esa-sceva/satcom-chunk-collection huggingface dataset:
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 1 Introduction
In summary, the contributions of this paper are that, firstly, the theory of FCS-MPC is introduced into the collision avoidance of the USV, and the combination of path planning and the control system is realized to improve the safety and rapidity of collision avoidance for the USV; secondly, combining the characteristics of USV and the theory of FCS-MPC, a safe and fast CAS is proposed; thirdly, it is more practical as the output of CAS is the thruster speed and propulsion angle, instead of force and torque; finally, the CAS is verified in different static and dynamic environments, and the real environment model of collision avoidance is established from the electronic chart. The rest of the paper is organized as follows. Section 2 introduces the hierarchical structure and technology of the USV system. Section 3 establishes the mathematical model of the USV and applies the FCS-MPC method to CAS. Section 4 is the simulation result verification. Finally, Section 5 concludes the paper, summing up comments and future works about the CAS of USV.
## 3 Time-Frequency Representation
### Continuous Wavelet Transform  
The continuous wavelet transform of the signal \(x(t)\) is expressed as:  
\[\text{CWT}(a,b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}x(t)\overline{w} \bigg{(}\frac{t-b}{a}\bigg{)}dt \tag{2}\]  
Figure 10: Time\(-\)frequency image.  
where \(\overline{w}\) is the complex conjugate of \(w\), \(w\) is the mother wavelet function that satisfies the admissible condition, \(a\) is the expansion factor, and \(b\) is the translation factor [29]. The commonly used mother wavelet function is Morlet wavelet, and its expression is:  
\[w(t)=\pi^{-\frac{1}{2}}\mathrm{e}^{2\pi t\mathrm{i}t}\mathrm{e}^{-\frac{t^{2}}{2}} \tag{3}\]
# Zero-Shot Learning for Cross-Lingual News Sentiment Classification
## 3 Datasets
### SentiNews Dataset in Slovene  
We used the publicly available SentiNews dataset (available at [[https://www.clarin.si/repository/xmui/handle/11356/1110](https://www.clarin.si/repository/xmui/handle/11356/1110)]([https://www.clarin.si/repository/xmui/handle/11356/1110](https://www.clarin.si/repository/xmui/handle/11356/1110))) [3], which is a manually sentiment-annotated Slovenian news corpus. The dataset contains 10,427 news texts mainly from the economic, financial, and political domains from Slovenian news portals (www.24 ur.com, www.dnevnik.si, www.finance.si, www.rtvslo.si, www.zurnal24.si), which were published between 1 September 2007 and 31 December 2013. The texts were annotated by two to six annotators using the five-level Likert scale on three levels of granularity, i.e., on the document, paragraph, and sentence level. The dataset contains information about average sentiment, standard deviation, and sentiment category, which correspond to the sentiment allocation according to the average sentiment score. The dataset statistics are:  
* 10,427 documents;
* 89,999 paragraphs;
* 168,899 sentences.  
For our news classification experiments, we used the document-level annotations, with 10,427 news articles and an imbalanced distribution of 3337 (32%) negative, 5425 (52%) neutral, and 1665 (16%) positive news, where the sentiment category corresponds to the sentiment allocation according to the average sentiment score. For intermediate training, we also leveraged paragraph-level annotations.
## 4 Materials and Methods
### EEMD-Based Bearing Fault Diagnosis Using Energy Distribution Features of IMFs  
After the signal is decomposed by EEMD, multiple IMF components and a residual quantity are obtained. Each IMF component contains the characteristic information of bearing faults. Fault diagnosis is carried out using the energy distribution of the IMF component as the feature vector, and its fault diagnosis method and process can be expressed as follows:  
_Step 1_ : The envelope demodulation of the bearing vibration signal based on the Hilbert transform is carried out to obtain the envelope signal of the vibration signal.  
_Step 2_ : The envelope signal is decomposed by EEMD to obtain the IMF component and the remainder.  
