esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Performance Analysis
The improved performance of  
Figure 4: Performance analysis of the proposed and existing approach in terms of precision, recall, F1 score, sensitivity, and specificity. Figure 5: False acceptance rate (FAR) and false rejection rate (FRR) analysis. the proposed technique was due to the utilization of the Radial Basis Function (RBF) kernel within the DBSCAN framework. Consequently, the suggested methodology accurately anticipated the connection failures inside the authenticated channel of the transmission process. Incorporation of RDBSCAN into the link-failure detection pipeline was justified by the marked improvement in the Jaccard Index. This was due to the overlapping of RDBSCAN's high overlap with true failure events, minimizing false alarms and missed detections, and allowing for more reliable, fully automated channel maintenance in CV-QKD systems. * Performance analysis for channel equalization: The performance of the proposed \(R^{2}LMS\) was evaluated using known methods: LMS, Normalized Least Mean Square (NLMS), Recursive Least Squares (RLS), and Kalman Filter (KF). Table 4 presents the performance analysis of the proposed and current approaches based on SNR and BER values. The proposed method attained a markedly enhanced SNR of 53.65 dB and a diminished BER of 0.00713, indicating a higher efficacy compared to the current methodologies. However, the current methodologies, including LMS, NLMS, RLS, and KF, achieved diminished SNR and elevated BER, indicating suboptimal performance in channel equalization. The performance enhancement with the proposed method was due to the application of Ridge Regularization in the LMS technique, hence improving channel equalization.
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 1 Introduction
Our contribution can be summarized as follows. * A QoS remediation model (ReSQov) using response time and throughput parameters. * Cloud Service capacity model generation using Universal Scalability Law. * Demonstrate the better performance of the model through simulation and comparison with other policy-based resource allocations. * Validation of the Significance difference through the statistical test ANOVA. The following sections comprise the paper: Section 2 discusses relevant works; in Section 3 the proposed approach is explained; Section 4 presents the experimental setup and scenarios for evaluating the proposed model, while Section 5 presents the performance results and evaluations.
## 1 Introduction
This study presents the application of the 1D-CNN and 3D-CNN deep learning algorithms and an ensemble of learning techniques like RF, SVM, K-nearest neighbor (KNN), and multiclass naive bayes (MNB) algorithms to a first available time series of new hyperspectral imagery belonging to four farm test cases. At this scope, the PRISMA mission provides an ideal benchmark for demonstrating the advancement offered by hyperspectral time series for the application in the agriculture context for crop mapping. This brings us to clarify the objectives of our study: (1) to develop a cross-farm ML/DL training and accuracy assessment when there are no in situ data available for training and (2) to test fine early-season crop mapping capabilities of hyperspectral time series data in comparison with multispectral imagery with a fine temporal resolution. The novelty of this research lies in (1) the supply of contemporaneous ground truth prepared by analyzing the PRISMA and Sentinel-2 optical images for training and validation; (2) demonstrating by ML and DL algorithms the importance of the use of hyperspectral satellite time series data for the accurate early- and in-season crop mapping. Section 2 describes the study areas used for the field data collection and analysis, and the methodology for achieving this study's objectives. Sections 3 and 4 report and discuss the results.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 2 Related Work
