esa-sceva/satcom-chunk-collection huggingface dataset:
# Flavoromics Analysis of Passion Fruit-Roasted Chicken
## 3 Conclusions  
Flavoromics technology is an effective method for research on flavor components. Using this technology, the top contribution rate of flavor components to the overall flavor of passion fruit-roasted chicken was determined as ethyl butyrate (50.44%), phenylethanol (14.81%), guaicaene (8.77%), ethyl maltol (4.93%), n-hexanal (3.87%), 3-furfurral (3.53%), and isovaleraldehyde (3.15%). During the roasting process, these characteristic flavor components were dynamic: they first increased, reaching their maximum values at 30 min of roasting, then slowly decreased during the 30-60 min roasting stage. During the preservation process, the types and content of flavor components showed a pattern of first increasing and then decreasing. After 30 days of storage, there were only 31 volatile substances, with the highest proportions of substances being alcohols (35.46%) and hydrocarbons (37%).  
On the other hand, during the roasting process, the main precursors of flavor components evolved in different patterns. The protein content was high (70.77%) in raw chicken, and decreased in the roasting period, but was basically maintained at the level of 53.35%-56.48%. Fat content was also high (19.36%) in raw chicken and decreased in the roasting period, but first increased from 11.85% to 17.82% before 45 min of roasting and then dropped to 12.23% in the finished product. In contrast, the content of soluble saccharides was low (41.91 mg/100 g) in raw chicken but markedly increased during the roasting period, being maintained at the level 7279.62 mg/100 g-9381.19 mg/100 g.  
Finally, it is noted that a number of flavor components were present in passion fruit-roasted chicken but absent in raw roasted chicken without passion fruit (Table S2), with the main components including aldehydes (isovaleraldehyde, 3-furfurral, phenylacetaldehyde), ketones (3-hydroxy-2-butanone), alcohols (linalo oxide), acids and esters (ethyl acetate), and hydrocarbons (\(\beta\)-pinene). In particular, some flavor components were present in passion fruit-roasted chicken but absent in other pre-made chicken products (Table S9), such as ketones (5-methyl-2-hexanone, 3-hydroxy-2-butanone, and 2-furanylacetone), alcohols (2-methylbutanol, 2-Heptanol, Hexanol, (S)--Linaloal oxide, L-Linaloal, Terpineol, and Phenylethanol), acids and esters (ethyl acetate, Ethyl Butyrate, ethyl lactate, 1-Methylhexyl ester, Hexyl butyrate, Ethyl octanoate, and ethyl 3-hydroxybutyrate), hydrocarbons (\(\beta\)-myrcene, (+)--limonene, ocimene, styrene, Guaiacol, \(\beta\)-caryophyllene, 1-Pentene, and Anisin), ethers (dimethyl ether), and phenols (ethyl maltol). In summary, passion fruit-roasted chicken is a novel product with unique flavor components.
## 2 Literature Review  
### Embedded Methods  
Embedding methods map entities and relationships in the knowledge graph to a low-dimensional vector space and use vector operations to achieve semantic matching and reasoning. The core idea of these methods is to transform structured knowledge into continuous numerical representations, thus enabling end-to-end training compatible with deep learning models. [PERSON] et al. proposed a knowledge graph embedding approach that learns high-dimensional embeddings for entities and relations in knowledge graphs [7]. By combining these embeddings with question representations, their method scores candidate answers to select the final result [7]. [PERSON] et al. introduced hypergraphs and reasoning chains for multi-hop inference [8]. Their framework generates entity embeddings with high-order semantic features via hypergraphs and employs a reasoning chain modeling module to evaluate the importance of inference paths. A scoring network also assesses candidate responses based on both factual triples and reasoning processes [8]. [PERSON] et al. proposed TransferNet, a model that iteratively propagates entity scores across multiple steps to infer answers [9]. Starting from the topic entity in a question, the model calculates the activation probabilities for the entities; computes the activation scores of the relation, focusing on the key semantic tokens of the query; and traverses the high-scoring relations to reach the target entity [9]. [PERSON] et al. developed the UNIKGQA model, which comprises two core modules: a semantic matching module that leverages pretrained language models to align questions with relations and a matching information propagation module that computes entity matching scores based on semantic features [10]. [PERSON] et al. proposed the MemNN, which stores knowledge graph triples through memory units and uses an end-to-end multi-layer attention mechanism for multi-hop reasoning [11]. Essentially, it implicitly encodes structured knowledge as vector representations