esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction: Name Disambiguation and Collocation
### Algorithmic vs. manual name disambiguation and collocation  
Name disambiguation in databases occurs two ways: manual and automatic. Manual name disambiguation is done in library cataloging. When a new book is processed, the cataloger searches a database of name metadata and identifies the authors and subjects of the work, and adds name metadata to the bibliographic record that matches the authorized forms found in the authority file. If no record for the author exists in the file, the cataloger either creates a new one or just adds the name to the bibliographic record in the form found in the book. Manual name disambiguation is done by humans.  
The other kind of name disambiguation is algorithmic name disambiguation. This is the kind of disambiguation done by computers. Whereas manual name disambiguation is deterministic in its nature, algorithmic disambiguation is probabilistic. Still, neither method is error-free. Disambiguation is made more difficult by the practice -- in the past few decades -- of extensive collaboration on research. In fact, it's not uncommon for an article to have as many as ten or twenty authors, often with the forename of each represented by just an initial.  
Much algorithmic name disambiguation relies on metadata created manually. This hybrid approach in the end may become the most successful and widely used. In this approach, computers attempt to either disambiguate names of different people or to aggregate variant names in a single person in a given database [8]. This process is informed by manually-created name metadata. The richness and discriminating ability of the metadata powers the algorithmic processes. All online information -- including names -- does a poor job of representing itself and making itself discoverable. Metadata fills this gap.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### UOC--Today's Research Challenges
#### 7.2.2 Underwater Optical Communication  
Underwater optical communication is recently gaining more attention to provide high-speed underwater data-point connectivity. UOC technology can establish communication links between remotely operated underwater vehicles (ROVs) and AUVs. Underwater UOC can be used in a global communication network to establish internet connectivity in remote areas. The most efficient optical wavelength range is the blue-green region at a wavelength of 450-550 nm. Laser diodes (LDs) were proposed to increase the data rate compared to the light-emitting diodes (LEDs), which have insufficient bandwidth and low transmission distance. A laser-diode-based underwater wireless communication system with a data rate of 1.5 Gbit/s over a 20 m channel using an NRZ-COK modulation scheme is reported in [492]. Other recent research for underwater UOC to achieve a data rate of 4.88 Gbit/s using QAM-OFDM is reported in [493].
## 3 Methods and Materials
### Combination of Deep Learning and SVM
#### 3.3.2 Support Vector Machine  
In the hybrid method, the classification layers are removed from deep learning models and replaced by the SVM algorithm. The deep features are extracted by deep learning models; the features are stored in feature vectors and sent to the classification stage by the SVM algorithm [36].  
The SVM algorithm is a supervised algorithm that solves many tasks, including classification tasks. SVM distributes the dataset in \(n\) dimensions, where \(n\) represents the deep features, and each value of the deep features represents a specific coordinate [37]. The algorithm separates the dataset classes by a hyperplane. The algorithm generates many hyperplanes, and each hyperplane has a margin between different classes. Thus, the algorithm chooses the one hyperplane that has the maximum margin between classes. Support vectors define the best hyperplane, and which data points are above or near the hyperplane. There are two types of SVM algorithm, linear and non-linear [38]. The linear type was applied in this study because the dataset is linearly separable. The algorithm created a single hyperplane to separate the dataset into two classes, namely normal and tuberculosis.  
Figure 4 describes the methodology of the hybrid method for classifying chest X-rays. It can be seen from the figure that deep learning models are fed with optimized images. Deep learning models for extracting deep features. Because of the high dimensionality of the features, the dimensions were reduced by applying the PCA algorithm and storing them in the feature vectors [39]. The last part of the technique involves classifying feature vectors by the SVM algorithm.
## 5 Conclusions  
Based on the influence of intense L-band SRBs on the \(C/N_{0}\) of GNSS, a new method for detecting intense L-band SRB events is proposed. The influence of the number of satellites, number of monitoring stations, satellite distribution and different GNSS systems on the detection results are analyzed through several experiments. The conclusions can be summarized as follows:  
1. The detection rate of intense L-band SRBs reaches more than 80% for the flux density above 800 SFU at the L2 frequency of GPS.