_Step 3_ : The first 10 IMF components are selected and the total energy of each component is calculated as  
\[E_{i}=\int_{-\infty}^{+\infty}|c_{i}(t)|^{2}dt=\sum_{i=1}^{n}|C_{i}|^{2} \tag{7}\]  
where \(c_{i}(t)\) is the \(i\) IMF component, \(C_{i}\) is the amplitude of discrete points, and \(n\) is the number of sampling points.  
Based on the energy of each IMF component, the normalized energy distribution characteristic vector ED can be expressed as  
\[E_{i}=\int_{-\infty}^{+\infty}|c_{i}(t)|^{2}dt=\sum_{i=1}^{n}|C_{i}|^{2}. \tag{8}\]  
By using the energy of each IMF component, the normalized energy distribution characteristic vector can be constructed as  
\[ED=[E_{1}\;E_{2}\;\ldots\;E_{3}]/\sum_{i=1}^{10}E_{i} \tag{9}\]  
_Step 4_ : The ED values are arranged in sequence and fault diagnosis is performed by analyzing their distribution characteristics. The judgment criterion is defined as follows: If the ED values exhibit a strictly decreasing trend, the bearing condition is considered normal. Conversely, if the ED values deviate from a strictly decreasing order, it suggests the presence of a fault in the bearing.  
load, where the experimental environment involved short-term continuous monitoring of the bearing operating status using accelerometers. The data are available online: [[http://csegroups.case.edu/bearingdatacenter/pages/download-data-file/](http://csegroups.case.edu/bearingdatacenter/pages/download-data-file/)]([http://csegroups.case.edu/bearingdatacenter/pages/download-data-file/](http://csegroups.case.edu/bearingdatacenter/pages/download-data-file/)) (accessed on 2 March 2020).
## 3 Proposed Method  
### Model Assumption  
To solve the problem that the degradation rate of products is slow at the beginning of the step-stress ADT, an assessment method for the SDSADT considering random effects and detection errors is proposed in this article. This article is based on the following three assumptions:  
1. In the SDSADT, the degradation path of the product follows the IG process. Without loss of generality, it is assumed that the performance degradation increases monotonically with time.
2. The failure mechanism of the product under accelerated stress levels is unchanged and the same as under normal stress levels.
3. The remaining life of the product depends only on the current stress level and the parts that have accumulated degradation. It has nothing to do with the accumulation method [25].  
Based on the above assumptions, this article proposes a new product storage life assessment method under the SDSADT. Firstly, the proposed method takes into account the effects of factors such as material and processing and introduces the Gamma distribution to characterize the randomness of and difference in the product degradation path. Secondly, due to the influence of uncertain factors such as detection equipment and environment, there are detection errors in the detection results. Therefore, the normal distribution is used to describe the detection errors of performance parameters. Based on this foundation, the MC method is used to estimate the model parameters and reliability. Compared with traditional parameter estimation methods, this approach can significantly improve calculation accuracy and efficiency.
