esa-sceva/satcom-chunk-collection huggingface dataset:
# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 3 Preliminary Work
### An Overview of IFDNAMF Framework  
To make full use of the auxiliary information to model user preference and item representation more comprehensively and accurately, and, at the same time, learn the complex non-linear high-order interaction between users and items, and then further improve the recommendation performance, this paper proposes information fusion-based deep neural attentive matrix factorization recommendation model, in which, when fusing information, element-wise product operation is employed to complete the interaction between features of different information domains in a targeted manner. In addition, the IFDNAMF adopts the attention mechanism to distinguish the importance of different cross-features on the final latent feature representation during the fusion process. Furthermore, the deep neural network is utilized to model the complex non-linear high-order interactions between users and items. Figure 1 shows the model framework. The framework is mainly composed of 6 parts: input layer, interaction layer, pooling layer, GMF layer, hidden layer, and output layer. The input layer obtains the continuous features and category features of the user and the item, including the user ID, item ID, and other attribute features, the embedding vectors of the user and the item are obtained by embedding technology, and then the cross-features are captured by pairwise element-wise product operation between these embedding vectors. After that, the cross-features are entered into the pooling layer based on the attention mechanism to fusion by different weights learned from the attention mechanism to obtain the latent features of the user and the item, and then the IFDNAMF models the second-order interaction between the user and the item in the GMF layer by performing element-wise operation on these latent features. The result from the GMF layer is trained iteratively by the deep neural network in the hidden layer to model the high-order interaction between the user and the item. Finally, the user's prediction ratings for the items are generated in the output layer, and a Top_N recommendation list based on the predicted rating is recommended to the user.
# Implicit Algorithm of the SBSP-R Model for Predicting the Non-Unique Critical State of Soils
## 5 Validation of the Implicit Algorithm of the SBSP-R Model  
### Sandy Clay  
Triaxial compression tests on the sandy clay are adopted to validate the accuracy and efficiency of the implicit algorithm of the SBSP-R model with the parameters shown in Table 3. The values of \(M\), \(\lambda\), \(\kappa\), and \(\mu\) are taken from the thesis by [PERSON] [34]. The values of \(\varepsilon_{r}\), \(c_{b}\), and \(r\) are determined according to the reference [31].  
As shown in Figure 9, the critical state, stress-strain and strength of the sandy clay are not captured if \(r=2.0\) (MCCM) or \(r=2.7\) (CCM). These behaviors are well captured by the implicit algorithm of the SBSP-R model with \(r=2.3\), which reflects the real \"distance\" between CSL and ICL (see Figure 1). Besides, the implicit algorithm requires less computation time and shows higher accuracy than the explicit algorithm.  
Figure 8: Comparisons between implicit algorithm and explicit algorithm (**a**) computation time (**b**) accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(M\) & \(\lambda\) & \(\kappa\) & \(\mu\) & \(\varepsilon_{r}\) & \(c_{b}\)/kPa & \(r\) \\ \hline
1.18 & 0.063 & 0.009 & 0.25 & 0.03 & 0 & 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters for Sandy clay.
## 1 Introduction  
In the context of academic bibliography, the tools and methods of Machine and Deep Learning constitute an alternative approach to statistical methods for time series forecasting. However, the available data with regard to their relative performance in terms of accuracy and computational demands is limited [1]. Artificial Intelligence, which is a superset of machine and deep learning, has expanded during the last decade in multiple fields of entrepreneurial and academic activity, with applications regarding the financial sector, medical care, industry, retail, supply chain, utilities, and networks [2]. Nevertheless, the classical approach with respect to the analysis and forecasting of time series is mostly based on integrated autoregressive moving average (ARIMA) models, and their various versions [3]. Moreover, a core issue of the existing academic literature in the field of machine learning forecasting techniques is the fact that the majority of publications point to adequate accuracy values, without a proper foundation, meaning without a preestablished comparison with the results of simple statistical methods and prediction models [1].  