2. The detection results of GPS L2 and GLONASS G2 are better than those of GPS L1 L5, GLONASS G1 and Galileo E1 E5.
3. The distribution of the satellites relative to the Sun has no impact on the overall detection rate.
4. Statistically, the proposed detection algorithm is proven to have high reliability, with a false alarm rate of approximately 0% for historical SRB events detection with the optimal \(N_{sat}\) and \(N_{station}\).  
The integrity, continuity, availability and accuracy of GNSS are important evaluation indexes for navigation satellite systems, and the interference of solar radio noise is a critical factor affecting the performance of GNSS. With the rapid development of GNSS, it is necessary to assess the negative impact of intense L-band SRBs on the system. Dispensing with expensive radio telescopes, we propose an intense L-band SRB detection method with high recognition rate, low false alarm rate, low cost, and all-weather real-time monitoring. The proposed method and insights in this paper can be conducive to the identification of intense L-band SRBs, which is a key component of attempting to suppress SRBs interference on GNSS in the future.  
editing, X.C. and M.L.; visualization, F.Y.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Plan of China under Grant 2018 YFB0505103.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Sample Preparation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Flour** \\ **Name** \\ \end{tabular} } & **Class** & \multirow{2}{*}{**Production Company**} & **Ingredients** & \begin{tabular}{c} **Particle Size** \\ **(\(\upmu\)m)** \\ \end{tabular} & \begin{tabular}{c} **Damaged** \\ **Starch Content** \\ **(\(\upmu\)s)** \\ \end{tabular} \\ \hline \multirow{2}{*}{**Weak rice flour**} & \multirow{2}{*}{WF} & Daedoo Foods Co., Ltd, & \multirow{2}{*}{Rice 100\(\%\)} & 54.44 \(\pm\) 0.62 & 2.31 \(\pm\) 0.04 \\  & & Seoul, Korea & & & \\ \hline \multirow{2}{*}{**Mimyeon**} & MF1 & NICS, RDA, & \multirow{2}{*}{Rice 100\(\%\)} & 128.3 \(\pm\) 0.61 & 7.44 \(\pm\) 0.44 \\ \cline{2-6}  & MF2 & NICS, RDA, & \multirow{2}{*}{Rice 100\(\%\)} & \multirow{2}{*}{76.33 \(\pm\) 1.02} & 4.42 \(\pm\) 0.15 \\ \cline{2-6}  & & Miryang, Korea & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Strong rice** \\ **flour** \\ \end{tabular} } & \multirow{2}{*}{SF} & Daedoo Foods Co., Ltd, & \multirow{2}{*}{Rice 75.6\(\%\), gluten, } & \multirow{2}{*}{63.12 \(\pm\) 0.17} & 7.46 \(\pm\) 0.35 \\  & & Seoul, Korea & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Tapioca starch** \\ **flour** \\ \end{tabular} } & \multirow{2}{*}{TS} & Bampong Tapioca Flour & \multirow{2}{*}{Tapioca 89\(\%\), } & \multirow{2}{*}{27.33 \(\pm\) 0.24} & \multirow{2}{*}{2.16 \(\pm\) 0.27} \\  & & Industrial Co, Ltd & & maltodextrin, guar gum \\ \cline{2-6}  & & Bangkok, Thailand & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Sweet potato** \\ **starch flour** \\ \end{tabular} } & \multirow{2}{*}{SS} & Tureban Co., Ltd & Sweet potato starch & \multirow{2}{*}{16.79 \(\pm\) 0.75} & \multirow{2}{*}{0.30 \(\pm\) 0.05} \\  & & Goyang, Korea & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Chestnut** \\ **starch flour** \\ \end{tabular} } & \multirow{2}{*}{CS} & Gongju Chestnut Farm & \multirow{2}{*}{Chestnut starch 100\(\%\)} & 26.34 \(\pm\) 0.25 & 1.05 \(\pm\) 0.01 \\  & & Union, Gongju, Korea & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Mungbean** \\ **starch flour** \\ \end{tabular} } & \multirow{2}{*}{MS} & Nongmin food, & \multirow{2}{*}{Mungbean starch 100\(\%\)} & 514.2 \(\pm\) 12.89 & 0.47 \(\pm\) 0.03 \\ \cline{1-1} \cline{5-5}  & & Seecheon, Korea & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Flour items attribute and particle size. (MF2, No.3, 76 \(\upmu\)m), strong rice flour (SF, No.4), tapioca starch flour (TS, No.5), sweat potato starch flour (SS, No.6), chestnut starch flour (CS, No.7), and mungbean starch flour (MS, No.8), respectively.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 2 Methods
