esa-sceva/satcom-chunk-collection huggingface dataset:
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 2 Data and Methods
### PM\({}_{2.5}\) Mass Concentration  
The PM\({}_{2.5}\) mass concentrations observed at 837 environmental monitoring stations (see Figure 1) were used to illustrate the air pollution condition. The PM\({}_{2.5}\) mass concentration is the concentration of particles with an aerodynamic diameter less than 2.5 \(\upmu\)m in the air, and the unit is \(\upmu\)g m\({}^{-3}\). The hourly data were provided by the China National Environmental Monitoring Center ([[http://www.mep.gov.cn](http://www.mep.gov.cn)]([http://www.mep.gov.cn](http://www.mep.gov.cn))). The data from 12:00 to 14:00 China Standard Time (CST = UTC + 08), covering the overpass times of Aqua and CALIPSO, were averaged to obtain daily mean data. When the daily mean PM\({}_{2.5}\) was larger than 75 \(\upmu\)g m\({}^{-3}\), it was classified as polluted weather.
## 4 Case Study
### Network Resources and Simulation Parameters  
Each layer has limited computational, storage, and communication resources available to implement the DL slices, which are indicated in Table 2. These values reflect the current and anticipated capabilities of NTN devices. For example, VUs are typically equipped with mobile processors that can provide up to 10,000 FLOPS. RSUs tend to be more powerful than VUs, boasting computational capabilities of up to 30,000 FLOPS. LAPs and HAPs are typically equipped with specialized networking hardware that can provide up to 50,000 FLOPS. Satellites, on the other hand, are constrained by available power and cooling, and their computational capacities typically range from 70,000 FLOPS to 100,000 FLOPS.  
The communication bandwidth values represent the current capabilities of Long-Term Evolution (LTE) and 5G and beyond cellular networks. VUs are typically connected to 5G networks with bandwidths of up to 20 Mbps. RSUs can connect to higher speed networks with bandwidths of up to 40 Mbps. LAPs and HAPs can also connect to higher-speed networks, with bandwidths of up to 50 Mbps. Satellites, however, are limited by the available bandwidth of the satellite link, and their bandwidth typically ranges from 90 Mbps to 100 Mbps.  
The storage resource values represent the current capabilities of flash memory and solid-state drives. VUs typically have storage capacities of up to 10 GB. RSUs can have storage capacities of up to 30 GB. LAPs and HAPs can have storage capacities of up to 50 GB. Satellites, on the other hand, are constrained by the available storage space in the satellite, and their storage capacities typically range from 100 GB to 200 GB.  
These values were employed as a starting point for our simulations and analyses. Of course, the specific values that one utilizes will depend on the specific applications and scenarios that they are considering. Finally, the LAP, HAP, and LEO nodes are located at distances of 1.2, 20, and 1000 km from the Earth's surface, respectively. [PERSON]'s channel capacity formula is adopted to model the channel between different layers [58].
## 4 Evaluation Metrics and Experimental Results
### Evaluation Metrics  
This study employs the evaluation metrics proposed during the MUC assessment conference for named entity recognition (NER). Specifically, the initial evaluation metrics for NER - namely F1 score, precision (P), and recall (R) -introduced by MUC-2 [49], are utilized to assess the efficacy of the model in extracting Chinese geographical place-name entities.
## 3 Application in the Classroom  
The use of VRLEs, both the former versions (Figure 3) and the later ones (Figure 4), have followed the same overall procedure, which is illustrated in Figure 5 and is similar to that used in prior studies reported by [PERSON] et al. [53]. This procedure has been implemented as an assignment regarding Materials Science and Technology, which is part of the program of the second course of Mechanical Engineering Degree at the Catholic University of Avila (Spain), between the academic courses 2013-2014 and 2019-2020, and consist of the following main stages:  
* The instructor teaches master classes, during which he/she addresses among others, both the theory and solution of practical exercises concerning TPD or CL problems. This stage lasts approximately two weeks (8-10 class hours) for teaching binary and ternary phase diagrams, and one week (2-4 hours) to explain the concepts related to CL.
* The use of VRLE in the classroom under the supervision of the instructor (0.5-1 hour). In addition, the student can continue using the VRLE on his/her personal computer after class as needed.