In the present study, we will attempt a review of ARIMA, Machine Learning, and Deep Learning techniques with regard to their relative performance in time series forecasting. The strategy behind our review is to present a selection of academic papers in which the performance of machine learning, deep learning, and hybrid prediction models is compared with the performance of the ARIMA or SARIMA (Seasonal ARIMA) algorithms based on a variety of metrics. The results of these studies are presented in Tables 2-5.  
The scientific works presented in this review consist of research in the financial sector (with a special focus on the application of bitcoin price prediction), in the health sector (with a special focus on the applications of predicting parameters and variables related to the disease of COVID-19), in the field of weather forecasting (wind speed, drought, solar energy production, etc.), in the field of utility parameters forecasting (offer and demand of energy, water consumption, oil production, etc.), and in network parameters prediction (both transportation and web network traffic prediction).
## 2 Materials and Methods
### Evaluation of Secondary Oxidation Products in Beef Patties  
The secondary oxidation products of fatty acids in patties were evaluated through the thiobarbituric acid-reacting substances (TBARS) test, extracting malonyldialdehydehyde (MDA) with a 5% solution of trichloroacetic acid in water, as seen in [PERSON] et al. [27]. Samples (1 g) were mixed with a 40 mM solution of thiobarbituric acid (TBA) in water and heated at 93 \({}^{\circ}\)C for 20 min. MDA content was determined using a spectrophotometer (Cary 50, Varian, Palo Alto, CA, USA) at a wavelength of 532 nm. TBARS were quantified by comparing the absorbance with a calibration curve obtained using a solution of tetraethoxyp propane [28]. The results were expressed as mmol of MDA equivalent kg\({}^{-1}\) meat [24]. The TBARS quantification was performed at T0, T1, and T2.  
Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (SPME-GC/MS) Analysis of Beef Patties  
The VOCs emitted by the beef patties were determined by the solid phase microextraction-gas chromatography/mass spectrometry technique (SPME-GC/MS), according to [PERSON] et al. [29], with some modifications. Samples (200 \(\upmu\)g) were placed in a 20-mL glass vial, which was sealed with an aluminium cap equipped with a PTFE-septum. The VOC mixture was collected using a stable Flex SPME fibre (75 \(\upmu\)m; 2 cm long, DVB/Carboxen/PDMS) (Supelco, Bellefonte, PA, USA). The SPME fibre, conditioned for 30 min at 270 \({}^{\circ}\)C in the GC injector to eliminate residues, was exposed to the meat's headspace for 30 min at 60 \({}^{\circ}\)C. Conditioning and exposure were carried out at 60 \({}^{\circ}\)C [30]. The VOCs were analysed using a single quadrupole GC/MS apparatus (TRACE GC/MS, Thermo-Finnigan, Waltham, MA, USA). The injector was set at 250 \({}^{\circ}\)C. For the first 3 min, the split was set in splitless mode, and the fibre was kept in the injector for 30 min. The GC system was coupled with a Varian CP-WAX-52 capillary column (60 m \(\times\) 0.32 mm; coating thickness 0.5 \(\upmu\)m), and the temperature program was set according to [PERSON] et al. [31] as follows: the oven temperature was held at 40 \({}^{\circ}\)C for 8 min, then programmed to 220 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C min\({}^{-1}\) and held at 220 \({}^{\circ}\)C for 20 min. Helium was used as a carrier gas at a flow rate of 1.0 mL min\({}^{-1}\). The transfer line and ion source were set at 250 \({}^{\circ}\)C. The filament emission current was 70 eV. A mass range from 35 to 270 mz was scanned at a rate of 1.6 amu s\({}^{-1}\). The acquisition was carried out by electron impact using the full scan (TIC) mode. The VOCs quantification was performed at T0, T1, and T2.  
The VOCs were identified by comparing the mass spectra of the Wiley library (version 2.0-11/2008), by injection of authentic standards (Sigma-Aldrich), and by calculating the LRI and matching them with bibliographical indexes [29; 31]. The data were expressed as the peak percentage of total VOCs.
