esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Experimental Results
### Experiment on Four Classes of Emotion Recognition  
There were 32 subjects, 40 groups of affective experiments per subject, and 1280 data groups in total. They were labelled and categorized according to four modalities: LALV, LAHV, HALV, and HAHV. Among them, the baseline features have 88.92% and 84.70% training and testing accuracy on the four classifications, respectively, and the feature extraction approach proposed in this article has 90.15% and 88.46% training and testing accuracy, respectively, on the model. The test accuracy improved by nearly 4% compared to the baseline features. The results are shown in Figure 13.  
Figure 12: (**a**) Valence dimension recognition results; (**b**) arousal dimension recognition results; (**c**) dominance dimension recognition results; and (**d**) liking dimension recognition results.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 2 Related Work  
Various techniques have been proposed in the literature to mitigate the beam blockage problem, such as beam reset mechanisms, out-of-band assistance, multi-connectivity, vision-assisted, and deep learning algorithms.
# Deep Learning for COVID-19 Diagnosis from CT Images
## 2 Materials and Methods
### Metrics  
The performance measures have been evaluated by averaging five different simulations for all the networks. The measures used to quantify the performance of a network are the accuracy (Acc), precision (Pre), specificity (Spec), recall (Rec), F1-score (F1) as following defined:  
\[\text{accuracy}=\frac{TP+TN}{TP+TF+FP+FN},\]  
\[\text{precision}=\frac{TP}{TP+FP},\]  
\[\text{specificity}=\frac{TN}{FP+TN},\]  
\[\text{recall}=\frac{TP}{TP+FN},\]  
\[F1=\frac{2*\text{precision}*\text{recall}}{\text{precision}+\text{recall}}= \frac{2*TP}{2*TP+FP+FN}.\]  
Precision measures the number of correctly labelled items belonging to the positive class divided by the items correctly or incorrectly labelled as belonging to the same class. Specificity measures the proportion of correctly identified negatives (also called the true negative rate), while sensitivity measures the proportion of correctly identified positives (also called the true positive rate). The fourth measure is accuracy, defined as the ratio of correctly labelled instances to the entire pool of instances. The last is the F1 score, which conveys the balance between accuracy and recall.  
Furthermore, since we are facing a multiclass imbalance problem, we adopted two global metrics for multiclass imbalance learning to evaluate the performance of the networks [75]. They are the macro geometric average (MAvG), defined as the geometric mean of the partial accuracy of each class, and the macro arithmetic average (MAvA), defined as the arithmetic mean of the partial accuracies of each class.  
We describe them as follows:  
\[MAvG=(\prod_{i=1}^{J}Acc_{i})^{\frac{1}{J}},\]  
\[MAvA=\frac{\sum_{i=1}^{J}Acc_{i}}{J}.\]
## 4 System Design Based on Software Engineering
### System Architecture  
Figure 1 depicts the FELL system architecture diagram. The system architecture encompasses the essential components requisite for predicting the health of electric vehicle batteries. The presentation layer incorporates the user interfaces module, serving as the front-facing component of the system. This layer comprises web pages or user-friendly graphical user interfaces (GUIs) through which users interact with the system. Its focus lies in providing intuitive and user-friendly interfaces for users to input data, view predictions, and access system functionalities pertinent to EV battery health analysis.  
The business logic layer and data access layer comprises two main modules: the authentication module and the core business function module. The authentication module manages user authentication and authorization processes, ensuring that only authorized users can access the system and its functionalities. It implements secure authentication mechanisms such as OAuth, JWT (JSON Web Tokens), or other industry-standard protocols to enhance security. The core business function module serves as the central processingunit of the FELL system, handling requests from the presentation layer, processing data, conducting data analyses, and interacting with the database layer. This module hosts the business logic responsible for predicting battery health indicators such as SoC and SoH. The business logic layer and data access kayer ensures that the system's backend is scalable, reliable, and efficient in handling varying loads and data processing requirements. The data storage layer houses the database module, responsible for storing and managing the system's data. It is designed to accommodate structured data related to electric vehicle batteries, including historical battery performance data, sensor readings, user profiles, and analysis results.  
The data analysis component entails the processing and analysis of battery-related data collected from electric vehicles. It employs techniques such as statistical analysis, machine learning algorithms, and data visualization to derive insights and patterns. Big data analytic technologies ensure the robustness, scalability, and efficient handling of large volumes of data within this component. The predicting battery SoC component focuses on estimating the current charge level of electric vehicle batteries based on historical data, usage patterns, environmental factors, and other relevant parameters. It involves the use of algorithms such as Kalman filtering, neural networks, or regression models to accurately predict battery SoC. The predicting battery SoH component aims to forecast the overall health condition of electric vehicle batteries over time. It considers factors like battery degradation, cycle life, internal resistance, and capacity fade to assess long-term viability and performance. Utilizing predictive modeling techniques and diagnostic algorithms, it evaluates battery SoH and offers actionable insights for maintenance and replacement strategies.