### Interaction Styles of Telementoring
#### 2.3.5 Other Types of User Interfaces  
There are other interaction styles such as using tangible UIs [25], foot pedal [27] and avatars [21]. We think that using foot pedals depicted in Figure 5, could go well with the aforementioned types of UIs (touch-based, voice-based, and gesture-based) and complement shortcomings of these interaction styles. For example, foot pedals could replace on-device and touch-based UIs in the medical and surgical settings. This interaction style can be used to confirm correctly recognized gestures or cancel incorrectly recognized gestures. Table 2 identifies telementoring systems with smartglasses and their employed interaction styles discussed in this section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Telementoring** & **Smartglass** & **Interaction Styles** \\ \hline [PERSON] et al. [33] & No & Vuzix Blade & Touch-based \\ [PERSON] et al. [13] & No & MS HoloLens & Voice-based \\ [PERSON] et al. [22] & No & Google Glass & Gesture-based \\ [PERSON] et al. [25] & No & Epson Moverio BT-200 & Tangible UI \\ [PERSON] et al. [27] & No & Brother AiRScouter & \\  & & WWD-200B & Wireless Two-button Foot Pedal \\ [PERSON] et al. [34] & No & N/A & Video, Quiz \\ [PERSON] et al. [14] & Yes & MS HoloLens & Voice-based, Telestration \\ [PERSON] et al. [23] & Yes & Glass Enterprise Edition 2 & Voice-based \\ [PERSON] et al. [35] & Yes & N/A & Voice-based \\ [PERSON] et al. [26] & Yes & Epson Moverio BT-200 & Telestration \\ [PERSON] et al. [32] & Yes & N/A & Voice-based \\ [PERSON] et al. [15] & Yes & MS HoloLens & Telestration \\ [PERSON] et al. [36] & Yes & N/A & Gesture-based \\ [PERSON] et al. [37; 38] & Yes & N/A & Touch-based, Telestration \\ STAR [16; 17; 18; 19] & Yes & MS HoloLens & Touch-based, Voice-based, Telestration \\ [PERSON] et al. [20] & Yes & MS HoloLens & Voice-based, Telestration \\ [PERSON] et al. [39] & Yes & N/A & Voice-based, Telestration \\ [PERSON] et al. [40] & Yes & MS HoloLens & Telestration \\ [PERSON] et al. [21] & Yes & MS HoloLens, HTC Vive Pro & Telestration, Avatar \\ [PERSON] et al. [41] & Yes & N/A & N/A \\ [PERSON] et al. [42] & Yes & Head-mounted Camera & Voice-based, Gesture-based \\ [PERSON] et al. [31] & Yes & N/A & Voice-based, Telestration \\ [PERSON] et al. [7] & Yes & HTC Vive HMD & Gesture-based, Head Pointer, Telestration \\ [PERSON] et al. [8] & Yes & HTC Vive HMD & Gesture-based, Head Pointer, Eye Gaze, Telestration \\ \hline \hline \end{tabular}
\end{table}
Table 2: Telementoring Studies with Smartglasses and Interaction Styles.  
Figure 4: Illustrations of telestration interaction styles.
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Supervised Method
#### 3.1.5 Mask RCNN  
Mask RCNN is an extended form of Faster-R-CNN that integrates object detection and instance segmentation functions for a two-stage framework network: the first stage scans images and generates proposals (proposals may contain a target area), and the second stage classifies proposals and generates bounding boxes and masks.  
Literature [51] proposed an improved model of Mask RCNN--IPCNN. The model first uses a deep residual neural network to process the image from the image pyramid to extract features; the extracted features are generated through the feature pyramid to generate pyramid features, which are processed by RPN to generate defect bounding box and classify it, and after that, uses FCN to generate a defect mask in the defect bounding box. Literature [52] designed an end-to-end system that can locate solar panel pollution; the system is based on Mask FCNN (Fully Convolutional Mask RCNN), which consists of a classification network ImageNet and a comprehensive network of bottom-up up-sampling of feature images; the effect of information loss is eliminated by up sampling.  
In the field of surface defect detection of industrial products, due to high accuracy and good adaptability, the supervised method is the most mainstream method in the current deep learning method, and its application scope is becoming wider and wider. However, the disadvantages of this kind of method have gradually become prominent in practical applications, that is, the huge workload brought by the advance labeling of the dataset, especially in some high-precision scenarios; at the same time, the continuous improvement of the industrial level has led to the continuous reduction of defective samples, which also has a certain impact on the method of supervision.
## 4 MSaaS Reference Architecture
### Enterprise Architecture and Reference Architectures  
The design and development of large and distributed systems built by integrating various components that cooperate by exchanging control information and data are non-trivial tasks. Thus, the specification of a _system architecture_ (which specifies the system structure, the boundary of each component, the interfaces they provide, and the expected inputs and outputs) is one of the most important steps to be taken in developing a complex system.  