* Solution of exercises related to CL in groups of 2-4 students (in the case of TPD there is no solution of exercises). The duration of the solution process of these exercises, plus the corresponding correction by the instructor, typically involves 2 hours in the classroom.
* Survey fulfillment by students. These surveys aim to evaluate specific KPIs of the VRLEs: motivation, degree of interactivity, ease of use and usefulness (Table 1).
* Analysis of the data obtained through the above surveys.  
Figure 4: Screenshots of the VRLEs subsequently developed (VRLEs-2): (**a**) VRLE-2 system dedicated to TPD studies and (**b**) VRLE-2 system designed for CL investigations.  
The authors of this article have verified that solely applying the above stage corresponding to master classes (theory explanation and practical exercises solution) the teaching-learning process is incomplete, so that often students forget quickly what they have seen or heard in the classroom. Despite this, using VRLEs has demonstrated to improve the spatial understanding of students particularly in both TPD and CL exercises, so that they later assimilate related concepts. This certainly results in students achieving better meaningful learning and retaining concepts learned in class for longer periods of time [36].
## 2 Methods
### Backbone Network  
We use the one-stage object detection network RetinaNet [21] as the basic network and construct a multi-scale rotation anchor generation mechanism with multi-scale, multi-aspect ratio, and multi-rotation angle to realize automatic oriented detection of aircraft. The model structure is shown in Figure 1. It consists of four parts: Residual Network (ResNet) for initial extraction of aircraft features [25], Feature Pyramid Network (FPN) for multi-level features fusion and local salient feature integration [26], multi-scale rotating anchor generation, and sub-network for classification and location.  
In the initial stage of feature extraction, linear convolutional neural networks are generally used to extract features, such as AlexNet [27], GoogLeNet [28], and VGGNet (visual geometry group) [29]. These networks often increase the depth of the network to improve the ability to express features. When the network reaches a certain depth, the accuracy of training will tend to be flat. If we increase the number of layers again, the optimization effect will become worse. Therefore, we use the ResNet network to initially extract the features of aircrafts, by introducing residual mapping and jump connections in the network and adding the feature information of the previous residual block to the next residual block. It effectively avoids the disappearance of gradients and loss of feature information caused by the excessive depth of the network.  
RetinaNet is a simple one-stage object detector, named for its dense sampling of object locations in an input image [21]. The large class imbalance encountered during training of dense detectors overwhelms the cross-entropy loss. Easily classified negatives comprise the majority of the loss and dominate the gradient. The Focal Loss is designed to address the one-stage object detection scenario in which there is an extreme imbalance between foreground and background classes during training.  
Specifically, a modulating factor \((1-p_{t})^{\gamma}\) is added to the cross entropy loss, with tunable focusing parameter \(\gamma\geq 0\). The focal loss can be shown as:  
\[FL(p_{t})=-(1-p_{t})^{\gamma}\log(p_{t}), \tag{1}\]
## 4 Conclusions  
CGNPs were prepared by nanoprecipitation with different concentrations of \(\alpha\)-Toc, and the physicochemical characterization might suggest its encapsulation. The \(\alpha\)-Toc concentration influences directly the encapsulation efficiency and the particle size. With a spherical shape, the best particle presented 370.97 \(\pm\) 55.80 nm, 100% of encapsulation efficiency, 8.91 \(\pm\) 0.089% of loading content, and polydispersity 0.16 \(\pm\) 0.02. On the other hand, the Zeta potential did not have a significant difference between the formulations. In terms of stability, all formulations presented good macroscopic stability, but when verified by DLS, the CGNPs+10 shows the smallest size variation and polydispersity during 30 days. By TEM was possible to see the spherical submicron particles loaded with \(\alpha\)-Toc, in agreement with the previous results and suggesting a nanocapsule. Moreover, the \(\alpha\)-Toc release profile and the mathematical models revealed that when the \(\alpha\)-Toc are loaded in a CG capsule, the CGNPs protect the \(\alpha\)-Toc of the media and can deliver a bigger amount of the hydrophobic oil on the target site. The effectiveness of CGNPs+10 on cells with high proliferation capacity and abnormal characteristics is clear, offering them different applications with their cytotoxic action against abnormal cells and antioxidant action against normal cells. Lastly, the results reinforce the capacity to developing a new nanosystem with \(\alpha\)-Toc, more biocompatible and that can be used in different delivery systems for different purposes.  
