esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cider VOC Profiles Resulting from Different Yeast Strains
To quantitatively verify that these central species were at ideal concentrations in the finished cider products, we performed an external calibration [20], and the results are reported in Table 3. If ethyl acetate is present in excess, it can yield an undesirable solvent smell [13]. We detected this species below the perception threshold (<12 mg\(\cdot\)L\({}^{-1}\)) for cider fermented with Edinburgh and French Saison yeasts. Cider produced with Belgian II and Abbey yeasts generated ethyl acetate concentrationssimilar to those previously observed [11] but not high enough to have a deleterious effect on odor. Ethyl octanoate was the most abundant ethyl ester identified in every cider reported herein and is responsible for a fruity or candy fragrance [21], while 2-phenylethylly alcohol produces distinctive floral aromatic notes. Our results show that every cider, independent of yeast strain, contained ethyl octanoate and phenylethyl alcohols at concentrations above the perception threshold (>0.014 mg\(\cdot\)L\({}^{-1}\) and >0.390 mg\(\cdot\)L\({}^{-1}\), respectively) and within concentration ranges that match comparable, recent cider headspace studies [6,11]. Lastly, 3-methyl-1-butanol can contribute a desirable, whiskey aroma at concentrations below 300 mg\(\cdot\)L\({}^{-1}\)[13], but it can become harsh when present at higher quantity. All concentrations reported in Table 3 fall below 300 mg\(\cdot\)L\({}^{-1}\) and above the perception threshold (>30 mg\(\cdot\)L\({}^{-1}\)) for ciders fermented with Belgian II, French Saison, and Abbey yeasts, evidencing an enjoyable aroma.
## 3 Results
### Velocity Domain Results
No significant effect is visible in the GPS case using measurements retrieved from the Xiaomi device, because the error peaks occur when DOP values are high and measurement redundancy is low, making the application of RAIM-FDE ineffective. For the Glonass-only case with uBlox, several anomalous measurements have been identified and rejected, as demonstrated by the improvements of all the considered figures of merit, but some large errors remain in the solution owing to low redundancy. Both the Doppler and TDCP solutions are discontinuous because RAIM was not able to identify blunders in spite of the presence of measurement inconsistencies. Reliable solutions are almost completely absent for the Glonass and Galileo cases when the Xiaomi device is used; this is mainly due to the limited number of available measurements, as at least five measurements are needed to apply RAIM-FDE. Finally, considering BeiDou measurements, RAIM has no impact with uBlox but is effective with Xiaomi, as demonstrated by the reduction in the maximum errors. In Table 9, the reliable availability, defined as the solution percentage indicated by RAIM as reliable, is reported for each considered device, GNSS, and processing technique. Reliable availability is always lower than or equal to solution availability. The most critical configurations, according to reliable availability, are Glonass with both uBlox and Xiaomi, and Galileo with Xiaomi. For Glonass with uBlox, about 35% of the solutions are not reliable; for Glonass with Xiaomi, about 98% of the solutions are not reliable; and for Galileo with Xiaomi, there are no reliable solutions. For Galileo, these low values are due to severe geometric conditions and a limited number of measurements, while for the uBlox Glonass case, several outliers have been identified among the measurements.
## 2 Methods
### Hybrid Global Structure Model (HGSM)  
HGSM [28] is an enhanced version of the Global Structure Model (GSM). HGSM integrates the \(Ks\) value into conventional self-influence (iSI) to increase the local impact of a node's \(DC\). The self-influence (iSI) is then used to compute the global impact (iGI). The iGI factor calculates the sum of the neighbor ratios of the shortest path lengths by averaging the iSI values for directed and undirected nodes with \(Ks\) and \(DC\) values. The HGSM is defined as Equation (7).  
\[\begin{split} HGSM(v)&=iSI(v)\times iGI(v)\\ &=e^{\frac{Ks(v)\times DC(v)}{N}}\times\sum_{v\
eq u}\frac{e^{ \frac{Ks(u)\times DC(u)}{N}}}{d_{uv}^{cell(\log_{2}(avc_{v}iSI))}}\end{split} \tag{7}\]  
where \(Ks(v)\) and \(Ks(u)\) are K-shell decomposition of node \(v\) and node \(u\). \(DC(v)\) and \(DC(u)\) are degree centrality of node \(v\) and node \(u\), respectively. \(N\) is the total number of nodes.
# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 6 Recommendations and Future Research Directions
### Datasets Structure and Size  
There is a need for a structured format to represent feedback datasets, whether they are captured at the sentence level or document level via a survey or a questionnaire form. A structured format in either an XML or a JSON file would be highly useful to standardize dataset generation for sentiment analysis in this domain. Furthermore, there is a need to associate the meta-data acquired at the time of the feedback responses. The meta-data would help to provide a descriptive analysis of the opinions expressed by a group of people for a given subject (aspect). Moreover, more than half (56.7%) of the datasets used in the reviewed papers were of a small-size, with merely 5000 samples or less, which affects the reliability and relevance of the results [102]. Additionally, most of these datasets are not publicly available, meaning that the results are not reproducible. Therefore, we recommend the collection of large-scale labeled datasets [14] to develop generalized deep learning models that could be utilized for various sentiment analysis tasks and for big data analysis in the education domain.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 2 Model Design
### Component-Specific Prediction Submodels
#### 2.2.1 BiLSTM Model for Trend Component Prediction
The convolution layer is the core part of the 1D-CNN model. Through the convolution operation, local features can be automatically extracted from the remainder. Different convolution kernels can learn various types of features, such as trend patterns, periodic features, etc. By using multiple convolution kernels in parallel, the convolution layer can effectively capture diverse feature representations, thereby enhancing the model's ability to represent complex residual patterns. The pooling layer is usually placed after the convolutional layer and is responsible for downsampling the output of the convolution operation. This process reduces the dimension of the data and computational complexity, while enhancing the robustness of the model. In addition, the pooling layer provides a certain invariance to small changes in the input, thereby improving the generalization ability of the model. The function of the fully connected layer is to integrate the features extracted by the previous convolution layer and pooling layer. Each neuron in this layer is fully connected to all neurons in the previous layer, thus achieving a comprehensive aggregation of feature. After a series of convolution and pooling operations, the output of the fully connected layer is used for the remainder prediction.
## 5 Conclusions  
HF DPOAEs are measurable and repeatable in newborns. While DPOAE repeatability is similar between FPL and SPL calibration methods, the results are promising for improved results with FPL calibration, warranting further exploration. Deeper probe tip insertions resulted in a greater number of DPOAEs recorded in newborns, regardless of the calibration method used. Overall, for monitoring cochlear function at the highest frequencies with present DPOAEs, alternate calibration methods and eartips allowing a deeper insertion have the potential for maximizing the measurement of and repeatability of responses in newborns. SOAEs were measured in 86% of newborns with 32% of the recorded SOAEs occurring at frequencies greater than 4 kHz. Stimulus parameters need to be explored further and maximized for the measurement of HF DPOAEs, as well as determining the repeatability of these measures across sessions in newborns so these measures can be utilized in serial monitoring programs. Further examination of calibration techniques impact and the influence of SOAE presence on HF DPOAE measures need to be conducted in newborns. The results of this initial work show promise for developing objective measures of basal cochlear health in newborns.  
All authors contributed equally to data collection, analysis, and writing of the manuscript. The first three authors were responsible for the research design. Specifically, Conceptualization, L.D.H. and E.A.; Data Curation, L.D.H., N.P., E.A., W.H., D.K., S.M. and C.M.; Formal Analysis, L.D.H., N.P., E.A. and W.H.; Investigation, L.D.H., N.P. and E.A.; Methodology, L.D.H., N.P. and E.A.; Project Administration, L.D.H. and E.A.; Supervision, L.D.H.; Writing--Original Draft, L.D.H., N.P., E.A., W.H., D.K., S.M. and C.M.; Writing--Review & Editing, L.D.H., N.P., W.H., D.K., S.M. and C.M.  
**Funding:** The second author received an undergraduate summer research award for salary, supplies needed to collect the data, and travel reimbursement to visit AbdaLab at the University of Southern California where [PERSON] and [PERSON] reside and have the expertise to measure OAEs in newborns. No other external funding was received to support this research.  