More generally, in order to make the system architecture suitable to provide different stakeholders with the required information, various _architectural layers_ have to be considered. _Conceptual architecture_ models can be defined to describe the system from an abstract or business-related perspective. Then, iteratively, more refined architectural models are derived from abstract ones, until _concrete architectures_ specifying the internal detail of each system component are finally developed. Several _Architectural framework_ exist that provide guidance for structuring, classifying, and specifying such architectural layers, among others, NAF [37], DoDAF [38], and TOGAF [39].  
In this context, the _enterprise architecture_ discipline [40], introduces the concept of _reference architecture (RA)_: _A RA is a part of the enterprise architecture that provides standards and documentation for a particular type of capability throughout the enterprise_[41].  
Figure 1: Application and Infrastructural orchestration for MSaaS applications.  
Thus, an RA is an _abstract architectural template_ that provides guidance to support the development of a concrete system in a given operational context. More specifically, a RA addresses a given domain and describes an abstract architecture in terms of structural building blocks, their relationships, and the capabilities each building block provides.  
From the same RA, one or more **concrete architectures** can be derived, each used for driving the implementation of actual systems, as summarized in Figure 2.
# Detection of Surface Water and Floods with Multispectral Satellites
## 3 Multispectral Indices for Water Segmentation
### Investigation of Spectral Index Performances
Therefore, there is a potential impact due to such methodologies that is hard to quantify, but we do believe that the overall analysis provides an interesting picture of the performances obtained by exploiting specific indices for the scope of interest.
## 2 Materials and Methodology
### Training Process  
This section presents the training of the classifiers using the CNN transferable features. One way to train a chosen classifier is to divide the extracted features into two parts: one for training and the other for testing. The reason for this is that training and testing the classifier using same data cannot give a true estimate of the classifier's performance. This approach, also known as the train-test split (TTS) is very efficient and less computationally expensive since it involves training only a single classifier in each run. However, the training or testing split may not contain strong representation patterns, which may lead to a biased system. Secondly, the TTS approach is prone to overfitting.  
Alternatively, although it is more computationally expensive than TTS, cross-validation (CV) is an interesting technique which mitigates overfitting and captures the representation of each instance during both training and testing processes, thereby giving an optimum performance estimate of the classification algorithm. A common CV technique is \(k\)-fold cross-validation, where \(k\) is the number of folds or parts into which the features are divided. The choice for \(k\) takes different values; most commonly, choices are \(k=3\), \(k=5\), and \(k=10\). When the value of \(k=10\), this means that 10% of the features are held out for testing, while the remaining 90% are used for training. This process is repeated \(k\)-times, whereby, at each run, a different fold is used for testing. Logically, training using \(k\)-fold CV ensures that \(k\)-classifiers are trained, and the mean score of their accuracies gives the overall performance estimate. Due to its significant impact on mitigating overfitting and its performance effectivity, all classifiers in this study were trained using the CV technique. Figure 5 depicts the CV technique used in this paper where \(k=10\).  
Figure 4: Classification processes.  
Figure 5: Illustration of \(k\)-fold cross-validation (CV) technique.
# Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process
## 4 Simulator Selection and Experimental Design
Figure 6 shows the swarm development process flow using the above-mentioned experiment series. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Experiment Series (ES)** & **Description** &
\begin{tabular}{c} **Target Mechanisms Developed** \\ **and Observed** \\ \end{tabular} & \multicolumn{1}{p{142.3 pt}}{**Simulation Tools Used**} \\ \hline
1 & Basic swarm phenomenon observation & Flocking maneuvers, 2D path planning. & MATLAB \\ \hline
2 & Studying FANET topology and performance. & \
\begin{tabular}{c} Examine inter-agent \\ communication process, equipment range, and routing \\ protocols. \\ \end{tabular} &
\begin{tabular}{c} OMNET++ and associated \\ plugins. \\ \end{tabular} \\ \hline
3 & Photorealistic Single UAV design and observation. & \
\begin{tabular}{c} Examine the addition of various \\ cameras, sensors on UAV, and \\ data collection using simulated \\ environments. \\ \end{tabular} &
\begin{tabular}{c} Webots, MATLAB \\ UAV toolbox, \& Simulink \\ \end{tabular} \\ \hline
4 & Examining basic movement operations of UAV swarm. &
\begin{tabular}{c} Establishing and defining \\ inter-swarm policies, agent \\ deployment, and defining swarm \\ topology \\ \end{tabular} & CoppeliaSim \\ \hline
5 & Implementing ground terrain features as well as realistic obstacles with varied agents. &
\begin{tabular}{c} Defining buildings, trees, and \\ realistic heterogeneous agents \\ \end{tabular} & CoppeliaSim, Webots \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experiment series with descriptions, target observations, and simulation platforms used.