## 3 Discussion
### Light Quality Regulated Nutrient Acquisition in Alfafa Plants  
The quality of light induces nutrient regulation in various plant species. Numerous studies have revealed that red light and blue light alter the micro- and macro-nutrient acquisition in plants [32]. Blue light and green light improve plant growth and regulate Fe, Zn, Cu, Ca, and Se, although they do not affect Mn and P acquisition in plants [15; 33]. Blue light induces Fe, Zn, Cu, and Mg acquisition in lettuce [16]. In addition to single-light treatment, combined light (blue+red) influences nutrient acquisition in plants. For instance, the red and blue light (R:B; 1:1) used in this study enhances the P, S, B, Ca, Mg, Cu, Fe, Zn, Mn, and Mo acquisition in _Brassica oleacea_[17]. In our study, we observed that the P, Cu, Zn, Ca, K, and Mg levels in alfalfa increased significantly, particularly in response to blue light or a combination of red and blue (1:1) light. Furthermore, the combined red and blue light enhanced the concentrations of Ca, K, and Mg to their highest levels. The variations in the mineral content under various light conditions suggest that the light ratio is one of the key factors that influences the acquisition of minerals in plants.
## 3 Methods
### Detecting Anomalies in Real-Time Telemetry Data
#### 3.3.2 Anomaly Analysis Based on a Knowledge Graph  
Data matching algorithms can determine whether the data from spacecraft equipment sensors display a state different from their stable or normal status. When real-time telemetry data are received, anomaly detection algorithms identify potential anomalous telemetry parameters. Once an anomaly is detected, the abnormal parameters and phenomena output by the algorithm can be used as keywords to locate the anomalous event in the knowledge graph, thereby aiding staff in developing anomaly handling plans and conducting anomaly cause analysis.  
Anomaly analysis is a significant application of knowledge graphs in intelligent knowledge processing. In this application scenario, we aim to construct a classifier capable of matching corresponding events based on user-inputted keywords. This task can be seen as a specialized form of text classification. However, unlike standard text classification, user input is often incomplete, with some keywords lost during the input process. Therefore, the classifier must still accurately match the correct event when some features (keywords) are missing.  
To resolve this issue, we selected the Naive Bayes model as our classification algorithm, primarily because it can handle inputs of indefinite length, and its training and prediction speeds are fast. These traits are crucial for our scenario, where we need to frequently update the model. Furthermore, the Naive Bayes model has a higher tolerance for missing input features compared to other models, which is also essential for our task.  
Specifically, let \(X=(x_{1},x_{2},\cdots x_{n})\) represent the \(n\)-dimensional keyword vector of abnormal parameters or abnormal phenomena output by the anomaly detection algorithm and \(E=(e_{1},e_{2},\cdots e_{m})\) represent the \(m\) abnormal event categories. Under the assumption that keyword probabilities are mutually independent within each abnormal event category, the Naive Bayes classifier selects the category with the maximum posterior probability as the classification label for the keyword vector \(x\):  
\[E(x)=\underset{e_{j}\in E}{argmax}P(e_{j})\prod_{i=1}^{n}P(x_{i}|e_{j}  
Here, \(F(x_{i}|e_{j})\) is the term frequency of \(x_{i}\) in the \(e_{j}\) class and \(\sum\limits_{i=1}^{n}F(x_{i}|e_{j})\) is the term frequency count of all words in the \(e_{j}\) class; \(C\) is the set of core words corresponding to the \(e_{j}\) class, and these core words are some special keywords that are often representative for certain classifications; and \(k\) represents the importance of core words in impacting classification, with \(k>1\).  
By adopting the above method, the abnormal event type can be determined based on the abnormal keywords, identifying the cause of the anomaly and the handling plan. In addition, when the abnormal information is incomplete, the priority order of abnormal event investigation can be determined according to the probability values output by the classification model.