## 4 Discussion

Table 2: Microfauna changes (n = 3) in the acidic and alkaline reactors. T = 20 \({}^{\circ}\)C, pH 8.5). These concentrations are high enough to cause a toxic effect on the ASP [5,6]. Typical municipal wastewater has approximately 30 mg/L of total nitrogen (TN), temperature = +20 \({}^{\circ}\)C, and pH = 7-8 and contains 0.14-1.38 mg/L FA [31]. A further increase in pH and TN in municipal wastewaters could be toxic for NOB due to the formation of excess FA. An inhibition of AOB can be enhanced by the limitation of inorganic carbon (IC) source for several days [21]. Hence, simultaneous limitation in IC source and an increase in FA concentration can lead to destruction of the first-step nitrification and, consequently, to the nitrogen removal process failure. pH is a technologically important parameter in the biological wastewater treatment process, especially in nitrification and denitrification efficiency. pH 6.5 is more destructive for the activated sludge process than pH 8.5. Generally, the increase in ammonia concentration is caused by AOB inhibition in the activated sludge in acidic reactor (Figure 1a). The same trend has been shown earlier [2]. Additionally, sludge foaming was observed in this reactor at 72 h of inhibition (Figure S3). Therefore, pH 6.5 is not suitable for the nitrification process. Experiments with alkaline pH were associated with the formation of high FA concentration and showed a potential risk of inhibition of second-step nitrification (conversion of nitrites to nitrates). However, the acidic reactor showed strong inhibition of AOB, probably caused by FNA formation. High ammonia levels and a decrease in pH leads to _zoogloea, gymmoehae_, and _Chilodonella_ sp. development; this affects sludge settleability, and sludge washout from low FA reactor occurred. Hence, the organic load increased after sludge washout, and sludge foaming was observed. Increase in pH caused in filamentous sludge bulking was due to filamentous microorganisms' high pH preference. The differences in IC concentration in the influent of both reactors contributed by pH regulation have a significant effect on biomass. The high IC concentration in effluent acted as a buffer for the AOB, and the further inhibition process was not intensified. The negative influence on both AOB under acidic pH can be explained by the low inorganic carbon source to buffer nitrification process acidity. Results showed that nitrification (NIT) was high and nitrite accumulation ratio was low (NAR) in both reactors. As reported, nitrification is the most efficient in a narrow pH range of 7.8-8.0 [32]. Acidic pH enhances free nitrous acid (FNA) production, which is considered highly toxic for the biological nitrogen removal process. Even 0.0013 mg HNO\({}_{2}\)/L can inhibit AOB bacteria [33]. Consequently, AOB and NOB bacteria inhibition with acidic pH have different outcomes when compared with alkaline pH. pH, temperature, and ammonium are useful indicators of a good nitrification process. These parameters can be used as quality parameters for monitoring inhibition of bioreactors and in online early warning systems to reduce potential failures in a WWTP.
## 2 Materials and Methods
### Methodology of ResRAD-ONSITE  
Argonne National Laboratory developed the RESRAD family of codes in the 1980s to analyze potential human and biota radiation exposures from the environmental contamination of RESRAD materials. The codes use pathway analysis to estimate radiation exposure and associated risks and develop cleanup criteria for radionuclide concentrations in the contaminated source medium. RESRAD uses a pathway analysis method to track radionuclide transport in the environment (soil, water, and air) and model how these radionuclides reach the receptor through direct exposure, ingestion, and inhalation. The code uses various dose coefficients, such as age and gender-specific DCs and methodology-based DCs from the International Commission on Radiological Protection (ICRP) publications 26, 30, and 60 [21; 22; 23]. The RESRAD-ONSITE computer code estimates the radiological dose and excess cancer risk to an individual who is exposed while living or working in a radionuclide-contaminated area. The RESRAD-ONSITE code has continuously improved in response to feedback from users and sponsors. The RESRAD team has participated in many national and international model intercomparison studies in which both hypothetical and actual contaminated site-based scenarios were analyzed using RESRAD-ONSITE [24]. We assumed a resident scenario with a person settling on a former landfill site to simulate radiological dose using RESRAD-ONSITE. We assumed an urban resident scenario where the resident does not consume any food or water from the contaminated site because most South Koreans live in suburban areas. The pathways considered were external irradiation and inhalation. The code uses a Gaussian plume model, where the radionuclide concentration and plume are constant and do not change with time.
# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
## 4 FiQ--A Fastener System Inspection and Quantization Method Based on Mask FRCN
### A Quantization Method Based on Cluster Method to Estimate Fastener Hat and Shim Status  
This study proposes a clustering-based image feature calculation method to solve problems such as fastener shift, fastener hat damage/loss, and shim shift. The method can be represented by the following formula:  
\[\textbf{X}=\{x_{1},x_{2},...,x_{n}\}. \tag{8}\]  
Here, X represents the existing dataset, and \(x_{i}\) represents the feature vector of the \(i\)th image. We extract features of each component to obtain the feature vector of each image. Then, we perform clustering analysis on these feature vectors to obtain cluster centers, as follows:  
\[\textbf{C}=\{c_{1},c_{2},...,c_{k}\}. \tag{9}\]  
Here, **C** represents the set of cluster centers and \(c_{i}\) represents the vector of the \(i\)th cluster center. We use segmentation and connected domain similarity calculation to obtain similarity clustering center values for fasteners, shims, and fastener hats. For the looseness of shims, we cluster the deflection angle of the connected domain and calculate the deviation from the center value to determine if there is looseness. For the looseness of fasteners, we determine the length of the fastener hat connecting line and its distance to the clustering center to judge the degree of looseness.  
Specifically, we calculate the distance between each image's feature vector and the cluster center to determine if it is abnormal. For fasteners and shims, we use Euclidean distance to calculate the following distance:  
\[d(x_{i},c_{j})=\sqrt{\sum_{m=1}^{M}(x_{im}-c_{jm})^{2}}. \tag{10}\]Here, \(M\) represents the dimension of the feature vector. For fastener hats, we use the Manhattan distance to calculate the distance, as follows:  
\[d(x_{i},c_{j})=\sum_{m=1}^{M}|x_{im}-c_{jm}|. \tag{11}\]  
We set a threshold to determine if it is abnormal. If the distance is less than the threshold, it is considered abnormal.  
In summary, this study proposes a clustering-based image feature calculation method, which can be represented by the formula. This method can effectively detect fastener shift, fastener hat damage/loss, and shim shift, providing a reliable detection method for industrial production. Following are the fastener system inception details based on a cluster idea.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 3 Connected Vehicles and Data Availability
### Basic Safety Message Data  
Connected vehicle safety application are greatly dependent BSM to exchange the core data that describe vehicle status, position, and motion among vehicles, as well as between vehicle and Infrastructure. The BSM has been designed with two parts (see Figure 3, for the format of the BSM). The Part I contains the core data information and is transmitted regularly. The second part consists of other data elements that differ according to the vehicle model.  
Table 2 groups Part I data elements. This content presents the official data element and data frame terminology from the standard. The Acceleration Set4 Way and VehicleSize items are based on data frames, and the remaining items are based on data elements [27].  
In this paper, we focus on study and consideration of BSM as an original and affluent data source. BSMs that contain position and motion data, state information of the vehicles (e.g., latitude, longitude, elevation, heading, speed, acceleration, lights, brakes, wipers, time-stamped, path history) exist only temporarily, used locally, and are never stored.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Message Set** & **Purpose** \\ \hline A La, Carte Message & Generic message with flexible content. \\ \hline Basic Safety Message & Transmits information necessary for vehicle-to-vehicle safety applications \\ \hline Common Safety Request & A vehicle uses this to request specific state information from another vehicle \\ \hline Emergency Vehicle Alert Message & Alerts, drivers that an emergency vehicle is active in an area \\ \hline Intersection Collision Avoidance & Provides, vehicle location information relative to a specific intersection \\ \hline Map Data & A roadside unit uses this to convey the geographic description of an,intersection \\ \hline NMEA, Corrections & Encapsulates, one style of GPS corrections—NMEA (National Marine Electronics Association),style 183 \\ \hline Probe, Data Management & A, roadside unit uses this to manage the collection of probe data from vehicles \\ \hline Probe, Vehicle Data & Vehicles, report their status over a given section of road to allow a roadside unit to, derive road and traffic conditions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Society of Automotive Engineers (SAE) J2735 Dedicated Short Range Communication (DSRC) standard message sets.