## 4 Robotic Arm Position Detection
### Robotic Arm Control  
The robotic arm model we used was manufactured by Quanser, as shown in Figure 3. It is a 2-Degree-of-Freedom (DOF) robotic arm that allows for rotational control at the shoulder and elbow joints. We introduced the Inverse Kinematic Equation (IKE) in 2D space, which was used to compute the control angles needed for each part of the robotic arm, in order to position it over each key on the keyboard. The angles were computed by:  
\[\theta_{1}=\tan^{-1}\Bigl{(}\frac{y}{x}\Bigr{)}-\tan^{-1}\biggl{(}\frac{l_{2} \sin(\theta_{2})}{l_{1}+l_{2}\cos(\theta_{2})}\biggr{)} \tag{3}\]  
\[\theta_{2}=\cos^{-1}\biggl{(}\frac{x^{2}+y^{2}-l_{1}^{2}-l_{2}^{2}}{2l_{1}l_{ 2}}\biggr{)} \tag{4}\]  
where \((x,y)\) is the coordinate, \(\theta_{1},\theta_{2}\), \(l_{1},l_{2}\) are the corresponding angle and length of the shoulder and elbow. The [PERSON] robot was controlled using Simulink and the process model depicted in Figure 4. Here, the calculated joint angles were scaled into control voltage inputs, which were fed into a PID control loop. The control loop ensured that the  
Figure 2: Dependence on location and movement.  
final position of the robotic arm was identical between trials, allowing us to perform a highly repeatable experiment for localisation.  
The PID control system was characterised by 3 design factors that affected the speed of the robotic arm as it moved, as well as its transient response characteristics. To ensure a repeatable position between trials, we designed PID parameters to ensure no steady-state error in position and an over-damped transient response to prevent shaking of the experimental apparatus. The PID coefficients are detailed in Table 1.
###### Abstract  
The effects of transition metals on the hydrophobicity of nano-structured layered double hydroxides (LDHs) and the compatibility of LDHs/ethylene vinyl acetate (EVA) composites have seldom been reported. NiMgAl-LDHs slightly surface-modified with stearate and doped with transition metal cations (Mn\({}^{2+}\), Co\({}^{2+}\), Cu\({}^{2+}\), Zn\({}^{2+}\)) are investigated. Compared to the pure EVA, not only were the maximal degradation-rate temperatures (\(T_{\rm max}\)) of the ethylene-based chains enhanced, but also the smoke production rate (SPR) and the production rate of CO (COP) were sharply decreased for all the composites. Most importantly, a new flame retardant mechanism was found, namely the peak heat release rate (pk-HRR) time, which directly depends on the peak production rate of CO\({}_{2}\) (pk-CO\({}_{2}\)) time for EVA and all composites by cone calorimeter test. Moreover, the Mn-doped LDH S-NiMgAl-Mn shows more uniform dispersion and better interfacial compatibility in the EVA matrix. The cone calorimetric residue of S-NiMgAl-Mn/EVA has the intumescent char layer and the compact metal oxide layer. Therefore, S-NiMgAl-Mn/EVA shows the lowest pk-HRR and the longest pk-HRR time among all the composites.  
