9 & 11 \\ \hline
**Sum of ranks** & **31** & **19** & **23** & **35** & **29** & **57** & **31** & **23** & **26** & **59** & **56** \\ \hline
**Average ranks** & **5.17** & **3.17** & **3.83** & **5.83** & **4.83** & **9.5** & **5.17** & **3.83** & **4.33** & **9.83** & **9.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall ranking of HT classifier with different drift detection methods. Figure 12: Comparison of drift detection methods against each other with the Bonferroni–Dunn test.
## 4 Conclusions  
In conclusion, this study demonstrated the production of AgNPs employing cell biomass of _L. plantarum_ TA4. The _L. plantarum_ TA4 was found to have silver metal tolerance ability at the concentration of 2 mM and simultaneously produced AgNPs. The AgNPs were spherical, with an average particle size of \(14.0\pm 4.7\) nm with monodispersed NPs. Based on the SEM-EDX analysis, the formation of AgNPs was found distributed on the cell membrane, indicating the involvement of biomolecules and functional groups present on the cell surface to initiate the biosynthesis of AgNPs. Moreover, the FTIR analysis detected the presence of hydroxyl, carboxyl, and protein on the surface of AgNPs, which confirmed the involvement of these functional groups in the reduction and stabilization process. Furthermore, biosynthesized AgNPs exhibited considerable antibacterial activity against Gram-negative and Gram-positive pathogens in a concentration-dependent manner, indicating the potential application as an antibacterial agent. Moreover, the in vitro antioxidant activity of AgNPs demonstrated moderate radical scavenging activity on DPPH. The findings in our study highlight the sustainable and low-cost approach in the development of AgNPs, which can be the best alternative to the conventional method.  
Conceptualization, H.M.Y. and N.A.R.; data curation, H.M.Y.; formal analysis, H.M.Y., N.A.R., R.M., and U.H.Z.; funding acquisition, N.A.R.; investigation, H.M.Y.; methodology, H.M.Y.; project administration, N.A.R.; resources, N.A.R.; software, H.M.Y.; supervision, N.A.R., R.M., and U.H.Z.; validation, N.A.R. and R.M.; visualization, H.M.Y.; writing--original draft, H.M.Y. and N.A.R.; and writing--review and editing, N.A.R., R.M., and U.H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank Universiti Putra Malaysia for the financial support given.  
The authors declare no conflict of interest.
# Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion
## 2 Methods
### Joint Optimization Target Fuction  
Two conventional measurements are used in previous studies [21; 22; 28; 29], Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC), which are computed as Equations (4) and (5). RMSE is used to describe the deviation between predicted sequences and targets, and the PCC mainly shows the similarity of overall trend.  
Mark \(y^{\prime}(i)\) as the \(i\)-th predicted articulatory trajectories and \(\vec{y}\) and \(\vec{y^{\prime}}\) as the average of the sequence \(y\) and \(y^{\prime}\).  
\[RMSE(y^{\prime},y)=\sqrt{\frac{1}{n}\sum_{i=1}^{N}(y^{\prime}(i)^{2}-y(i)^{2})} \tag{4}\]  
\[PCC(y^{\prime},y)=\frac{\sum_{i=1}^{N}(y^{\prime}(i)-\vec{y}^{\prime})(y(i)- \vec{y})}{\sqrt{\sum_{i=1}^{N}(y^{\prime}(i)-\vec{y}^{\prime})^{2}\sum_{i=1}^{ N}(y(i)-\vec{y})^{2}}} \tag{5}\]  
Figure 3: The visualization of the TCN-based AAI model.  
Figure 2: A simple implementation of the TCN residual block using in the experiment. k represents the kernel size and d represents the dilation base.  