K.C.L., A.J., E.P., I.B.L.-V., P.S., L.S.S., A.L.S.S. and M.V.C. contributed to the conceptualization, methodology, validation, formal analysis, and investigation, and writing--original draft preparation. H.S.B., M.F.L.R.S., R.L.C.d.A.-J., J.C.C., E.B.S., M.d.C.M. and P.S. contributed to the supervision, writing--review and editing, project administration, resources, and funding acquisition. All authors have made a substantial contribution to the work. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the Coordenacao Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Fundacao de Amparo a Pesquisa do Estado de Sergipe (FAPTEC) (PROCSSO: 88887.159533/2017-00 extracao, encapsusalacao cacterizacao de bioativos para o interesse biotecnologico) and Consheheo Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq 301964/2019-0 Chamada 06/2019, and Chamada CNPq n\({}^{\circ}\) 01/2019), from Portuguese Foundation for Science and Technology (FCT/MEC) through national funds, and co-financed by FEDER, under the Partnership Agreement PT2020 for the project UIDB/04469/2020.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methods
### Phase 3: Slick Feature Refinement
#### 2.3.2 Oil-Slick Information Descriptors  
As before [19; 20; 21; 22], we start our research analyzing the same ten attributes describing the oil slicks' geometry, shape, and dimension (these are collectively referred to as Size Information Descriptors) derived from two basic morphological features characterizing the oil slicks--i.e., area (Area) and perimeter (Per):  
\(\bullet\)**AtoP**: Area to Per ratio;  
\(\bullet\)**PtoA**: Per to Area ratio [35];  
\(\bullet\)**PtoAnor**: Normalized Per to Area ratio = Per/[(2.(Pi.Area))\({}^{1/2}\)] [36];  
\(\bullet\)**Complex Index** = [Per\({}^{2}\)]/Area [37];  
\(\bullet\)**Compact Index** = [4.Pi.Area]/[Per\({}^{2}\)] [18];  
\(\bullet\)**Shape Index** = [Per\(/4\)]/[Area\({}^{1/2}\)] [38];  
\(\bullet\)**Fractal Index** = [2.Ln(Per\(/4\))]/[Ln(Area)] [39];  
\(\bullet\)**LEN**: Number of pixels of each oil slick polygon.  
Analogously, we also exploit the same 36 basic descriptive statistics metrics experimentally explored to characterize the oil slicks' SAR backscatter signature as in our previous investigations [19; 20; 21; 22]. These metrics are calculated based on all pixels inside individual oil slick polygons:  
\(\bullet\)Four central tendency measures: Average (AVG), Median (MED), Mode (MOD), and Mid-mean (MDM: mean of the values between the 2 nd and 3 rd interquartiles, i.e., it trims off 25% of both ends);  
\(\bullet\)Six measures of dispersion: Range (RNG), Coefficient of Dispersion (COD: the subtraction of the 1 st interquartile from the 3 rd interquartile and the division by their sum), Standard Deviation (STD), Variance (VAR), Average Absolute Deviation (AAD: mean of the absolute difference of each value to the mean), and Median Absolute Deviation (MAD: median of the absolute difference of each value minus the median);  
\(\bullet\)24 pair-values of Coefficients of Variation (COV: ratio between STD and AVG [18], such that each of the six dispersion measures are individually divided by the four central tendencies);  
\(\bullet\)The Minimum (MIN) and Maximum (MAX) pixel values of each oil slick.  
Herein we introduce two new variables that describe the distribution patterns of the pixels within each oil slick: Skewness (SKW) and Kurtosis (KUR). As such, this collection of 38 basic descriptive statistics metrics characterizing the oil slick's SAR backscatter signature is henceforth referred to as SAR Information Descriptors. Together, these two types of Oil-Slick Information Descriptors (i.e., Size and SAR) determine the initial number of variables (48) accounted in our workable dataset.
## 1 Introduction
### Organization  
This research paper is organized into five sections to present a comprehensive study on vehicle detection and tracking. Section 2 offers a literature review that summarizes various existing algorithms related to object detection, object tracking, and vehicle detection tracking. In Section 3, the proposed framework for vehicle detection and tracking, specifically designed to estimate traffic flow, is thoroughly discussed. Section 4 presents the results obtained from implementing the proposed framework and comprehensively analyzes the findings. Quantitative and qualitative assessments of the system's performance are discussed, and comparisons with existing approaches are presented. Finally, Section 5 serves as the conclusion of the research paper.