### Problem Statement and Proposed Solution Framework
#### 2.2.3 Source Model: Gaussian Mixture Model  
In this paper we use a Gaussian mixture model as a prior for the (pseudo) log-power coefficients \(\mathbf{s}_{n}\) as  
\[p(\mathbf{s}_{n}\mid\mathbf{\mu},\;\mathbf{\gamma},\;\mathbf{z}_{n})=\prod_{d=1}^{D}\left( \prod_{f=1}^{F}\mathcal{N}\Big{(}s_{n}^{f}\mid\mu_{d^{\prime}}^{f},\;(\gamma_{ d}^{f})^{-1}\Big{)}\right)^{z_{nd}}, \tag{12}\]  
where the source index \(k\) is omitted for compactness of notation. Here we assume independence between the frequency bins to ease computations ([34], pp. 64-65). The mixture components are denoted by \(d=1,2,\ldots,D\). The mixture means \(\mathbf{\mu}=[\mathbf{\mu}_{1},\ldots,\mathbf{\mu}_{D}]^{\top}\) are modeled as  
\[p(\mathbf{\mu}_{d})=\prod_{f=1}^{F}\mathcal{N}\Big{(}\mu_{d}^{f}\mid m_{d^{\prime} }^{f},\;\
u_{d}^{f}\Big{)}, \tag{13}\]where \(\mathbf{\mu}_{d}=[\mu_{d}^{1}\dots,\mu_{d}^{F}]^{\top}\) and where \(m_{d}^{f}\) and \(v_{d}^{f}\) represent the mean and variance of the mixture mean \(\mu_{d}^{f}\), respectively. Similarly the mixture precisions \(\gamma_{d}=[\gamma_{d}^{1},\dots,\gamma_{d}^{F}]^{\top}\) are modelled as  
\[p(\gamma_{d})=\prod_{f=1}^{F}\Gamma(\gamma_{d}^{f}\mid a_{d}^{f},\;b_{d}^{f}), \tag{14}\]  
where \(\Gamma(\cdot\mid\alpha,\;\beta)\) denotes the Gamma distribution with shape and rate parameters \(\alpha\) and \(\beta\), respectively. \(a_{d}^{f}\) and \(b_{d}^{f}\) represent the shape and rate parameters of the mixture precision \(\gamma_{d}^{f}\), respectively.  
We use one-hot encoding [28] to represent mixture selection variables \(\mathbf{z}_{n}=[z_{n1},\dots,z_{nD}]^{\top}\), thus \(\sum_{d=1}^{D}z_{nd}=1\) and \(z_{nd}\in\{0,1\}\). We assume a categorical prior distribution for \(\mathbf{z}_{n}\)  
\[p(\mathbf{z}_{n}\mid\mathbf{h})=\text{Cat}(\mathbf{z}_{n}\mid\mathbf{h},\;D), \tag{15}\]  
where \(D\) denotes a number of components and \(\mathbf{h}\) the event probabilities. Finally, we model \(\mathbf{h}\) using a Dirichlet prior as  
\[p(\mathbf{h})=\text{Dir}(\mathbf{h}\mid\mathbf{\alpha}), \tag{16}\]  
where \(\mathbf{\alpha}=[\alpha_{1},\dots,\alpha_{D}]^{\top}\) are the concentration parameters.  
In summary, in this section we have further specified the generative model of (1). To apply the proposed framework, two distinct models have been specified. The first model is based on the Algonquin-based source mixing model, in which the individual signals are represented by Gaussian mixture models. This first probabilistic model is fully specified by (8), (12)-(16). Secondly, an alternative model has been presented, which is based on Gaussian scale models. This probabilistic model is fully specified by (11)-(16). The soundscaping framework supports different acoustic models, concerning both the source mixing models as the source models.
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 4 Fine-Level Focusing and Classification of MEO SAR Ship Targets  
Based on the first-level classification results, target local region slices are extracted according to the spatial distribution of typical features. Target local region slices are then directed to corresponding second-level focusing-classification networks for fine classification. The typical feature locations for different targets are demonstrated in Figure 9.  