in memory and calculates answers relying on attention weights [11]. [PERSON] et al. proposed a key-value separation structure, introduced based on the MemNN [12]. The key encodes the vector of the \"subject-relation\" pair, and the value corresponds to the target entity. More accurate semantic alignment is achieved through key-value matching [12]. Embedded methods make full use of the efficiency of spatial operations of vectors and have obvious advantages in retrieval efficiency. However, this reliance on implicit vector operations also has certain limitations: on the one hand, this black-box reasoning mode makes it difficult to trace the specific reasoning path, and it is impossible to explain why a certain entity is selected. On the other hand, there is insufficient semantic alignment between natural language questions and structured embeddings.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 2 Related Work
### PSO-Based Routing Protocols  
In Reference [26], the PSO algorithm is utilized for cluster forming and inter-cluster routing. Two different kinds of sensors are randomly deployed in the target area. Nodes with higher initial energy not only detect the information of surroundings but also function as gateways for data forwarding. A novel method adopted PSO is presented to determine which gateway should be chosen as the forwarder. In Reference [27], authors enhance the performance of the algorithm presented in Reference [26] and pay more attention to energy balancing. A special particle-encoding method and multi-target fitness method are created to achieve a balance between energy efficiency and energy balancing.  
In Reference [28], a novel idea for a clustering-adopting PSO has been presented in EPMS. The authors utilize several straight lines to partition the whole sensor area into subdomains, and each domain denotes a cluster. Each line is represented as an included angle and intercepts with the coordinate axis, and the fitness function describes the quality of the clustering. In Reference [29], the authors present a variable dimension PSO method for mobile sink moving trajectory planning. The mobile sink stays at the rendezvous points for data collection and the number of rendezvous is not determinate. The authors also present a novel method for particle updating using the nearest dimension.  
Protocols such as ACB-SA, ACO-MCC3D, and TPACO, which do not introduce clustering technology, are restricted to small scale networks because the sink node has no assistants and it needs to process all packages. Meanwhile, protocols which only adopt a static sink like ACB-SA, ACO-MNCC, and DSBA may cause hot spot problems and result in nodes' premature death. One of the big challenges this field faces is how to combine clustering with sink mobility technology to enhance the performance of the network. Additionally, all the protocols in Table 1 are centralized, which means nodes in those protocols need control messages to have guidance for their next action. Distributed protocols are demanded to decrease the burden of control messages.
###### Abstract  
With the development of 5G technology, programs are gradually moving to cloud services. This leads to an increasing demand for storage. In the field of high-density data storage, bit-pattern media recording (BPMR) is considered a promising approach, as it can expand the data density to 4 Tb/in\({}^{2}\). However, in high-density BPMR, bits or magnetic islands are very close to each other, leading to significant intertrack interference (ITI) from the cross-track direction and intersymbol interference (ISI) from the down-track direction. To minimize two-dimensional interference, including ITI and ISI, the serial detector method has been highly effective. However, in this method, the signal at the output of the first decoder is still a hard output. Therefore, we suggest methods to convert the output of the first detector into a soft output. Additionally, we have developed a new form of generalized partial response target to overcome the track mis-registration. The results show that our proposed methods apparently improve bit error rate performance.  
information storage; bit-patterned media recording; intersymbol interference; partial response maximum likelihood; soft-output viterbi algorithm +
Footnote †: journal: Applied Sciences
# Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
## 3 Proposed Method  
For rotating machine fault diagnosis, we propose a new fault diagnosis method called enhanced k-nearest neighbor (EKNN), which tries to combine both the advantages of parameter-based and case-based fault diagnosis methods. Generally, the powerful and automatic feature extraction capability of parameter-based fault diagnosis methods is adopted in EKNN first, and then the case-based classification process is adopted in the feature classification stage of EKNN.  