# Evaluation of Glottal Inverse Filtering Techniques on
## 2 Analysis Methodology and GIF Methods
### IAIF-Based Approaches
#### 2.2.2 Iop-Iair  
The IOP-IAIF variant was proposed by [PERSON] and [PERSON] [24] to improve the cancellation of the spectral tilt of the glottal source remaining from the initial estimation of the vocal tract. It is based on replacing the initial estimation of glottal source with an iterative procedure [24]. This process defines an unconstrained pre-emphasis filter order devoted to minimizing the one-delay correlation of the speech signal at each iteration. The number of iterations applied to a given speech frame is that which obtains a coefficient value lower than a given threshold (typically 0.001). In this IAIF-based approach, in addition to this initial estimation of GS based on an iterative optimization, the other processes related to the coarse estimation of VT as well as the second iteration that obtains a refinement of both GS and VT remain exactly the same as the original IAIF approach. Hence, apart from the VT order and the lip radiation coefficient \(d\), the glottal source is also modelled at the refinement stage using an all-pole filter of a certain order (\(N_{g}\geq 1\)).
# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 5 Deep Feature Extraction with Context Scene Feature Fusion and Detection
### Training of CNN and Transfer Learning
#### Training the Classifier and Hard Negative Mining Method  
For the classifier, we employed the simple linear SVM. For the training of the SVM classifier, we chose the real object sub-images as the positive sample and the sub-images with \(\geq\)0.3 IoU overlap with a ground-truth box as negatives. For each training sample, we extracted the object feature by the objNet network and the corresponding context feature by seeNet network, and then, we merged the two features as the input of the SVM classifier.  
In general, when training a classifier, the more training samples, the better. However, there is a regular problem in the field of object detection, which is the extreme imbalance of positive and negative samples, that is the positive samples with objects are relatively small, while negative samples with background are very numerous. For remote sensing images, especially large-sized images, the problem is more obvious. Too many negative samples will lead to a very slow training process for classifying algorithms, and will even be detrimental to the performance of the final classifier.  
For example, for SVM, many negative samples far away from the separating plane are almost useless for optimization. In addition, too many negative samples will make the algorithm's memory requirement too large. If a negative set with a similar number as the positive set is randomly selected,  
Figure 16: The transfer learning flowchart of the seeNet network.  
the algorithm cannot guarantee the best effect on the whole training set. If manually selecting the negative set, the cost is too large, and the subjectivity is too strong. Therefore, it is very important to search for a small representative negative set in the negative sample space. The usual strategy is to initialize a small hard negative set \(C_{t}\in D\) randomly (\(D\) denotes the entire negative sample space), train an initial model \(\beta_{t}\) with all positive samples, and classify the negative sample set \(C_{t}\). Remove the easy negative sample while searching for hard negative samples from \(D\) to add to the \(C_{t}\), until the memory limit or a threshold \(L\). The iterative updating model \(\beta_{t}\) and hard negative sample set \(C_{t}\), until \(C_{t}\) no longer changes or the iteration number reaches a certain limit, stop training. In practice, the hard negative mining method converges very quickly; usually, only a single pass over all images is required.
# Magnetic Field Effect of Near-Field Radiative Heat Transfer for SiC Nanowires/Plates
## 4 Conclusions  
In this paper, we determined the radiative transfer properties of SiC nanowires under different external magnetic field intensities, such as spectral heat fluxes, transmission coefficient, and heat transfer coefficient, and theoretically investigated the effect of an external magnetic field on the SiC nanowires on near field thermal radiative transfer. Compared to SiC plates, the transmission coefficient of SiC nanowires was higher at very large lateral wavevector values and in the hyperbolic band. In general, the external magnetic field tunes the near field radiation properties of the subwavelength magneto-optical materials. Under the appropriate parameters, external magnetic fields obviously enhance the evanescent waves in near field thermal radiation. In summary, the external magnetic field's effect on SiC nanowires in near field heat radiation has important applications like thermophotovoltaic energy conversion and radiation-based thermal management.  
