esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 3 Related Work
### Deep Learning-Based Recommender Systems  
The main problem of the recommender task is the non-linear interaction and large amount of data between users and items, making feature learning a difficult task. This in turn makes the learning process slow, as it requires much more time to train. In addition, it affects the computational power. Therefore, we will employ deep learning frameworks that can handle the above-stated challenges and improve the quality performance of the RSs. For instance, the author of [21], in Neural Collaborative Filtering (NCF), showed that the inner product could be automatically learned using the multi-layer perceptron (MLP). This implicitly learns the non-linear feature representation between users and items, which is the MF-based models' main weakness. DeepFM [25] integrated factorization machine (FM) and MLP to build an end-to-end framework for learning both low and high-dimensional feature data. Due to this, many companies employ RSs to improve the user experience and boost sales on their websites. For example, [PERSON]. [26] developed a video recommendation algorithm based on deep neural networks on YouTube. The authors of [27] stated that Netflix RS has an 80% impact on consumer decisions. Most of these approaches outperformed traditional recommendation models significantly. As a result, we can observe that deep learning has sparked a significant change in the recommender sector. To solve the aforementioned shortcomings, we introduce a dual deep learning architecture to determine the latent factors. Like that of NCF, our approach does not learn the interaction record automatically; instead, we use dual deep learning and embedding-based techniques to extract the features of users and items separately. We then predict the ratings using the inner product.
# Research on Multi-Physical Field Coupling Simulation of Local Electrochemical Machining
## 3 Simulation Result Analysis
### The Influence of Two-Phase Flow Field and Temperature on the Conductivity Distribution
#### 3.1.2 Conductivity Distribution  
Figure 8 shows the conductivity distribution and the evolution process of local electrochemical machining of the pre-shaped convex contour at different processing times of 1 s, 250 s, 500 s, and 744 s. The legend represents the conductivity. As only the region where the electrolyte is present has a conductivity that is not zero, the distribution of conductivity is similar to the distribution of a two-phase field. As the temperature in the processing area has a certain increase compared with the initial temperature, the maximum conductivity of the electrolyte will be greater than 13 S/m, and it increases slowly with the feed depth. The maximum electrical conductivity of the four time points is, respectively, 13.7 S/m, 13.8 S/m, 13.9 S/m, and 14.1 S/m, which are usually distributed in the machining gap and the insulating cavity, because the bubble rate here is 0 and the temperature is higher than the initial temperature. Due to the high bubble rate of the electrolyte flowing on the smooth surface of the workpiece, the electrical conductivity is often less than 13 S/m. For the surrounding air, the conductivity is 0 S/m.  
Figure 6: Gas–liquid two-phase flow field distribution: (**a**) t = 1 s, (**b**) t = 250 s, (**c**) t = 500 s, and (**d**) t = 744 s.
# Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities
## 1 Introduction  
Hyperspectral imaging has its origins in remote sensing [1] and has evolved into high-resolution imaging systems used in laboratory settings [2]. The goal of the project was to transition laboratory instrumentation back to field use to allow the acquisition of high resolution hyperspectral fluorescence images irrespective of solar lighting conditions. A specific laboratory objective was to address food safety risks associated with fecal contamination in produce fields. Our laboratory developed laser-induced fluorescence imaging technologies that can be used to detect fecal materials [3; 4; 5; 6; 7; 8; 9; 10]. The cart-mounted system described herein was developed to be able to detect fecal materials in produce fields using these technologies.  
Food-borne illness is an ever-present problem throughout the world. According to the Centers of Disease Control and Prevention (CDC), food-borne illness is responsible for the hospitalization of 128,000 Americans and 3000 American deaths each year [11]. Of the 9.6 million annual incidents of food-borne illness in the United States, 2.2 million (22%) are attributed to leafy-green produce [12]. Leafy green produce can be contaminated prior to harvest, and one of the most common sources ofcontamination is fecal material [13]. To reduce the risk of food-borne illness, the current practice is to have produce fields inspected by workers for fecal material and signs of animal intrusion before harvest [14]. The objective of this project is to develop an autonomous system to augment this current practice.  