transition metals; interfacial compatibility; layered double hydroxides; ethylene vinyl acetate copolymer; flame retardancy; thermal stability Article
Research on Low-Density Parity-Check Decoding Algorithm for Reliable Transmission in Satellite Communications  
[PERSON] and [PERSON]  
1 Institute of Systems Engineering, Academy of Military Sciences, Beijing 100141, China 12 School of Telecommunications Engineering, Xidian University, Xi'an 710071, China2
# Study on Contamination with Some Mycotoxins in Maize and Maize-Derived Foods
## 1 Introduction  
Corn (_Zea mays_ L.) is native to Central America, cultivated worldwide as a food, industrial and fodder plant and was first mentioned in Romania in the late seventeenth century. Maize is a plant of the _Poaceae_ family. The annual harvest of maize in 2020 was 1192 million tons, as maize is one of the most commonly used cereals in food [1]. Mycotoxins infect corn in the field or during the storage stage [2]. Mycotoxin contamination is the main problem with maize production. Mycotoxins are formed by filamentous fungi of the genus _Ascomycata_, which affects human and animal health and causes chronic and acute diseases [3; 4; 5; 6]. Corn is most frequently contaminated by _A_spergillus sp. and _Fusarium_ sp. [7].  
The most important mycotoxins that contaminate maize throughout its development cycle and storage period are fumonisins (FUM), deoxynivalenol (DON) and zeazalenone (ZEA) [8]. Fumonisins are formed by _Fusarium verticilloides, F. prolifertatum_ and _F. fujikuroi_; zeazalenone are formed by _Fusarium gaminatearum_, _F. culmorun_, _F. cereals_ and _F. equisetti_; deoxynivalenol are produced by _Fusarium gaminatearum_ and _F. culmorun_[9]. The development of fungi and, by extension, mycotoxins, is favored by high temperatures, high humidity and prolonged storage [10]. The possibility of mycotoxin production rises if inappropriate agricultural practices are applied.  
Mycotoxins cannot be detected in the visible field (VIS) but can be recognized in ultraviolet (UV) light; moreover, they do not have a distinctive smell and do not change the organoleptic properties of foodstuff [11]. The consumption of corn contaminated with mycotoxins by humans or animals can generate multiple serious toxic effects [12]. Exposure to mycotoxins leads to mycotoxicosis (ergotism, food poisoning and aflat), a disease affecting human organs which, under certain conditions, may cause death [11; 13].  
Mycotoxicosis may be acute or chronic [2]. The main ways of exposing the human body to mycotoxins are by ingestion, inhalation and contact.  
The European Union has established by legislation limits on the content of mycotoxins in all categories of foodstuffs, including corn and corn-derived aliments, to protect the population's health. Regulation (EC) No. 1881/2006 [14] sets the maximum levels for aflat, fumonisis, zeanalenone and deoxynivalenol in food. This regulation was amended by (EC) Regulation No. 1126/2007 [15] and (EC) Regulation No. 165/2010 [16]. Prevention, detoxification and decontamination measures are needed to combat mycotoxin contamination of food [17]. The objective of this study is to assess the contamination with three mycotoxins, namely deoxynivalenol, zeanalenone and fumonisis, of maize and maize-derived products from local producers in the eastern part of Romania.
## 1 Introduction
While the shapes of SIF spectrum may vary due to changes in plant physiological conditions and environmental stress, SIF emitted from plants typically has two peaks: one in the red spectral region (640-700 nm), with a maximum at about 685 nm, and the other in the near infrared (NIR) region, with a peak around 740 nm. SIF has been shown to be highly linked with gross primary productivity (GPP) in different land ecosystems [31; 32; 33; 34]. A previous SIF study [35] showed that reflectance-based green indices have a low sensitivity to short-term variations in GPP. In particular, [PERSON] et al. [20] found that SIF has a better performance in capturing phenology in the northern high latitude forests. In addition, satellite observations of SIF were proven to quantify the timing and magnitude of photosynthetic activity in Alaskan tundra more accurately than the satellite-derived VI did [36]. In this study, we compared the performance of various data sources at different spatial scales in estimating the phenology of a deciduous temperature forest in 2014. Specifically, we used MODIS and ground based NDVI, and SIF from satellite-derived and site-level observations. We had the following three objectives:  
1. compare the performance of SIF-based and reflectance-based observations in monitoring the GPP-based phenological phases;
2. compare the differences between the phenological metrics derived from near-surface SIF measurements and those from the satellite-based SIF dataset; and
3. determine the roles of meteorological variables and physiological properties in determining the phenological events.