Note that the two measurements are conventionally used separately when judging the model performance. The regression model, however, is trained with the frequently-used RMSE loss function. That leads to a mismatch of targets in the evaluation and training stage that it will care more to ease the definite error but not the sequence trend. In order to eliminate the mismatch during the training stage and optimize the performance from the two aspects aforementioned, we combine these two measurements as the model target function \(\mathcal{L}(x)\) as Equation (6) for model error back-propagation:  
\[\mathcal{L}(x)=\lambda RMSE(f_{INV}(x),y)-(1-\lambda)P\text{CC}(f_{INV}(x),y) \tag{6}\]  
The \(\lambda\in[0,1]\) is the weight parameter adjusting the ratio of the two parts target function. And \(f_{INV}\) is the inversion model function. \(x\) is the input acoustic feature. The value of the weight parameter \(\lambda\) can be set in the model initialization process. Both RMSE and PCC have been appropriately scaled to ensure that the two parts of loss are under the same measure.
## 3 Results and Discussion
### Comparison of the Proposed Method with Other Studies  
The proposed SI-HLIME sample preparation protocol was compared with other novel extraction approaches used for the monitoring of PIR and MEL. Table 4 summarizes the results of the comparative study.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Analyte** & **Sample** & **Sample** & **Analytical** & **RR\%** & **RSD\%** & **LOD** & **Ref.** \\ \hline PIR & Horse urine & SDME & CEC-DAD & 94.8 & \textless{}4.32 & 0.01764 & [34] \\ \hline MEL & Human plasma & IL-UA-ISFME & HPLC-UV & 82.1–93.6 & \textless{}5.1 & 0.0001 & [35] \\ \hline PIR & Water samples & HF-SLPME & HPLC-DAD & 70.02–110.98 & \textless{}4.27 & 0.00458 & [36] \\ \hline MEL & Human serum & Cloud-point extraction & HPLC-UV & \textgreater{}92 & 6.9 & 0.01 & [37] \\ \hline PIR & Pharmaceuticals, formulations, human urine & DLLME & Spectrophotometry & 97–110 & 2.83 & 0.058 & [2] \\ \hline PIR & Human urine & SI-HLIME & HPLC-DAD & 94.3–103.4 & \textless{}4.4 & 0.02–0.03 & This study \\ \hline \hline \end{tabular}  
* SDME: single drop microextraction, IL-UA-ISFME: ionic liquid-based ultrasound-assisted in situ solvent formation microextraction, HF-SLPME: hollow fiber solid/liquid microextraction, DLLME: dispersive liquid–liquid microextraction, SI-HLIME: salt-induced homogeneous liquid–liquid microextraction. 2 CEC-DAD: capillary electrochromators-diode array detection, HPLC-DAD: high–performance liquid chromatography–diode array detection, HPLC-UV: high–performance liquid chromatography–ultraviolet detection.  
\end{table}
Table 4: Comparison of the SI-HLIME method with other microextraction approaches.  
Figure 5: Accuracy profiles for the determination of (**A**) MEL and (**B**) PIR in human urine samples using unweighted linear regression models. The relative error (%) is represented by the red plain lines, the accuracy profiles are represented by the blue dashed lines and the acceptance limits \(\lambda\) (\(\pm\)15%) are represented by the blank dotted lines.  
As can be observed, the proposed method exhibited comparable LOD values to References [34; 37], lower LOD values compared to Reference [2] but higher LOD values compared to References [35; 36]. Moreover, the proposed protocol exhibited good accuracy. The relative recovery values of this study were better than those of References [35; 36] and comparable to the values of the other studies. The RSD% values of this study can also be considered satisfactory in comparison with those obtained in the other studies. Apart from the satisfactory figures of merit, the proposed protocol is characterized by the inherent benefits of the SI-HLIME including the simplicity of the process, the rapid analyte extraction and the possibility to perform parallel handling of a high number of samples resulting in increased sample throughput. These advantages are of utmost importance for routine laboratories working in the field of bioanalysis.