Meanwhile, in order to address the inherent shortcoming of KNN, which is widely-used in case-based fault diagnosis methods, we also propose a novel solution. First, the global search throughout the whole training set is always required to locate \(k\) nearest neighbors in KNN, which is cost expensive and laborious. Hence, a new method based on sparse coding and KNN is proposed for feature classification. It can effectively overcome shortcomings by combining sparse coding and the similarity matrix to reconstruct each testing sample from the limited training dataset in order to obtain the correlation vector of each testing sample. After the correlation vector is obtained, the proposed method can determine the nearest neighbors automatically. The proposed EKNN is composed of four stages, and their details are as follows. The flowchart of the proposed method is shown in Figure 1.
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 4 Materials and Methods
### Crowd Size Estimation
At regular intervals, the person training the system counted the number of people present in the environment and submitted the resulting count through a training smartphone app, which automatically timestamped the sample. By simultaneously collecting data on people counts and vehicle presence, it becomes straightforward to split the samples based on whether a vehicle is present or not in order to train and evaluate both models. This resulted in a training set of 304 training samples without a vehicle present and 24 training samples with a vehicle present. The evaluation of both models was performed on a test set comprising two one-hour periods of manually collected ground-truth data. This set contained 162 test samples without a vehicle and 40 test samples with a vehicle. The metric used for evaluating the crowd size estimation models is based on a combination of the median, Mean Absolute Error (MAE), RMSE, and Cumulative Distribution Function (CDF) plots displaying the cumulative error expressed in the number of people.
## 2 Proposed Techniques
### Group Method of Data Handling  
The group method of data handling (GMDH) neural networks is a self-organized algorithm, meaning that the connections of the network (connections between neurons) are selected throughout the training phase to optimize the network [32]. In this approach, the neurons are completely not connected with the function nodes. Moreover, the number of layers, neurons in hidden layers, and active neurons are automatically configured, because of their self-organized capability. Furthermore, the network structure is modified until the best structure is accomplished, and thereafter, the optimized network defenses the dependency of the output values on the most notable input variables. It should be mentioned that GMDH can be employed in a wide range of fields, such as complex system modeling, forecasting, data mining, and knowledge discovery. The relation between inputs and outputs can be described as follows:  
\[y=a_{0}+\sum_{i=1}^{M}a_{i}x_{i}+\sum_{i=1}^{M}\sum_{j=1}^{M}a_{ij}x_{i}x_{j}+ \sum_{i=1}^{M}\sum_{j=1}^{M}\sum_{k=1}^{M}a_{ijk}x_{i}x_{j}x_{k}+\ldots \tag{1}\]  
where \((x_{1},x_{2},\ldots,x_{M})\), \((a_{1},a_{2},\ldots,a_{M})\) and \(M\) are the input variables, the coefficient, and the number of input variables, respectively. By applying input data as a matrix, \(N\) point of observations of \(M\) variables are included. In the learning step, the network is tuned and estimates the coefficients of the polynomial, as described by Equation (2), and the remaining data samples are utilized to choose the optimal structure of the model, which can be realized by minimizing the error between the expected output (real value) and the estimated value. In this regard, Equation (3), known as a mean square error, is defined as a cost function of the algorithm.  
\[y=a_{0}+a_{1}x_{i}+a_{2}x_{j}+a_{3}x_{i}x_{j}+a_{4}x_{i}^{2}+a_{5}x_{j}^{2} \tag{2}\]  
\[\frac{1}{N}\sum_{n=1}^{N}\ (y_{n}-\hat{y}_{n})^{2} \tag{3}\]  
where \(\hat{y}_{n}\) and \(y_{n}\) are the estimated and expected values, respectively, and \(N\) is the length of the training dataset. The input variables are considered as pairs of \((x_{i},x_{j})\), as can be seen by Equation (2). The regression polynomial is created and then iterations continue from two to three steps, until the mean square error of the test data converge to a constant value. The configuration of the group method of data handling is depicted in Figure 2.  
Figure 2: Group method of data handling (GMDH) structure.  
Figure 2 illustrates the optimized structure, configured automatically by minimizing the cost function, as defined previously. Furthermore, some node functions were not connected to the network, as can be distinguished in Figure 2.