CALGMA (California Leafy Green Products Handler Marketing Agreement) recommends that both a pre-planting and a pre-harvest assessment be performed to check for fecal material or signs of animal intrusion. Signs of animal intrusion include damaged produce, evidence of feeding, fur, feathers, animal tracks, and other signs of animals in sufficient quantities. Problem sites are marked and nothing within a designated distance of the site is harvested [14].  
Our laboratory has developed methods that use fluorescence responses to detect fecal materials [3; 4; 5; 6; 7; 8; 9; 10]. One method utilizes laser-induced fluorescence and time-resolved hyperspectral line-scan imaging [5; 6; 7; 8; 9; 10]. To use this technology in produce fields, a field deployable mounting system and a motorized cart are needed. Therefore, the goals of this study were to modify existing imaging technologies for field use, to develop a motorized cart, and to design and build a semi-autonomous system to facilitate the acquisition of hyperspectral data in fields.  
This paper is organized as follows. In Section 2, the materials and methods used to develop the cart with camera and laser mount will be discussed. The cart design and field results are discussed in Section 3. Validation of the results and implications to future work are also provided.
# MM-Wave Phased Array Quasi-Yagi Antenna for the Upcoming 5G Cellular Communications
###### Abstract  
The focus of this manuscript was to propose a new phased array antenna design for the fifth generation (5G) mobile platforms. Eight elements of compact Quasi-Yagi antennas were placed on the top portion of smartphone printed circuits board (PCB) to form a beam-steerable phased array design. The \(-10\) dB impedance-bandwidth of proposed 5G smartphone antenna spans from 25 GHz to 27 GHz providing 2 GHz bandwidth with less than \(-16\) dB mutual coupling function. A coax-to-microstripline with a truncated crown of vias around the coaxial cable was used as a feeding mechanism for each radiation element. An Arlon Ad 350 substance with properties of \(\varepsilon=3.5\), \(\delta=0.003\), and h = 0.8 mm was chosen as the antenna substrate. The proposed phased array antenna provides wide-angle scanning of \(0^{\circ}\)-\(75^{\circ}\) with more than 10 dB realized gain levels. For the scanning angle of \(0^{\circ}\)-\(60^{\circ}\), the antenna array provides more than 90% (\(-0.5\) dB) radiation and total efficiencies. In addition, the specific absorption rate (SAR) function and radiation performance of the design in the presence of the user-hand/user-hand have been studied. The results validate the feasibility of the proposed design for use in the 5G handheld devices. Furthermore, using the presented Quasi-Yagi elements, the radiation properties of \(2\times 2\), \(4\times 4\), and \(8\times 8\) planar arrays were studied and more than 8.3, 13.5, and 19.3 dBi directivities have been achieved for the designed planar arrays. The results show that the designed arrays (linear & planar) satisfy the general requirements for use in 5G platforms.  
5G systems; end-fire radiation beam; cellular communications; phased array; mm-Wave applications; Quasi-Yagi antenna +
Footnote †: journal: Applied Sciences  
_Article_
# Concept Justification of Future 3 DPVS and Novel Approach towards its Conceptual Development
## 3 Conceptual Design Approach for 3 DPVS
### Potential Methodology and Tools Supporting 3 DPVS Conceptual Design  
Through the steps discussed in the previous section, a 3 DPVS conceptual design can be undertaken. After illustrating the innovation and design aspects of 3 DPVS, the conceptual design of 3 DPVS can be separated into three stages, each containing a chief task. Design initiation is for generating system analysis and problem statement, and the chief task considers how to analyze the problems in existing or previous design solutions as well as prepare materials for the conceptual design. Stage two is about concept generation and problem solving, the core task of which is concept innovation, and appropriate concepts for the required 3 DPVS will be generated. Stage three regards concept evaluation, whose task is to evaluate and validate the generated concepts of 3 DPVS in the previous stage as well as finalizing concept scenario creation. Since the previous scaffold design does not use formal methodologies, how to choose proper ones and apply them in a 3 DPVS context could possibly be considered as novel and inspirational.
# Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 Technologies
## 2 State of Art
### ETSI ITS-G5 Technology
MAPEM messages work together with SPATEM and are used to spread information about any layout intersection change. These messages are broadcasted when the road layout is changed: for example, a change in the crossing priority at a traffic lights intersection [11]. Figure 1 shows the ITS application classification which groups them into Infotainment and Comfort, Traffic Management, Road Safety and Autonomous Driving [14]. The ITS-G5 protocol stack includes a management layer that handles all protocol stack layers and a security layer that provides security and privacy services to all other layers. There are several scientific works about the development of ITS-G5 applications related to traffic safety which contribute to the increase of road infrastructure security. In [15], the authors propose a system to collect regular weather forecasts for specific road stretches to deliver and display road weather data to drivers, thus increasing traffic fluency and safety. In [16], the authors present the TRUST project, where the main goal is to develop a weather monitoring system for road infrastructures, to identify potential driving hazards and alert generation for cars and traffic management centres. In [17], the authors propose a study to assess and optimise the performance of ITS-G5 for time-critical safety conflict scenarios between vehicles and pedestrians. Figure 1: Intelligent transport system (ITS) application classification, source [14].
## 3 Simulations and Discussion
### Higher-Resolution Forecast Results with the DVI
With a more southward position following the best track, the typhoon of BND_H before landfall may encounter weaker mountain blocking than that with a northward deviated track of BND. Compared to the best track data, all the experiments showed weaker vortices but with rather consistent tracks before moving closer to the north. Figure 13: (**a**) The CWB best track (black line) and the forecasted tracks for BND_H (blue line), BND_RVH (pink line), and BND_RPH (green line) for Typhoon Nepartak. (**b**) as in (**a**) but for the track errors with forecast time, and (**c**) as in (**a**) but for the central sea-level pressure. The numbers following the case names indicate the number of cycle runs. to the terrain. Given the more consistent track and intensity forecasts before landfall, the forecast of accumulated rainfall over Taiwan is also improved for both DVI experiments (figures not shown). Figure 14 shows the differences in temperature and specific humidity at 850 hPa for the BND_RVH experiment from the ERA-Interim. The differences are similar for both BND_RPH and BND_RVH (not shown). Compared to Figure 11 for BND_RP, the temperature differences are reduced, especially to the north of the outer typhoon and east of the inner typhoon as well as far southeast of the typhoon. For moisture, the major reduction is found in the inner typhoon where the wetter condition in BND_RV over the vortex core was largely decreased in BND_RVH. The averaged RMSEs of temperature and specific humidity verified against the ERA-Interim over the verification area at 0000 UTC 7 July 2016 for BND_H, BND_RVH, and BND_RPH are shown in Figure 15. Compared to BND_H, the RMSEs of temperature and moisture were reduced throughout the troposphere, especially above 700 hPa, for both DVI experiments. A greater reduction at the 72 h forecast is obtained in the mid-upper troposphere, which indicates that the DVI not only enhances the initial inner typhoon but also helps produce a more persistent and stronger development as seen in Figure 13c. The relative impacts for V-match and P-match are mixed in both temperature and moisture differences. It appears that P-match gives a better temperature forecast above 700 hPa but slightly degraded moisture forecast below 500 hPa. Which match is preferred for typhoon forecasts remains inconclusive.
## 2 Materials and Methods
### Evaluation Methodology
#### 2.1.2 Pilot Site Evaluation  
This phase takes place after all points from the evaluation preparation part are agreed upon and deployed. Appropriate events should be arranged at the pilot site or on the evaluation route. There is always an effort to create the event (represented by a use case in terms of C-ITS) in such a way that its location is not affected by other circumstances and that it is really taking a place in a given area. Inappropriate circumstances could significantly affect the results of the evaluation in a negative sense.  