The extracted local region slice is first processed by the fine-refocusing algorithm to further improve imaging quality. Then the refocused slice is input to the fine-classification network. If the first-level result is a civilian ship, it enters a dedicated civilian ship classification network for further distinction among bulk carriers, container ships, and tankers. If the first-level result indicates other military ships, it proceeds to a specialized network for further classification between destroyers and cruisers. If the first-level result is an aircraft carrier, it enters an aircraft carrier confirmation network for final verification.  
Figure 8: Coarse classification network diagram.  
Figure 9: Typical features of and key part distribution for different ship targets.
## 2 Materials and Methods
### Data Processing and Models' Development  
The experimental data of APC (log CFU/mL or g) in the examined samples during manufacturing and ripening of Domiati cheese versus time (days) was collected in MS Excel (Microsoft Corporation) for each separated treatment. The lag time (LT, d), maximum growth rate (\(\upmu_{\text{max}}\), log CFU/g) and maximum population density (MPD, log CFU/g) for each growth curve were calculated by fitting the observed data to the Baranyi model (Equation (1)) [36] by using the DMFit excel program (Institute of Food Research, Norwich, England). The performance and accuracy of the developed models were evaluated for the goodness-of-fit with the current data by measuring the Mean Squared Error (MSE) and coefficient of determination (R\({}^{2}\)).  
For the sake of the evaluation of the fate of _S. aureus_ during storage against the addition of yoghourt starter culture and NiO NPs during processing and cheese ripening, two mathematical inactivation models were tested.  
1. The log-linear model was used as follows: \[\log N(t)=\log N_{0}-k_{\text{max}}\cdot t\] (1) where \(N(t)\) is the number of survival cells (log CFU/g) at time \(t\)(d); \(N_{0}\) corresponds to the initial inoculum level (log CFU/g); and \(k_{max}\) is defined as the maximum survival rate (log CFU/g).
2. The Weibull model was as follows: \[\log_{10}N(t)=\log_{10}N_{0}-\left(\frac{t}{\delta}\right)^{p}\] (2) where \(\delta\) is the first-decimal reduction time (d) and \(p\) is a shape parameter.  
Specifically, the Weibull-type model [37; 38] has been previously used to describe the non-log-linear nature of survival curves thanks to its flexibility and simplicity. The freeware add-in GINaFit v1.6 [39] was used for the fitting procedure and statistical estimates.
###### Abstract  
The aim of this study was to pinpoint the cerebral regions implicated during swallowing by comparing the brain activation areas associated with two different volitional movements: tongue protrusion and tongue elevation. Twenty-four healthy subjects (11--males 22 \(\pm\) 2.9 y; 13--females 23 \(\pm\) 4.1 y; were examined through functional magnetic resonance imaging (fMRI) while performing two different swallowing tasks: with tongue protrusion and with tongue elevation. The study was carried out with the help of fMRI imaging which assesses brain signals caused by changes in neuronal activity in response to sensory, motor or cognitive tasks. The precentral gyrus and the cerebellum were activated during both swallowing tasks while the postcentral gyrus, thalamus, and superior parietal lobule could be identified as large activation foci only during the protrusion task. During protrusion tasks, increased activations were also seen in the left-middle and medial frontal gyrus, right thalamus, inferior parietal lobule, and the superior temporal gyrus (15,592-voxels; Z-score 5.49 \(\pm\) 0.90). Tongue elevation activated a large volume of cortex portions within the left sub-gyral cortex and minor activations in both right and left inferior parietal lobules, right postcentral gyrus, lentiform nucleus, subcortical structures, the anterior cingulate, and left insular cortex (3601-voxels; Z-score 5.23 \(\pm\) 0.52). However, the overall activation during swallowing tasks with tongue elevation, was significantly less than swallowing tasks with tongue protrusion. These results suggest that tongue protrusion (on inferior incisors) during swallowing activates a widely distributed network of cortical and subcortical areas than tongue elevation (on incisor papilla), suggesting a less economic and physiologically more complex movement. These neuromuscular patterns of the tongue confirm the different purpose of elevation and protrusion during swallowing and might help professionals manage malocclusions and orofacial myofunctional disorders.  