## 3 Gaussian-Process-Based MPC
### Constraints of AUV Operation  
Building upon the prior establishment of the AUV's motion dynamics, which serves as the foundation for comprehending its behavior, it becomes imperative to account for the constraints that shape its operation. These constraints can be broadly categorized into two main components: constraints pertaining to the AUV's posture and control inputs, and constraints imposed by external obstacles. The constraints associated with the AUV's state vector, denoted as \(\mathbf{X}_{p}(k)\) and its input vector \(\mathbf{U}(k)\), can be expressed as follows:  
\[\mathbf{X}_{min}\leq\mathbf{X}_{p}(k)\leq\mathbf{X}_{max}\mathbf{U}_{min}\leq\mathbf{U}(k)\leq \mathbf{U}_{max} \tag{43}\]  
where \(\mathbf{U}_{min}\) and \(\mathbf{X}_{min}\) denote the predefined lower bounds, while \(\mathbf{U}_{max}\) and \(\mathbf{X}_{max}\) represent the predefined upper bounds. Obstacles are typically represented as ellipses, and to ensure collision avoidance, the relevant constraints must be adhered to during the computation process, as expressed in Equation (44). Furthermore, in scenarios involving dynamic obstacles, the centers of these obstacles will change over time.  
\[\left(\frac{x(k)-x_{obs}(k)}{l}\right)^{2}+\left(\frac{y(k)-y_{obs}(k)}{h} \right)^{2}\geq 1 \tag{44}\]  
Since the state vector \(\mathbf{X}_{p}(k)\) can be expressed as a function of the input vector \(\mathbf{U}(k)\), to simplify constraint handling in subsequent simulations, the aforementioned constraints are reformulated as follows:  
\[\begin{array}{c}\mathbf{B}(k)\mathbf{U}(k)\leq\mathbf{X}_{max}-\mathbf{A}(k)\mathbf{x}(k|k)\\ -\mathbf{B}(k)\mathbf{U}(k)\leq-\mathbf{X}_{min}+\mathbf{A}(k)\mathbf{x}(k|k)\\ \mathbf{U}(k)\leq\mathbf{U}_{max}\\ -\mathbf{U}(k)\leq-\mathbf{U}_{min}\\ 1-\left(\frac{x(k)-x_{obs}(k)}{l}\right)^{2}-\left(\frac{y(k)-y_{obs}(k)}{h} \right)^{2}\leq 0\end{array} \tag{45}\]While providing the status information of all obstacles, the AUV can effectively select appropriate action sequences for optimal obstacle avoidance. However, the extensive processing of such detailed information can impose significant computational demands, often rendering it impractical. To address this challenge, a common approach is to focus on local planning by observing the obstacle nearest to the AUV. In the context of this paper's obstacle avoidance simulations, considering the underactuated nature of the AUV, two parameters, angle and distance (\(angle\) and \(dir\)), are utilized to determine which obstacle to observe based on the AUV's attitude. Specifically, the AUV selects the obstacle with an angle falling within the \((-angle,\ angle)\) range from its forward direction and the one that is closest in terms of proximity. Furthermore, when the distance between an obstacle and the AUV is less than \(dir\), the AUV will prioritize avoiding that specific obstacle.
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 4 Discussion
### AloT as Shifting the Paradigm in Aviation from Health Monitoring to Health Management
* AloT enables a cradle-to-grave approach to aircraft health management. From manufacturing defects to end-of-life decisions, the system can provide insights and recommendations throughout an aircraft's entire lifecycle. This comprehensive view allows for better long-term planning and resource allocation. * AloT facilitates better integration of aircraft health management with the broader aviation ecosystem. This includes airports, air traffic control, manufacturers, and regulators. For instance, real-time health data could be used to inform air traffic management decisions or to provide manufacturers with valuable insights for future designs. * Just as healthcare is moving towards personalized medicine, AloT enables a move towards personalized aircraft management. Each aircraft can be managed based on its unique history, usage patterns, and current condition, rather than applying one-size-fits-all approaches. The shift from health monitoring to health management represents a fundamental change in how we approach aircraft maintenance and operations. AloT is the key enabler of this paradigm shift, providing the data collection, analysis, and decision-making capabilities necessary for truly proactive and holistic aircraft health management. It transforms aircraft from passive machines that are periodically checked and repaired into active, intelligent systems that continuously communicate their status and needs. As this technology continues to evolve and mature, we can expect to see even more sophisticated approaches to aircraft health management. The future of aviation will likely be characterized by self-aware aircraft that can predict their own maintenance needs, optimize their own performance, and seamlessly integrate with a broader, intelligent aviation ecosystem. The shift from monitoring to management is not just a technological upgrade; it is a fundamental reimagining of our relationship with aircraft.
# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 1 Introduction  
The connection between medicine and computer science started in the early 1960s, when scientists developed a digital computer-based monitoring system [1] that laid the foundations of the current standard in intensive care units across the world. Since then, the relationship between clinical practice and computing advances has been constant for the last half century [2]. Computer capabilities have been evolving at an exponential rate thanks to Moore's Law and significant improvements in processor design, and now the recent developments in artificial intelligence (AI) and deep learning (DL) methods are starting to fulfill some of the promises of computer science pioneers [3].  
Software systems in medicine have taken advantage of the increasing capabilities of modern computers to provide real-time solutions to intricate challenges. Complex medical imaging problems, such as computer-assisted ultrasonography, are now being tackled with AI with great results by exploiting the collaborative work of physicians and engineers, the capabilities of modern computing hardware [4, 5], and the access to digital medical images.  
Nevertheless, the introduction of deep learning-based strategies into clinical practice has just started, and much work lies ahead. Many leading AI experts agree that there is a huge gap between proof-of-concept research papers and actually deploying a machinelearning (ML) system in the real world [6], especially in clinical devices that have to pass regulatory processes. In fact, there is undoubtedly huge potential for ML to transform healthcare, but going 'from code to clinic' is the hardest part. The list of challenges to overcome in this field [6] includes the difficulties in obtaining datasets with sufficiently large, curated, high-quality data; the lack of robust clinical validations; the need for interdisciplinary collaborations of computational scientists, signal processing engineers, physics experts, and medical experts [7]; and some technical limitations of AI algorithms.
## 4 Conclusions  
This research tested the adsorption properties of seven bleaching earths, differing in chemical composition, during a low-temperature bleaching process of hemp oils. The selected BEs were applied in two variants of 2.5% and 5% m/m, and the process was carried out on oils pressed through CP and HP from three varieties of hempseed (Finola, Earlina 8 FC, and Secuieni Jubileu). Color was measured calorimetrically using the CIE Lab method, and natural dyes were determined using a spectrophotometer.  
After verifying the hypothesis based on the results obtained and relating them to the literature data, the following conclusions were made:  
1. Two doses of selected adsorbents were used during bleaching, and it was found that, in most cases, the use of more BE is associated with an increased reduction in dyes. This is not a favorable phenomenon for carotenoids, which are desirable compounds that have a beneficial effect on human health.
2. Taking into account hemp varieties and pressing temperature, it was noted that for Finola CP and Earlina 8 FC CP oils, the adsorbent with the most favorable adsorption ratio of chlorophylls to carotenoids was modified BEs 2.5%, especially kerolite-hydrated magnesium silicate. They remove sufficient chlorophylls while causing slight changes in carotenoid content. When bleaching HP oils from these varieties, using unmodified BEs 2.5% based on magnesium bentonite is recommended.
3. Oil obtained from the Secuieni Jubileu CP and HP hemp variety should be bleached with unmodified magnesium bentonite at 2.5%. Unmodified attapuligite clay is not recommended for this variety, as it strongly adsorbs carotenoids from the oil.
4. Tests conducted showed that an increase in the L* parameter (i.e., brightness) is caused by BEs based on modified magnesium bentonite and kerolite-hydrated magnesium silicate, although they do not show major changes in oil color (i.e., a* and b*), especially at doses below 5%.  
In conclusion, selecting the right bleaching earth and its quantity is key in determining the most suitable bleaching process. It is important to consider the chemical composition of the adsorbent and whether it is modified so that bleaching can be carried out in the most favorable way.  
**Author Contributions:** Conceptualization: [PERSON], [PERSON] and K.C.; methodology: [PERSON] and W.G.; software: D.M., E.N. and K.C.; validation: E.N. and K.C.; formal analysis: D.M. and E.N.; investigation: D.M. and K.C.; resources: D.M. and W.G.; data curation: E.N.; writing--original draft preparation: D.M., E.N. and K.C.; writing--review and editing: [PERSON] and [PERSON]; visualization: D.M., E.N. and K.C.; supervision: [PERSON] and [PERSON]; project administration: D.M.; funding acquisition: W.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.