## 7 Conclusions  
We have proposed a baseband digital receiver design that changes its sampling frequency (\(f_{s}\)) and word length (\(Q_{dig}\)) based on interference detection and signal quality (SNR\({}_{FE}\)) estimation. The approach is based on a LUT in the digital section of the receiver. Interference detector and SNR\({}_{FE}\) estimator that suit this approach have been proposed. Settings of different sections of digital receiver changes as \(f_{s}\) and \(Q_{dig}\) vary. But, this change in settings ensures that the desired BER is achieved. Overall, the receiver reduces amount of processing when conditions are benign and does more processing when conditions are not favorable. A hardware protocol is proposed for packet based communication that facilitates power scalable design. It is shown that the power consumption by the digital baseband can be reduced by \(85\)% (\(7\) times) when there is no interference and \(P_{sig}\) (SNR\({}_{FE}\)) is high. Design is experimentally verified and the proposed fact is established that energy condition of the hardware can be minimized when the signal condition is better.
# Enhanced Message Replication Technique for DTN Routing Protocols
## 5 Conclusions  
This paper introduces the Enhanced Message Replication Technique (EMRT)--a novel message replication technique for quota-based protocols that enables the number of replicas to be dynamic based on the nodes' capabilities. The EMRT is designed to generate the optimal number of replicas for newly generated messages by considering the history of encounters, as well as the buffer availability, energy, and time-to-live values of nodes. As a result, the EMRT improves the performance of quota-based protocols and more effectively utilizes network resources. To evaluate the performance of the EMRT, three quota-based protocols (Spray and Wait, Encounter-Based Routing, and the Destination-Based Routing Protocol) were considered. The results show that the EMRT significantly improves the delivery ratio (up to 13%, 8%, and 10% for Spray and Wait, EBR, and the DBRP, respectively) and reduces the latency average (by 51%, 14%, and 13% for Spray and Wait, EBR, and the DBRP, respectively) compared to the original quota-based protocols.  
Conceptualization, S.H. and S.I.; Methodology, S.H. and S.I.; Software, S.H. and M.S.S.; Validation, S.H., S.I. and M.S.S.; Formal Analysis, S.H., S.I. and M.S.S.; Investigation, S.H. and S.I.; Resources, S.H. and S.I.; Data Curation, S.H. and S.I.; Writing--Original Draft Preparation, S.H.; Writing--Review and Editing, S.H., S.I, M.S.S. and R.R.; Visualization, S.H. and S.I.; Supervision, A.H.A.-B., S.K., and S.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Data and Processing Strategies
### Processing Strategy  
GPS-only RT-PPP static modes were used in our study. GPS precise orbit and clock products and real-time satellite orbit and clock corrections were provided by IGS and GMV (one of the IGS analysis center), respectively. For the data processing, the elevation cutoff angle was set to 10\({}^{\circ}\), and the elevation-dependent weight for the observations with elevations below 30\({}^{\circ}\) was applied. The zenith wet delays were estimated as random-walk noise. The float-phase ambiguities were estimated as being constant for each continuous satellite arc. The initial standard deviation values for the GPS carrier phase observations were both set to 0.003 m, while the measurement error ratio between the pseudo range and carrier phase observations was set to 100. In the static PPP mode, the position coordinates were considered to be constants.  
In this study, the predicted clock offset is regarded as a prior value when the observation interruption occurs. The strategies of clock offset estimation were divided into two parts:  
1. [label=(0)]
2. Estimate clock offset with a white noise model (marked as scheme 1); and
3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **Receiver** & **Antenna** & **Clock** &
\begin{tabular}{c} **Distance from** \\ **BRUX (km)** \\ \end{tabular} \\ \hline BRUX & SEPT POLARX4 TR & JAVRINGANT\_DM & CH1-75A MASER & - \\ PTBB & SEPT POLARX4 TR & LEIAR5.R4 & H-MASER & 454.88 \\ OPT & ASHTECH Z-XII3T & 3S-O2-TSADM & UTC(OP) & 262.33 \\ WAB2 & ASHTECH Z-XII3T & ASHT700936F\_C & UTC(CH) MASTER & 487.20 \\ IENG & ASHTECH Z-XII3T & ASH701945C\_M & H-MASER & 687.69 \\ NIST & NOV OEM4-G2 & NOV702 & H-MASER & 7365.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the selected IGS stations.  