The typical course at the pilot site for the evaluation driver is as follows:  
* meeting of drivers at the agreed time and place,
* filling in the relevant pre-ride questionnaires,
* first evaluation ride (with C-ITS off),
* second evaluation ride (with C-ITS on),
* filling in the relevant post-ride questionnaires.  
More information about the questionnaires can be found in Section 2.1.3. User Acceptance in this paper.  
The aim of the evaluation is to find out the benefits resulting from the use of C-ITS services but also their shortcomings, to gain knowledge about the possibility of future improvements. For this reason, ideally, the driver should travel the evaluation route twice. In the first case, he will not have any C-ITS elements, mainly HMI, to inform him about the event. During the second evaluation ride, these systems will be switched on and the driver will be informed of the event in advance via C-ITS. The goal of this procedure is to compare drivers' reactions to the traffic events (use cases) using C-ITS services and the baseline behavior without any service. Impact assessment addresses these issues in detail in Section 2.1.3. Impact Assessment and its example makes up part of Section 3.1.3. Impact Assessment.  
Evaluation results can be affected by many factors. Such factors include, for example, weather changes (rain, strong wind, direct sunlight), light visibility (day/night), situation clarity (visibility at the intersection, objects blocking the view), used car, traffic restrictions (speed, priority), traffic flow (low/high), and road topology (urban/extra-urban). These factors can influence driver behavior and lead to misleading results. For this reason, it is recommended that the evaluation be performed under the same conditions on the same test circuit for all drivers. In this way, the influence of random factors can be reduced and thus approached more precisely in determining the true impact of the tested system on the driver.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 6 Conclusions  
In order to minimize the number of tag-to-tag collisions for RFID systems, we propose an anti-collision technique and data structure that employ the TD technique. In the proposed system, since only specific tags transmit the data to the reader in a specific time slot, interference signals from other tags are effectively suppressed. Therefore, when we simultaneously employ multiple tags and a single reader on an industrial site such as a construction site, the proposed RFID architecture improves the data reliability and performance, enabling the identification of information for specific objects or materials. We illustrated the interference suppression performance of the proposed RFID system by performing computer simulations, and compared the results with a conventional system without the anti-collision technique and an anti-collision RFID system that is based on the gold-code sequence. In the future, we will explore in greater detail some practical applications of the proposed RFID system.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-2015R1D1A1A01058827). This study was supported by research fund from Chosun University, 2016.  
[PERSON] and [PERSON] conceived the idea for the proposed system and data architecture. [PERSON] developed the scenarios used for the computer simulation and performed the simulations. All of the authors were involved in the analysis of the proposed architecture, equations, and theoretical approach.  
The authors declare no conflict of interest.
# Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors
## 3 Wavelet Transform Parameters Selection
In [67], the decomposition level and the wavelet function are selected in order to minimize the percentage root mean square difference criterion. For fault detection, there is no optimal way to select the best wavelet function for a specific application. In practice, this can be carried out by comparing the shape of the fault to be detected with the wavelet function to be used. Symmetric wavelets were found to be more effective in singularity analysis, for example, a very narrow pulse-like anti-symmetric wavelet, such as db10 or any higher-order of Daubechies family where it was also found to perform well [59]. Specifically, in crack detection, the maximum cross-correlation coefficients computed between the fault signal and the different wavelet functions from the library will give the optimal wavelet function, as shown in [68]. The Daubechies family, and more specifically the Daubechies order six has been widely used for crack detection due to its proven effectiveness in this field [33; 57; 69].