# Development of an Insect-like Flapping-Wing Micro Air Vehicle with Parallel Control Mechanism
###### Abstract  
Most traditional flapping-wing micro air vehicles (FMAVs) adopt a serial control mechanism, which means that one drive corresponds to one degree of freedom. However, the serial mechanism often struggles to meet FMAV requirements in terms of stiffness, size, and reliability. In order to realize a compact reliable control mechanism, we developed a two-wing insect-like FMAV with a parallel control mechanism. The prototype possesses an optimized string-based flapping wing mechanism, a 2 RSS/U parallel control mechanism, and an onboard power supply and controller. The pulley's profile of the string-based mechanism was refined to reduce the deformation and impact of the string. The parameters of the parallel mechanism were designed to enable the stroke plane to rotate a large angle to produce control torque. The prototype had a flapping frequency of 25 Hz, a full wingspan of 21 cm, and a total weight of 28 g. A PID controller with a decoupler based on the kinetics solution of parallel mechanism was designed to control the FMAV. A force and torque (F/T) experiment was carried out to obtain the lift and control torque of the prototype. The measured data showed that the flapping wing mechanism provided sufficient lift and the control mechanism generated a toque caused by the stroke plane rotation and trailing edge movement and were linear to the control input. A flight test was carried out to verify the flight stability of the prototype. The result shows that the attitude angle only fluctuates within a small range, which proved that the control mechanism and control strategy were successful.  
FMAV; parallel mechanism; stroke plane modulation; trailing edge modulation +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Comparison of DFA and OAV Aroma-Active Compounds' Identification  
Eleven volatile compounds were identified as potential aroma-active compounds in dried jujube slices based on the joint analysis of OAV and DFA, such as 4-hexanolide, 5-methyl-2(5H)-furanone, 3,5-dihydroxy-2-methyl-4-pyrone, 4-hydroxy-2,5-dimethyl-3(2H)furanone, 4-cyclopentene-1,3-dione, and so on. Some compounds with high DF could not be detected by OAV, such as 2,3-butanedione and acetin in the B7 sample, which were significantly and positively correlated with roasted sweet attributes. Moreover, some compounds with high OAV could not be recognized by DFA, such as butanoic acid with descriptors of smelly. In food matrices, the release of aroma compounds can be influenced by interactions between the volatiles and the components of the food [1]. Thus, we should use both methods to identify aromatic active compounds. The results of OAV were in good agreement with DFA in identifying the major contributors to the aroma of dried jujube slices. Furthermore, 21 compounds were recognized as significant aroma-active compounds in this study. The cluster heat map of the 21 compounds of the samples under different treatment conditions is shown in Figure 2.
# Assessment of Smart Mechatronics Applications in Agriculture: A Review
## 4 Results and Discussion
### Review Findings  
The \"concept of connected farm service,\" a management system for farms and agricultural machinery, was proposed as a result of the incorporation of modern mechatronics systems in agriculture. Development of server software and mobile application software for the agricultural machinery service management system involve installing a remote monitoring terminal on large, intelligent agricultural gear. Additionally, mechatronics technology was used in conventional agricultural production to offer important data on topics such as managing agricultural machinery operations, managing agricultural machinery in real time, and identifying the requirements for agricultural machinery operation and control. These systems allow for the remote monitoring of field conditions and agricultural machinery operating conditions, which increases agricultural production. By handling all measured data from installed sensors on farms, farm management information systems (FMISs) based on mechatronics systems have been proposed to help farmers make effective decisions. This technology has been utilized to deliver financial analysis findings to farmers based on big data analysis and data collected on materials such as machinery, seeds, herbicides, and fertilizers that are used on farms. A multi-intelligent control system (MICS) was introduced for the management of water resources in the agriculture sector since water constraints have increased rapidly in recent years. The proposed system, which is based on mechatronics, can be used to manage all water resources by tracking and regulating water use and reservoir water levels. This technology is reported to save up to 60% of water and to provide a satisfactory solution for water management in the agricultural sector. Mechatronics systems are also employed to manage resources such as irrigation, water quality, the environment of farm buildings and greenhouses, and mechatronics systems, with control systems used in particular to maintain ideal growth conditions for high-quality produce.