Conceptualization and Methodology, H.W. ([PERSON]); Software, Validation, and Data Curation, Z.S.; Formal Analysis, Investigation, H.W. ([PERSON]); Writing-Original Draft Preparation, Z.S.; Writing-Review & Editing, H.W. ([PERSON]); Visualization, H.W. ([PERSON]); Supervision, H.W. ([PERSON]); Project Administration and Funding Acquisition, Z.S.  
Figure 8: Heat transfer coefficient versus gap distance and a filling ratio of 0.2.  
**Funding:** This work was supported by National Nature Science Foundation of China (Grant No. 61501225).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Classification and Performance Assessment
#### 2.3.2 Cross-Validation, Balancing and Performance Metrics  
The examined dataset is particularly imbalanced in favor of patients; therefore, we adopted a three-fold classification framework to establish enough control in the validation sets. Moreover, it is essential that the numbers of patients and controls analyzed by a classification algorithm are balanced; otherwise, the algorithm could learn to discriminate one class well at the expense of the other classes. Among the simplest methods to balance a dataset are random over-sampling and random under-sampling strategies [46; 47].  
We performed the random under-sampling of the RS class during training for the machine learning models, while we performed the over-sampling of the HC class for the deep learning algorithm, which usually obtains higher performances with larger sample sizes. The balancing strategies were nested into the cross-validation procedure. Finally, respiratory cycles were randomly split into training and validation sets stratified over the subjects' identification codes; in this way, the presence of respiratory cycles from the same patient in both training and validation did not occur. We repeated the cross-validation procedure 500 times.  
As a measure of performance, we evaluated accuracy, namely the rate of correct classifications, defined as follows:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{15}\]where \(TP\), \(TN\), \(FP\), \(FN\) represent true positives, true negatives, false positives and false negatives, respectively. In addition to accuracy, we also used precision:  
\[Prec=\frac{TP}{TP+FP}; \tag{16}\]  
classification error of the class HC:  
\[EHC=\frac{FP}{TN+FP}; \tag{17}\]  
and classification error of class RS:  
\[ERS=\frac{FN}{TP+FN}. \tag{18}\]  
All the data processing and statistical analyses were performed in Python version 3.7 ([[https://www.python.org/downloads/release/python-370/](https://www.python.org/downloads/release/python-370/)]([https://www.python.org/downloads/release/python-370/](https://www.python.org/downloads/release/python-370/))) and R version 3.6.1 ([[https://www.r-project.org/](https://www.r-project.org/)]([https://www.r-project.org/](https://www.r-project.org/))).
## 2 Theoretical Background for Spherical Localization Based on Attenuation of EM Waves
### Signal Identification Using Channel Allocation  
Localization within a horizontal plane requires at least three beacons. An important aspect in RSS-based spherical localization is that the receiver has to assign the RSSs to the respective emitting beacons. In principle, a receiver can identify the signal source by time scheduling [14]. However, this requires clock synchronization, which is usually a challenging task for underwater applications [11].  
An alternative approach for beacon identification is channel assignment, which is deployed in [4; 5; 6]. Hereby, each beacon sends an EM signal at a unique frequency. The resulting superposed signal is then measured by the receiver. By applying a fast Fourier transformation (FFT) on the receiver side, each RSS can be allocated to the respective beacon. This technique increases the update rate significantly, because beacons can transmit their signals simultaneously and do not need to wait for a scheduled time. Moreover, in each cycle, the FFT provides access to all RSS values. This is shown in Figure 5 for two beacons with different emitting frequencies. The prior knowledge about the beacons' frequencies allows allocating the determined RSS values to the beacons and, thus, based on the corresponding URSM, to calculate the range between the each beacon and the receiver.  