**Acknowledgments:** We would like to acknowledge the parents who allowed their newborns to participate in this study as well as Dr. [PERSON], a UCSD neonatologist, who granted us access to this population and assisted with IRB approval at UCSD. We are grateful for the constructive discussions with [PERSON] and [PERSON] regarding the measurement of OAEs in newborns. Finally, we are appreciative of the thorough and insightful suggestions by two anonymous reviewers.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 3 Proposed PLSR-SVR Hybrid Predictor
### Plsr-Svr Hybrid Predictor  
When the DVL works well, its outputs can be used to provide the inputs for the KF by subtracting the SINS with DVL velocity measurements. At this time, the PLSR-SVR hybrid predictor works in the modeling mode. To train the PLSR predictor, the current and past calculating velocities of the SINS are used as the predictor inputs, and the DVL velocity measurements are treated as the training target. Besides, the prediction \(\hat{V}_{DVL\_P}^{n}\) by the PLSR predictor subtracted from the DVL measurement \(V_{DVL}^{n}\) gives the residual component \(\delta V_{DVL}^{n}\). The predictor of \(\delta V_{DVL}^{n}\) is developed based on SVR. The training processes of PLSR and SVR are described in detail in Sections 3.1 and 3.2 respectively. During this modeling process, the proposed PLSR-SVR hybrid model is adjusted online with the update of the SINS and DVL measurements. Figure 4 shows the block diagram of modeling mode when the DVL works well.  
When the DVL malfunctions, it can not provide valid measurements. At this time, the PLSR-SVR hybrid predictor switches to the prediction mode. The PLSR predictor uses the current and past calculating velocities of the SINS as inputs to generate the prediction \(\hat{V}^{n}_{DVL,p}\). At the same time, the SVR predictor outputs the prediction \(\delta\hat{V}^{n}_{DVL,S}\) by taking the SINS calculating velocities as inputs. The results of the two predictors are added to form an optimal prediction of the DVL measurement \(\hat{V}^{n}_{DVL}\). Finally, \(\hat{V}^{n}_{DVL}\) is used to substitute for the invalid measurements of the DVL during its malfunction. Figure 5 shows the block diagram of prediction mode when the DVL fails.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 4 Nonlinear Estimators
### GAE Enhanced Pulse Peak Attenuator  
The proposed instantaneous nonlinear device GAEPPA identifies the knowledge of the state of the underlying noise generation process, the same as in GAE. The proposed GAE enhanced PPA uses this knowledge to attenuate the input \(y\) only when the amplitude of the received signal exceeds the threshold value. In other words, it utilizes the knowledge obtained from GAE and threshold-based noise detection method. Hence, the operation of GAE enhanced PPA can be expressed as follows:  
\[\hat{x}_{|kGA|}(y)=\begin{cases}y&\text{if }|y|\leq\alpha_{th}\\ \rho_{k}.y&\text{otherwise.}\end{cases} \tag{10}\]  
Thus, GAE enhanced PPA improves the performance of the pulse blanking method, as it attenuates the signal values, which exceeds the threshold value rather than blanking the received signal, not causing the useful data loss present at carriers. Additionally, this method is less complex, as it works well even with 2-GMM modeling of the DME pulse. The device is well suited to process complex data signals as in LDACS with the modified equation as follows:  
\[\hat{x}_{|kGA|}^{*}(y)=\begin{cases}|y|e^{jarg(y)}&\text{if }|y|\leq\alpha_{th}\\ \rho_{k}.|y|e^{jarg(y)}&\text{otherwise.}\end{cases} \tag{11}\]
## 3 Results
### Automated Hematoma Segmentation  
The inter- and intra-rater mean ICC of manual hematoma segmentations by the Yale team were 0.92 and 0.94, and by the Charite team they were 0.97 and 0.99, respectively. The nn-UNET-based segmentation model was trained and optimized on the Yale dataset. The automated segmentation model achieved a Dice of 0.86 \(\pm\) 0.10 and an HD of 3.0 \(\pm\) 6.9 mm in the internal test (ATACH-2) cohort and a Dice of 0.79 \(\pm\) 0.19 and an HD of 7.5\(\pm\) 13 mm in the external validation (Charite) cohort. Notably, we found no significant association between hematoma volume and HD in either the ATACH-2 (r = 0.045, \(p\) = 0.113) or Charite (r = \(-\)0.067, \(p\) = 0.078) cohorts.