## 3 Results and Discussion
### Velocity Relations in the Designed Elliptical Gear Set
The values of length of meshing line AE and contact ratio for elliptical and spur gearing are shown in Table 2 and Figure 24.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Elliptical Gearing**} & \multicolumn{6}{c}{**Spur Gearing (m\({}_{n}\) = 3.75 mm)**} \\ \hline \multirow{2}{*}{**AE (mm)**} & \multirow{2}{*}{**CR**} & \multirow{2}{*}{**Gear Ratio**} & \multicolumn{3}{c}{**Pitch Radius**} & \multirow{2}{*}{**Pitch Radius**} & \multirow{2}{*}{**AE (mm)**} & \multirow{2}{*}{**CR**} \\  & & & & & & \\ \hline
14.482 & 1.3081 & 0.5 & 60 & 30 & 17.527 & 1.5832 \\
14.494 & 1.3092 & 0. To compare the difference in the change of the contact ratio, the value of the contact ratio for a pair of teeth with a gear ratio equal to 1 (u = 1) was chosen as the basis. Figure 25 shows a comparison of the change in the contact ratio parameter. Designed elliptical gearing has the greatest meshing line AE value for the teeth meshing pair which has a gear ratio u = 1.0. This elliptical gearing is characterized in that the gearing is composed of teeth whose profiles are not the same shape. Therefore, the length of the meshing line AE and contact ratio are not constant for each pair of teeth in meshing, as in a standard circular spur gear. In Figure 26, the velocity diagram for intermeshing teeth 4 (driving gear) and 8 (driven gear) in the central point of mesh is shown. For the designed elliptical gear set, the magnitude of the circumferential velocity in the central point of mesh is different for each intermeshing pair of teeth. The direction of circumferential velocities of individual pairs of intermeshing teeth is identical, their position between the centers of rotation changes (Figure 27). Figure 26: Velocity relations for the intermeshing teeth 4 and 8 in the central point of mesh. Figure 17:0 Comparison of the change of the contact ratio parameter. In Figure 28, the change of the circumferential velocity magnitudes in the central points of mesh for individual intermeshing pairs of teeth at one revolution of the elliptical gears is shown. The circumferential velocity in the central points of mesh of individual intermeshing pairs of teeth is not constant, but it changes continuously depending on the changing gear ratio.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
###### Abstract  
Vast applications and services have been enabled as the number of mobile or sensing devices with communication capabilities has grown. However, managing the devices, integrating networks or combining services across different networks has become a new problem since each network is not directly connected via back-end core networks or servers. The issue is and has been discussed especially in wireless sensor and actuator networks (WSAN). In such systems, sensors and actuators are tightly coupled, so when an independent WSAN needs to collaborate with other networks, it is difficult to adequately combine them into an integrated infrastructure. In this paper, we propose drone-as-a-gateway (DaaG), which uses drones as mobile gateways to interconnect isolated networks or combine independent services. Our system contains features that focus on the service being provided in the order of importance, different from an adaptive simple mobile sink system or delay-tolerant system. Our simulation results have shown that the proposed system is able to activate actuators in the order of importance of the service, which uses separate sensors' data, and it consumes almost the same time in comparison with other path-planning algorithms. Moreover, we have implemented DaaG and presented results in a field test to show that it can enable large-scale on-demand deployment of sensing and actuation infrastructure or the Internet of Things (IoT).  
wireless sensor and actuator networks; drone network; connectivity; architectures and applications for the Internet of Things; opportunistic and delay-tolerant networks +
Footnote †: journal: Sensors
## 4 Experimental Results  
### Human Ear Datasets  
In order to verify the effectiveness of the proposed YOLOv5s-MG lightweight human ear recognition method, the human ear dataset is needed to train and detect the model. In this paper, we use three distinctive human ear datasets: CCU-DE, USTB, and EarVNI.0.
## 3 Results and Discussion
### 16S rRNA Gene Sequence and Phylogenetic Analysis of Strain Q3-6  
An almost complete 16S rRNA gene sequence of strain Q3-6, with a length of 1538 bp, was obtained by PCR amplification. After 16S rRNA gene sequence comparison, strain Q3-6 was identified as belonging to the Phylum _Firmicutes_, Class _Bacililia_, Order _Bacillus_, Family _Bacillus_, and genus _Bacillus_, with the same highest sequence similarity to _Bacillus mobilis_ (99.7%), _Bacillus_ _thuringiensis_ (99.7%), and _Bacillus_ _pacificus_ (99.7%). Further phylogenetic analysis conducted by genome comparisons is described in the next section.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 2 Materials and Methods
### Regression Models
#### 2.4.1 Univariate Regression Models  
The VIs were correlated with _DMY_ by several UR models: linear, exponential, power, polynomial and logarithmic. Models were built using the '_lm_' function in R environment [37].
## 3 Results and Discussion
### Discussion on Substrate Materials  
The objective of the research was to develop a process that can print a hollow tube directly on a substrate (Substrate #1) with an orifice under the projection of the printed hollow tube. The printed hollow tube serves as a connector to an external tubing system for gas/fluid input. The orifice is a passage to the microchannel built in the next level substrate (Substrate #2). The essence and contribution of the research were the invention of the coaxial printing and the implementation of in-situ UV curing. The role of Substrates #1 and #2 is simply a vehicle for demonstration. Glass was chosen for Substrate #1 and PDMS was selected for Substrate #2. These are commonly used materials for microfluidic experiments. There may exist different material combinations for similar purposes. Future studies may use the printing technology developed on different substrate materials for their intended test configurations.
# Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
## 7 Results and Evaluations
### Evaluation
#### 7.3.2 Evaluation  
In this part, we evaluate the efficiency of our proposed method based on analyzing the extracted domains from non-intentional bid requests. To consider a domain as either trusted or untrusted, it has to match at least one of these above considerations. After thirteen days of the real-time process (2-14 July 2018), we collected a total 180 distinct domains and these domains were used to discover what domains are trusted or untrusted in this section. During our observation, we found some rules of untrusted domains as follows:  
* Several domains or sites of the domain appear as regular but have a very large amount of bid requests coming from only one or two audiences, and after more than one or two months, these sites stopped working;
* Several domains or sites of the domain hold entirely ad-based content as shown in Figure 14;
* Several domains or sites of the domain have the same site frames and hold entirely ad-based content as shown in Figure 15;
* Several domains or sites of the domain are blank;
* Several domains or sites of the domain appear as regular domains; however, when we click on any topics on that site, it will redirect to irregular ads;
* Several domains or sites of the domain contain illegal content;
* Several domains or sites of the domain have an unusually high number of ads; the percentage of ads is greater than the user content.  
Additionally, Table 4 shows the results of classifying trusted and untrusted domains. The result shows that 7% of bid requests come from trusted domains, 7% of bid requests come from untrusted domains, and 17% of bid requests come from unidentified domains.  
Figure 14: Untrusted domains contain entirely ad-based content.  
Figure 15: This is a figure. Schemes follow the same formatting.  
The unidentified domains mean these appear as regular domains, but contained several ads and have a site ranking in similarweb for the Taiwan area of more than 1000. The results obtained from the preliminary analysis of trusted and untrusted domains show that our proposed RFBR methods are good at capturing non-intentional bid requests in a real-time process.
## 4 Proposed Communication System for D-M
### Communication System Novel Features  
Figure 3 shows the novel features of the proposed system, which provides full duplex communication between the D-M and the ND-M. This low-cost system is user-friendly and easy to install, with no operational cost to the end user. There is a training option available, which allows the features to be customized for individual users. The ML-based algorithm provides a continuous improvement option where new and higher quality data, i.e., hand gesture images, can replace an existing image. The proposed system supports American Sign Language, Pakistani Sign Language, and Spanish Sign Language. More languages can be added. Hand gestures data is acquired using LMD, which is a COTS device and, unlike a glove, does not require maintenance or calibration. The image data is processed using the CNN algorithm.
## 3 Research Method
### Questionnaire Development  
A questionnaire, outlined in Exhibit 1, was developed from the literature review and was composed of 15 open-ended questions that covered the usage of SM (e.g., advantages and disadvantages, communication channels, information sharing, and future opportunities). When constructing the questionnaire, the researchers paid attention to the gaps identified and how the data collected would enable them to meet the aim of the research. In regard to the inclusion of questions, emphasis was placed on deriving the benefits of social media and also, the number of questions were reduced so that each interview would last about 60 min. The questionnaire was translated from English into Vietnamese by one of the researchers who is bilingual (a native speaker of Vietnamese that is fluent in English). Prior to the actual interviews with the owners of the fashion retail companies taking place, the researcher in Vietnam undertook pilot interviewswith staff in two other fashion retail companies (an owner and the head of marketing). Based on the results of the pilot interviews, the researchers amended the wording of the questions. The researcher in Vietnam, through a process of continual negotiation, was able to gain access to the participating companies, and interview the owner. All the owners had experience in building brands and using e-marketing through the use of established e-commerce platforms. A strict code of ethical practice was followed, which guaranteed anonymity to those interviewed.  
**Exhibit 1: In-depth interview questionnaire**  
1. What is the main advantage(s) of using social media to communicate with customers?
2. Which social media channels do you use to communicate with customers (such as YouTube, Facebook, WhatsApp; LinkedIn twitter etc)?
3. Which social media channel is the most suitable/successful and why?
4. How is the data/information received through social media analysed?
5. Do you share the results of the data analysis with your suppliers? If yes, please explain:
6. Do you and your suppliers co-operate in terms of social media usage? If yes, please explain:
7. Which aspects of customer service does social media usage support?
8. When and at what time of the year is social media usage most effective?
9. When is it not appropriate to use social media?
10. How effective is the use of social media compared to other forms of communication?
11. Does social media usage require special training and support? If yes, please explain:
12. Do you use social media specialist companies to administer your social media activities (including social media site)? If yes, please explain:
13. Do you monitor how other retailers use social media? If yes, please explain:
14. Which aspects of supply chain management, does social media support and why?
15. In the future, how do you see the usage of social media being extended?