## 4 Discussion
### Limitations  
In this study, we investigated the responses of GPP and SIF to different environmental conditions based on the binning method and found that the responses of GPP and SIF to different environmental parameters had fundamental differences, which further resulted in a dynamic GPP-SIF relationship under different environmental conditions. Though these results were consistent with some previous studies, we had to highlight some limitations in this study. Firstly, the SIF derived from remote sensing was different from the traditional measurements of the chlorophyll fluorescence at a leaf scale [4]. Therefore, when we interpret and apply the conclusions of our study, we must recognize that these conclusions are applicable to remote sensing SIF and not necessarily to laboratory data. Moreover, we did not quantify NPQ to explain the dynamic GPP-SIF relationship in this study. Though we discussed the potential impact of NPQ on the difference in the GPP and SIF light response curves in Section 4.1, we cannot find a suitable indicator characterizing NPQ to explore and validate the exact role of NPQ on the GPP-SIF relationship, which is mostly caused by the difficulties in the quantification of NPQ through remote sensing. Although many studies had shown that PRI (photochemical reflectance index) may be an effective indicator of NPQ, there were still many controversies and uncertainties [57; 58]. The photoprotective mechanisms of the photosynthetic apparatus consist of not only SIF, but also NPQ [49; 50; 59; 60]. Therefore, considering NPQ is very important to correctly understand the GPP-SIF relationship. Future research should be dedicated to determining an appropriate method to measure NPQ and apply it in the GPP-SIF relationship. Finally, the site-observed dataset we used in this study was from a corn site: the C4 plant. Therefore, the conclusions obtained in this study may not be applicable in the C3 plant. We need to investigate the relationship between GPP and SIF further for other plant function types (PFTs).
## 3 Results and Discussion
### Determination of the Biodegradation Efficiency of the Isolated Strains Exposed to Native Soils  
The biodegradation efficiency for each of the seven isolated strains was evaluated using the optimal growth conditions that were determined earlier in this study. The biodegradation period in each experiment was varied (0, 3, 5, 7, and 9 days) to identify the time period with the highest biodegradation efficiency. In the uninoculated control experiments, degradation efficiency of background TPH was still decreased due to the effect of native strains present in the original soil. Comparison with blank controls experiment, with the increase of time, the TPH concentration of _BY1-7_ gradually decreased, and then, the equilibrium appeared. All strains exhibited an optimal biodegradation efficiency on day 7 (Figure 4A). The residual concentrations of TPH in soil were found to decrease to below 600 mg/kg when incubated with _BY2 (A. junii)_, _BY6 (B. wiedmannii)_, and _BY7 (P. frederiskbergensis)_.  
Figure 3: Phylogenetic tree of the 16S rDNA gene sequence of _BY2_, _BY3_, and _BY5_.  
Figure 4: Residual concentration of TPH in soils using the isolated strains (**A**) and biodegradation efficiency in different growth media (**B**).  
In addition, we examined the effect of different growth media on the degradation efficiency of each strain. As shown in Figure 4B, _BY1 (B. taiwanetsis), BY2 (A. junii), BY5 (R. erythropolis)_, and _BY6 (B. wiedmannii)_ exhibited optimal degradation efficiency in BP medium. _BY3 (S. abikonense)_ exhibited its highest degradation efficiency (51.5%). However, _BY4 (P. mandeliii)_ displayed a maximum biodegradation efficiency of 47.5% and _BY7 (P. frederiksbergensis)_ exhibited its highest biodegradation efficiency in LB medium. It is likely that the strains have a better degradation efficiency in some media rather than others, owing to species differences. All of the strains were able to grow in BP medium but it was unable to provide the best conditions for some of them to grow abundantly and exhibit an optimal biodegradation efficiency. Therefore, the selection of an appropriate growth medium is vital to achieve an optimal biodegradation efficiency for any of these strains.