Estimate clock offset with the between-epoch constraint model (marked as scheme 2).  
The RT-PPP time transfer with data interruption was then investigated using simulation data and real-time data with the two handling schemes.
## 4 CSH-SVM Classification Algorithm  
### Principle of Multiclassification SVM Classification  
In linear separable problems, we can find the optimal hyperplane to separate two classes of points. The optimal hyperplane can be expressed as:  
\[\omega^{\mathrm{T}}\mathrm{x}+\mathrm{b}=0, \tag{8}\]  
The two types of points in space can be expressed as:  
\[\begin{cases}\omega^{\mathrm{T}}\mathrm{x}_{\mathrm{i}}+b\geq 0&\quad\mathrm{y}_{ \mathrm{i}}=+1\\ \omega^{\mathrm{T}}\mathrm{x}_{\mathrm{i}}+b\leq 0&\quad\mathrm{y}_{\mathrm{i}}=-1 \end{cases}, \tag{9}\]  
Applying the constraint \(y_{i}(\omega x_{i}+b)\geq 1\), the objective function was obtained and is as follows:  
\[\min\frac{1}{2}\omega^{2}, \tag{10}\]  
The Lagrange multiplier method was used to solve the objective function as below:  
\[\begin{split}\mathrm{L}(\omega,\mathrm{b},\alpha)=\frac{1}{2} \omega^{2}-\sum_{\mathrm{i=1}}^{\mathrm{n}}\alpha_{\mathrm{i}}\big{[}\mathrm{ y}_{\mathrm{i}}\big{(}\omega^{\mathrm{T}}\mathrm{x}+\mathrm{b}\big{)}-1 \big{]},\\ \alpha=\left\{\alpha_{1},\ldots,\alpha_{\mathrm{n}}\right\},\end{split} \tag{11}\]  
For the objective \(\omega,\ \mathrm{b}\) for partial derivatives, the maximum problem was converted into a minimum problem by using pairwise problems. Because of the inequal relationship in the constraints, the Lagrange multipliers must meet the Karush-Kuhn-Tucker condition. The final solution to the equation is:  
\[\min_{\alpha}\frac{1}{2}\sum_{\mathrm{i=1}}^{\mathrm{n}}\sum_{\mathrm{j=1}}^{ \mathrm{n}}\alpha_{\mathrm{i}}\alpha_{\mathrm{j}}\mathrm{y}_{\mathrm{i}} \mathrm{y}_{\mathrm{j}}\big{(}\mathrm{x}_{\mathrm{i}}\cdot\mathrm{x}_{ \mathrm{j}}\big{)}-\sum_{\mathrm{i=1}}^{\mathrm{n}}\alpha_{\mathrm{i}}, \tag{12}\]  
The solution must also must meet the following constraints: \(\begin{cases}\sum_{\mathrm{i=1}}^{\mathrm{n}}\alpha_{\mathrm{i}}\mathrm{y}_{ \mathrm{i}}=0\\ \alpha_{\mathrm{i}}\geq 0\end{cases}\).  
In solving practical problems, it is usually necessary to introduce a relaxation factor, thus the constraint becomes:  
\[\mathrm{y}_{\mathrm{i}}(\omega\mathrm{x}_{\mathrm{i}}+\mathrm{b})\geq 1-\bar{ \mathrm{x}}_{\mathrm{i}}, \tag{13}\]  
Figure 7: SMOTE echocardiogram dataset validated accurately.  
Under the new constraints, the new objective function is:  
\[\min\frac{1}{2}\left|\left|\omega\right.\right|\left|\right|^{2}+\mathrm{C}\sum _{i=1}^{n}\xi_{i}, \tag{14}\]  
By solving the objective function, we can find the optimal hyperplane that separates the two classes of points, and it is this property that determines the performance of support vector machine theory in binary classification problems over other machine learning methods. However, for multiclassification problems, SVMs generally must construct multiple binary classifiers, thus the multiclassification problem of SVMs should be viewed as multiple binary classification problems to solve, i.e., the hyperplane should be repeatedly constructed to meet the requirements of multiclassification.