# Smart Platform for Monitoring and Control of Discrete Event System in Industry 4.0 Concept
## 2 Preliminaries and Problem Formulation  
We are living at the time of Industry 4.0, which brings a lot of changes, such as smart devices in production processes. With the use of cloud databases and communication with individual smart devices, we can record any activity of machines and devices in industries and provide application users with all the necessary data.  
The term Industry 4.0 was first used in 2011 at the Hannover Messe. The main idea was to use the Internet in all aspects of production. In 2014, the concepts and progress in achieving the vision of the new industrial revolution, which Germany was already leading at that time, were presented. Since 2014, industries in Germany have been using the term Industry 4.0. More than 40% of companies worked with this concept through concrete initiatives. Such cooperation was mainly helped by 28 sponsors and partners, such as Siemens, Cisco, Phoenix, Beckhoff Automation, and others. At the conference, the need for standards was also addressed, such as mechanical, pneumatic, physical, or communication standards. Among the cited standards, concepts such as OPC UA, WSDL, EDDL, and IEE 61499 were mentioned [15; 16; 17; 18; 19; 20].  
Augmented reality belongs to the innovative technologies of Industry 4.0. However, notions of virtual, augmented, and mixed reality are very often confused or mislabeled. First, let us explain the differences between the individual terms. People perceive two types of reality, physical reality and computer-generated reality, which is also referred to as XR (eXtended Reality). XR is a universal term covering all virtual reality connected technologies. Extended reality includes all three other types: virtual (VR), augmented (AR), and mixed (MR) realities [21; 22; 23] (see Figure 1).
Waste fly ash, classified under the code 19 01 13*, is produced as a by-product during the incineration of municipal waste in thermal waste treatment facilities. According to the European waste catalog, this code refers to fly ash containing hazardous substances. Such ash is characterized by a fine grain fraction and a substantial specific surface area, which significantly influence its physicochemical properties. This material can be considered a substitute for natural aggregate in the production of mortars or concrete. This is justified because a number of waste materials, such as fly ash from coal combustion, silica ash, blast furnace slag, and ash from waste incineration, have been successfully used for the production of mortars and concrete [30; 31; 32; 33; 34; 35; 36; 37]. Additionally, polymeric materials like PET, polyethylene, and polypropylene have shown similar utility [38; 39; 40; 41; 42; 43; 44]. Several works have also been published on the use of waste such as slag (MSWI-slag), bottom ash (MSWI-BA), and fly ash (MSWI-FA) from the thermal transformation of municipal waste for this purpose [45; 46; 47]. MSWI-FA has found applications in geopolymers [48; 49; 50; 51] and as an additive in cement [52; 53; 54]. In some cases, MSWI-FA undergoes a melting process at 1450 \({}^{\circ}\)C in an electric furnace for one hour, followed by milling, before being incorporated into slag-cement mortars [55; 56]. When partially replacing Portland cement (10-40%) alongside CaCO\({}_{3}\) (10-40% by weight), these composites achieve high compressive strength after 90 days of curing. Furthermore, MSWI-FA has been used in concrete [37; 39] and mortars [54; 55; 56; 57; 58; 59; 60; 61; 62; 63], though it often reduces the compressive strength of mortars compared to control samples. However, incorporating 0.1% polypropylene fibers into concrete containing MSWI-FA has been shown to enhance compressive strength [64; 65]. These findings highlight the innovative potential of MSWI-derived materials in sustainable construction, offering environmental and performance benefits. The article analyzes research on the potential use of fly ash from municipal waste incineration (MSWI-FA) and dust from rotary kiln dedusting installations (CKD) in the production of cement mortars. Incorporating these materials into construction products could reduce the amount of waste sent to landfills and limit the exploitation of natural resources while simultaneously improving the mechanical properties and durability of the products. This strategy supports a circular economy, contributing to sustainable development and minimizing negative environmental impacts. It is worth emphasizing that, as indicated by the cited publications, the studied waste materials have so far been used only individually. This article presents research findings in which, in addition to the individual use of each type of waste, their mixture was also incorporated into the technology of cement mortar production. This approach significantly expands the application possibilities, enabling the effective use of a wider range of waste materials in construction without compromising their mechanical properties.