## 3 Results and Discussion
### Physiological Characterization
#### 3.2.1 Properties of pH Tolerance of the Selected Yeasts  
The low pH tolerance of the tested strains was assessed under varying pH conditions. The results are shown in Figure 2A. The experimental results demonstrated that _Pichia kudriavzewii_ GAAS-JG-1 exhibited significantly superior growth ability compared to the control strains under all tested pH conditions. When the pH dropped to 2, the OD\({}_{600}\) value of _Pichia kudriavzewii_ GAAS-JG-1 reached 1.18 \(\pm\) 0.09, whereas the OD\({}_{600}\) values of the control strains were 0.51 \(\pm\) 0.04, 0.57 \(\pm\) 0.01, and 0.49 \(\pm\) 0.01, respectively, indicating that _Pichia kudriavzewii_ GAAS-JG-1 possesses strong tolerance to extreme acidity. Moreover, as the pH increased, the growth ability of _Pichia kudriavzewii_ GAAS-JG-1 gradually improved, maintaining relatively high and stable OD\({}_{600}\) values (1.81-1.90) within the pH range of 3-6. In contrast, the control strains exhibited significantly inhibited growth under pH 2-3 conditions, with OD\({}_{600}\) values below 1.6. These results suggest that _Pichia kudriavzewii_ GAAS-JG-1 possesses excellent adaptability to low-pH conditions. Even in an extremely acidic environment (pH 2), _Pichia kudriavzewii_ GAAS-JG-1 maintained a robust growth performance, which was notably superior to the control strains. This finding highlights the suitability of _Pichia kudriavzewii_ GAAS-JG-1 for improving the fermentation of high- acidity fruit wines. The ability of _Pichia kudriavzewii_ GAAS-JG-1 to tolerate extremely low-pH conditions (as low as pH 2) provides theoretical support for its industrial application in acidic environments. Additionally, this characteristic suggests that _Pichia kudriavzewii_ GAAS-JG-1 may possess unique physiological regulatory mechanisms to cope with acid stress, as also reported in previous studies highlighting the acid tolerance of _Pichia kudriavzewii_ strains in fruit wine fermentations [11; 12].
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 2 Materials and Methods
### Dcase 2020 Baseline System  
The challenge provides a baseline system as a benchmark of SED performance [33]. Such system is based on a Convolutional Recurrent Neural Network implemented in _pytorch_ ([[https://github.com/turpaultn/dcase20_task4](https://github.com/turpaultn/dcase20_task4)]([https://github.com/turpaultn/dcase20_task4](https://github.com/turpaultn/dcase20_task4)) (accessed on 6 December 2021)) and trained using the Mean Teacher method [34] for semisupervised learning. This method defines a teacher network identical to the student, but which weights are computed as an exponential moving average of the student weights. To allow the network to learn from the different types of annotations present in DESED, including strongly labeled, weakly labeled, and unlabeled data, a classification loss and a consistency loss are defined for weak and strong predictions. Whereas the classification loss consists on the binary cross-entropy between the student predictions and the annotations of labeled data, the consistency loss measures the mean square error between the predictions of teacher and student. Therefore, considering that the computation of this consistency loss does not require ground truth annotations, it allows the network to leverage unlabeled data.  
The CRNN model is composed of 7 convolutional layers and 2 bidirectional recurrent layers. Each convolutional layer is composed by a different number of blocks that perform three operations: a 2D convolution using a \(3\times 3\) kernel, a ReLU (Rectified Linear Unit) activation function [35], and an average pooling. The convolutions are meant to learn transformations over the input features, while the pooling operations reduce the size of the feature representations in each layer. The recurrent layers are formed by bidirectional gated recurrent units (BiGRU) [36], which are intended to further model the output of the convolutional stage. Additionally, an attention pooling module is used to infer the temporal locations of events using weak labels. The system is fed with the mel-spectrogram features of the audio segments, and the output of the network is a sigmoid layer with 10 outputs, one for each target category. For a given audio segment, this layer provides 10 time sequences, containing the score values (from 0 to 1) of each target category along the duration of the segment. Finally, a threshold is applied to these score sequences, and the resulting binary vectors are smoothed by means of a median filter of approximately 450 ms. A threshold value of 0.5 is used to compute \(F_{1}\) score. A visual representation of the baseline system structure is presented in Figure 1.