## 2 Novel LEO Augmented GNSS Receiver System Structure
### Acquisition Method in the LEO-Augmented GNSS Receiver  
The digitized Intermediate Frequency (IF) signal received at the end of the GNSS receiver RF front-end can be presented as:  
\[r(i)=A(iT_{s})D(iT_{s})C(iT_{s}-\tau)\cdot cos[2\pi(f_{1F}+f_{d})T_{s}i+\phi]+n( iT_{s}) \tag{1}\]  
Figure 1: Low Earth Orbit (LEO)-augmented Global Navigation Satellite System (GNSS) receiver system structure.  
where \(r(i)\) is the received IF GNSS signal at the end of the RF front-end sampled at time \(t=iT_{s}\). \(T_{s}\) is the sample period. \(A(iT_{s})\) is the signal amplitude. \(D(iT_{s})\) represents the navigation data bits. \(C(iT_{s}-\tau)\) is the spreading code sequence for GNSS, and \(\tau\) is the code delay in samples. \(f_{IF}\) is the nominal Intermediate Frequency (IF) in Hertz. \(f_{d}\) is the carrier Doppler frequency shift in Hertz. \(\varnothing\) is the carrier phase in rad/s. \(n(iT_{s})\) is the additional band-limited white Gaussian noise. The bilateral power spectral density of the white Gaussian noise is \(N_{0}/2\). The purpose of the signal acquisition is to estimate the code shift \(\tau\) and carrier Doppler shift \(f_{d}\). The receiver needs to perform a two-dimensional search of the maximum range of Doppler frequency and initial code phase for each satellite. In addition, because of the navigation bit reversal, the coherent integration time of a conventional receiver is limited to 10 ms.  
We propose a method of LEO positioning-assisted GNSS signal acquisition to improve the acquisition efficiency and sensitivity. As shown in Figure 2, the baseband processing block estimates the Doppler shift from the LEO satellite. The initial position of the GNSS receiver can be predicted through the LEO Position, Velocity, and Time (PVT) solution algorithm. After projection on LOS, the Doppler and pseudo-range in GNSS can be estimated and used as the reference information of the carrier NCO and PRN generator respectively. Thus, the acquisition efficiency of the receiver can be improved by the Doppler and pseudo-range information. Moreover, the bit inversion problem in the GNSS signal can be eliminated by the navigation message received from the LEO satellite. Therefore, the acquisition sensitivity of the receiver can be effectively improved by the extended coherent integration time.
# Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro
## 3 Implementation Methodology
### Data Collection and Preprocessing  
In the analysis described in this paper, data were provided by the Environmental Protection Agency (EPA) of Montenegro. The data were collected from the air quality monitoring station located in Nilsic, Montenegro and are freely downloadable from the EPA website [25]. There are 9 monitoring stations in Montenegro, located in Podgorica UT, Podgorica UB, Niksic, Bar, Pijevla, Bjelo Polje, Kotor, Gornje Mrke, and Gradina, as shown in Figure 3. The focus of the analysis was Niksic, since this town is recognized as one of Montenegro's urban areas that consistently experiences high pollution levels throughout the year. Our selection was additionally guided by the fact that the datasets obtained from air monitoring stations in other cities in Montenegro were either incomplete or insufficient for our analysis. The air pollutant data from Niksic were recorded from 21 August 2019 until 17 December 2022, and consisted of hourly values of \(PM_{2.5}\), \(PM_{10}\), \(NO_{2}\), \(O_{3}\), \(SO_{2}\), and \(CO\).  
In general, data collected from monitoring stations, sensors, and other sources cannot be readily utilized for analysis without undergoing necessary preparatory steps. The raw data often contain inconsistencies, outliers, missing values, and other imperfections that need to be addressed. To ensure the accuracy and reliability of subsequent analyses and predictions of our model, multiple preprocessing techniques, including data cleaning, data scaling, and the removal of NAN values, were applied to the collected data. Invalid data (missing data) were simply ignored. To unify data on a common scale, we used a MinMax scaler. It works by transforming each feature independently, maintaining the relationshipsbetween the features while ensuring that they all fall within the interval \([0,1]\). For each feature value \(x\), the scaled value \(x_{scaled}\) is calculated using Equation (13):  
\[x_{scaled}=\frac{x-x_{min}}{x_{max}-x_{min}}. \tag{13}\]  
The initial dataset had \(28,142\) values, which was reduced to \(17,007\) after missing data elimination. With the outlier detection and removal, the dataset was additionally reduced to \(16,913\) values. As with other ML techniques, our approach also requires a phase of training and testing. The final dataset of \(16,890\) values, which is the number of continuous 24 h time series, was divided into training and testing datasets, with \(12,672\) (75%) and 4218 (25%) data values, respectively.