## 4 Results and Analysis
### Dataset
#### 4.1.2 Spatial Location Data Set  
Based on the spatial location feature extraction approach detailed in Section 3.1, three groups of original spatial location data were obtained, with each group comprising 26,000 data points. Finite element analysis (FEA) was employed to model and analyze the temperature field distribution inside the drying chamber. This analysis refined the temperature region division and validated the rationality of the hierarchical clustering results.  
The spatial location dataset of the tangerine peel drying temperature field consists of three components as follows:  
1. Spatial coordinate information (x, y, z): The three-dimensional positions of the temperature field in the drying chamber were extracted at a resolution of 5 mm, yielding a total of 26,000 points;
2. Temperature field values (T): The temperature value (in Kelvin) associated with each spatial point;
3. Regional temperature labels: The temperature field data were segmented into six regions via hierarchical clustering, and each data point was assigned a corresponding regional label.  
This dataset was utilized to train a deep learning model for prediction, enabling moisture ratio analysis based on regional temperature characteristics.
###### Abstract  
The current evolution of deep learning requires further optimization in terms of accuracy and time. From the perspective of new requirements, AutoML is an area that could provide possible solutions. AutoML has a neural architecture search (NAS) field. DARTS is a widely used approach in NAS and is based on gradient descent; however, it has some drawbacks. In this study, we attempted to overcome some of the drawbacks of DARTS by improving the accuracy and decreasing the search cost. The DARTS algorithm uses a mixed operation that combines all operations in the search space. The architecture parameter of each operation comprising a mixed operation is trained using gradient descent, and the operation with the largest architecture parameter is selected. The use of a mixed operation causes a problem called vote dispersion: similar operations share architecture parameters during gradient descent; thus, there are cases where the most important operation is disregarded. In this selection process, vote dispersion causes DARTS performance to degrade. To cope with this problem, we propose a new algorithm based on DARTS called DG-DARTS. Two search stages are introduced, and the clustering of operations is applied in DG-DARTS. In summary, DG-DARTS achieves an error rate of 2.51% on the CIFAR10 dataset, and its search cost is 0.2 GPU days because the search space of the second stage is reduced by half. The speed-up factor of DG-DARTS to DARTS is 6.82, which indicates that the search cost of DG-DARTS is only 13% that of DARTS.  
DARTS; DG-DARTS; neural architecture search; operation clustering; vote dispersion problem +
Footnote †: journal: Applied Sciences
_Applied_  
_sciences_  
_Edge-of-Field Technologies for Phosphorus Retention_  
from Agricultural Drainage Discharge_  
**[PERSON]**  
Independent Researcher, Natal 59064-740, Brazil; [EMAIL_ADDRESS];  
Tel.: +55-84-99891-8045 or +45-2336-5942  
Received: 27 November 2019; Accepted: 1 January 2020; Published: 16 January 2020  
**Featured Application: This review provides a background that helps targeting the phosphorus loads from specific tile-drained agricultural catchments with suitable edge-of-field technologies, ultimately contributing to a better cost-efficiency.**  
**Abstract:** Agriculture is often responsible for the eutrophication of surface waters due to the loss of phosphorus--a normally limiting nutrient in freshwater ecosystems. Tile-drained agricultural catchments tend to increase this problem by accelerating the transport of phosphorus through subsurface drains both in dissolved (reactive and organic phosphorus) and particulate (particle-bound phosphorus) forms. The reduction of excess phosphorus loads from agricultural catchments prior to reaching downstream surface waters is therefore necessary. Edge-of-field technologies have been investigated, developed and implemented in areas with excess phosphorus losses to receive and treat the drainage discharge, when measures at the farm-scale are not able to sufficiently reduce the loads. The implementation of these technologies shall base on the phosphorus dynamics of specific catchments (e.g., phosphorus load and dominant phosphorus form) in order to ensure that local retention goals are met. Widely accepted technologies include constructed wetlands, restored wetlands, vegetated buffer strips and filter materials. These have demonstrated a large variability in the retention of phosphorus, and results from the literature can help targeting specific catchment conditions with suitable technologies. This review provides a comprehensive analysis of the currently used edge-of-field technologies for phosphorus retention in tile-drained catchments, with great focus on performance, application and limitations.  
**Keywords:** phosphorus load; hydraulic residence time; water flow; sedimentation; sorption; constructed wetland; restored wetland; vegetated buffer strip; filter material; iron