Figure 4: The underwater range sensor model (URSM) is applied to determine the range \(R_{i}\) between the receiver unit and the \(i\)-th beacon. It maps the difference between received EM wave power \(S_{\text{R},i}\) and transmitted EM wave power \(S_{\text{T},i}\) to the range \(R_{i}\).
## 3 Enhancing Security Through Joint Precoding and Artificial Noise Design
### Secrecy Rate Optimization Through Joint Precoding and Artificial Noise Design
In the multipath model, [PERSON] might receive signals through multiple paths, and the precoding needs to consider all possible paths to design more complex interference strategies. Although the implementation of these objectives changes with different channel characteristics, the essence of precoding remains the same: it is a spatial resource optimization strategy aimed at enhancing physical-layer security. ```
1. Set the iteration index \(d=0\), and the maximum iteration number \(d_{\text{max}}\). 2. Set the initial power-control factor \(\zeta_{0}\), and the error tolerance \(\varepsilon\). while\(d<d_{\text{max}}\) and \(\left\|R_{L,d}-R_{L,d-1}\right\|^{2}>\varepsilon\)do
3. Set \(d=d+1\). 4. Compute \(\mathbf{P}_{t}\) by substituting \(\zeta_{d-1}\) into Algorithm 1, and set \(\mathbf{P}_{d}=\mathbf{P}_{t}\). 5. Compute \(\zeta_{d}\) by substituting \(\mathbf{P}_{d}\) into (20). 6. Compute \(R_{L,d}=R_{L}(\mathbf{P}_{d},\zeta_{d})\) based on (14). 7. end while Output:\(\mathbf{P}=\mathbf{P}_{d}\), \(\zeta=\zeta_{d}\). ```  
**Algorithm 2** An iterative algorithm for the joint optimization of \(\mathbf{P}\) and \(\zeta\) for maximizing the secrecy rate.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 8 Applications
### Environmental Monitoring
This review also provides the basis of fabrication techniques of SPR-based FOPS such as stack and draw method [135], drilling [136], sol-gel casting [137], slurry casting [138], and extrusion method [139]. The conventional stack and draw method is generally used to fabricate PCF-based SPR FOPS with the least transmission loss of 0.18 dB/km at 1550 nm wavelength [140]. In every MOF-based FOPS, the complexity of the geometric structure is the most common issue. However, the fabrication of the asymmetric geometry of the sensor is a challenging process. Moreover, the metal coating on the surface of circular air holes in MOF-based sensors is one of the common difficulties that restrict the experimental realization of FOPS. Table 2 provides the various metal coating techniques such as sputtering, side-polishing, dip-coating, thermal evaporation, electroless plating, wet-chemistry deposition, and chemical vapor deposition. By using the chemical vapor deposition technique, the complex metal coating on the surface of the circular air holes has been reported [141, 142]. Furthermore, the metal coating in the inner surface of the circular air holes is achieved by Tollens' reaction [143, 144]. Among the various metal-coating techniques, the chemical method is the most common and is cost-effective. The self-calibration process is essential to characterize and utilize the sensor. Various analytes have been tested by exchanging with water and filling the nitrogen gas [143]. The fabrication approaches employed for the fabrication of SPR-based FOBS have been reported [15]. In practical applications, several SPR-based FOBS play a role in the chemical, biological. and medical fields. In 2015, a fast bioassay for determining the infliximab concentration in serum using an in-house developed SPR-based FOBS was reported [145]. In 2018, a red-green dual color SPR-based FOBS using a smartphone was developed for real-time biodetection by monitoring the binding of IgG and protein A [146]. These portable devices are attractive for point-of-care and remote detection of biomedical and environmental targets. In lab-on-fiber technology, a new vision for chemical and biological sensing has been proposed to determine the direct impact of localized cancer treatment [9]. On account of these developments, we believe that these SPR-based FOBS would contribute greatly to enhancing industrial applications in several areas of the physical, chemical, biological, and medical fields. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Configuration and Its** & **Operating** & **Functional Materials** & **Performance with Sample** & **Ref. ** \\