brain; tongue posture; tongue elevation; tongue protrusion; fMRI analysis; swallowing +
Footnote †: journal: Accepted: 21 August 2020; Published: 31 August 2020
## 6 Conclusions
### Limitations Discussion  
Despite the achievements of this study, several important limitations warrant careful consideration. Our multi-run training method, while addressing the randomness problem in deep learning models, significantly increases computational cost by requiring training of five model instances for each hyperparameter configuration. Additionally, our hyperparameter optimization focused on three key parameters (LSTM units, LSTM layers, and fully connected layers) but did not explore other potentially influential parameters such as learning rate, batch size, and activation functions. The ensemble learning models demonstrated inconsistent improvements, failing to outperform single models across all prediction scenarios and evaluation metrics, suggesting that simple combinations of shallow learning and deep learning models may be insufficient for comprehensive performance enhancement.  
The scope of our experimental validation presents another significant limitation. Our experiments relied exclusively on urban road segment data from Shenzhen, lacking validation on segments with different geographical, geometric, and traffic characteristics that might affect the generalizability of our conclusions. Different road segments with varying locations, lengths, and traffic flow patterns may require distinct model designs and hyperparameter configurations. Furthermore, our data preprocessing approach using forward filling to handle missing values may introduce systematic biases, particularly during nighttime low-traffic periods when traffic patterns differ fundamentally from peak hours.  
The evaluation framework itself reveals important limitations in model assessment. The substantial performance differences between MAPE and RMSE metrics complicate model selection decisions, as deep learning models excel in MAPE performance while shallow learning models like random forest demonstrate superior RMSE performance.  
This metric inconsistency reflects the complex nature of urban road travel time data, which contain both numerous low-value observations and extreme values. These findings emphasize the need for more nuanced evaluation approaches that consider multiple metrics simultaneously and select models based on specific application requirements rather than single performance indicators.
# Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
## 6 Conclusions  
A method to design nearly linear-phase 2-D filters has been presented. The method is based on the model reduction of 2-D filters using balanced realization. The design method starts with the design of a 2-D linear-phase FIR filter and its state-space representation as a 2-D system with a separable denominator. The balanced realization of this system can be obtained using the 2-D structural controllability and structural observability Gramians of the system. These block-diagonal Gramians satisfy a set of Lyapunov inequalities, which are solved using an optimization approach under linear matrix inequality constraints. The use of the structural Gramians ensures that the resulting reduced-order system satisfies a 2-D Lyapunov equation and is, therefore, 2-D stable. The proposed method is illustrated by  
Figure 6: Original and filtered plane wave images using 2-D FIR fan filter and their spectrum.  
Figure 7: Original and filtered plane wave images using reduced-order 2-D IIR fan filter and their spectrum.  
numerical examples that have shown that the method is suitable for the design of reduced-order 2-D IIR filters with nearly linear phase in the passband. The performance of the obtained 2-D IIR filters compares favorably with existing techniques, while at the same time they can be implemented using a separable-denominator filter with less computational complexity than the original 2-D FIR filter. Although the phase of the reduced-order 2-D IIR filter is only nearly linear, we can see that the reduced-order 2-D IIR filter offers good selectivity, computation efficiency, and reduced system delay when compared to the corresponding 2-D FIR filter.  
Conceptualization, A.O. and P.A.; methodology, A.O. and P.A.; software, A.O., P.A. and D.S.; validation, A.O., P.A. and D.S.; formal analysis, A.O., P.A. and D.S.; investigation, A.O., P.A. and D.S.; resources, A.O. and P.A.; data curation, A.O. and P.A.; writing--original draft preparation, A.O.; writing--review and editing, A.O., P.A. and D.S.; visualization, A.O., P.A. and D.S.; supervision, P.A. and D.S.; project administration, A.O., P.A. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by NSERC of Canada.  
Data are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
1-D One-Dimensional  
2-D Two-Dimensional  
FIR Finite Impulse Response  
IIR Infinite Impulse Response  
LMI Linear Matrix Inequality  
MOR Model Order Reduction  
PB Passband  
SB Stopband  
SDP Semi-Definite Problem  
tr Trace  
diag Diagonal  
Min Minimum  
Max Maximum  
PW Plane Wave  
U Unit Circle