esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Recent Developments and Progress
### Atmospheric Sounding by GNSS-RO  
GNSS-RO is a satellite remote sensing method utilizing GNSS radio occultation observations from various signals such as GPS, GLONASS, Galileo, and BDS. These signals are gathered from low-orbit satellites to estimate the Earth's atmospheric parameters (including the ionosphere and troposphere) with a high degree of vertical precision. The high vertical resolution of GNSS-RO data provides a good opportunity to study space weather and ionospheric variations, and atmospheric characteristics, or climate monitoring and model validation. A case study has been conducted utilizing a virtual testing platform to enhance the sustainable product development of GNSS-RO signal receivers. The purpose was the implementation of GNSS radio occultation observations, using the innovative satellite FengYun-3C, or FY-3C, one of the world's most important civilian meteorological satellites, which was utilized for both morning and evening orbits. The outcome was more compelling when compared to results at the altitude spectrum of 0-5 km. Findings ensured that the relative error remained below 2%, whereas in the subsequent altitude range of 5-20 km, the relative error droped to under 1% [35].  
Also, commercial GNSS-RO satellites (the American PlanetiQ, the Korean KOMPSAT-5, and the Spanish PAZ) were evaluated in terms of coverage, SNR, and penetration depth. The results showed that the capability of PlanetiQ was approximately equal to those of KOMPSAT-5 and PAZ, especially with respect to the tracking of signals from any GNSS satellite. Additionally, PlanetiQ performed more strongly in the deepest troposphere [36].  
Figure 6: Static positional biases of AR and float solutions for MGEX stations on DoY 183, 2022 (Reprinted from Ref. [34]).
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 2 Preliminary Understanding of VANETs
### Features of VANETs  
Security is important for all networks, but it is considered more crucial in VANETs, as it is related to human lives by providing congestion and the collision-free path to ambulances and vehicles. Various features of VANETs include the following [31; 32].  
* _Centralized security system:_ In VANETs, a centralized security system is used, which is responsible for the implementation of security among all the nodes. Generally, servers are used for this. Packets are not monitored or rarely monitored in ad hoc networks, which make it more insecure. In these networks, either no security-protocol is used or used at very few occasions that make it more vulnerable to attacks.  
* _Time constraint_: In critical situations, messages need to be forwarded within a specified time, otherwise collision cannot be avoided. On the other hand, the authenticity of these messages needs verification, which may lead to extra delays. Time constraint specifies that secure and authentic messages should be forwarded within a specified time.
* _Shared broadcast channel_: VANET is a wireless network that uses broadcast for transmission. Therefore, it is very easy for hackers to get the information traveling in these networks.
* _Volatility and no fixed topology_: Vehicles never move at the same speed and in the same direction, so it is not possible to maintain a network for a very long period. It is a short-lived communication network where an attacker may launch an attack and move from its location and escape. Moving vehicles leads to another issue of no fixed topology of the network. Without topology, routing becomes a difficult process to be implemented. Routes are frequently reconfigured, which increases routing overheads.
* _Infrastructure less_: VANET is an ad hoc network, and all the ad hoc networks do not contain any infrastructure. Although V2I communication considers RSUs and TAs as infrastructure, but still major infrastructure components like routers and servers are not used. Therefore, a trust relationship should be established among vehicles using reputation management systems. Other important features are Quality of Service (QoS), authentication, repudiation, scalability, heterogeneity, and multi-hop connection, etc.
## 3 Results
### Validation
It can therefore be argued that simulated data are a valuable complement to satellite data in exploring relationships that are difficult to establish using satellite imagery only. However, it should be noted that the results obtained through numerical experiments still require validation through the use of other models, in situ measurements and satellite data to obtain more rigorous scientific answers.
# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 2 Background
### Grad-CAM  
The classical image classification problem is one of the most popular tasks for Deep Learning models. It consists of classifying images that contain items or generic shapes (such as typewritten letters) with the highest accuracy possible. The deep learning model completes the task by leveraging the information of a dataset of input samples. In the training phase, the deep learning models extract and memorizes features and patterns peculiar to a specific output class, thus learning how to distinguish between the different input samples.  
One of the most widely used deep learning models for image classification is the Convolutional Neural Network (CNN), which exploits mathematical convolutional operators on the input image to extract features. The input images pass through several layers of convolution, to combine the pixels with the neighboring ones, and subsampling, to reduce the size of the two-dimensional matrix while preserving the most relevant information. Finally, the last part of the CNN is usually composed of dense layers, which are formed by a variable number of _perceptrons_); this last part of the model performs the classification, and it is trainable with the standard backpropagation algorithm. We refer to the literature for further information on CNNs [30; 31].  
Many complex CNN variants were proposed in the literature; mainly, they differ in the size of the architecture and the number of convolutional layers. For instance, AlexNet introduced the use of ReLU as the activation function instead of the then function and optimization for multiple GPUs. It also addresses overfitting by using data augmentation techniques and the dropout layer. In this paper, we experimented with this architecture and other architectures, by considering also a CNN designed by the authors. We refer to the original papers for further information on their architectures [32; 33].  
Gradient-weighted Class Activation Mapping (Grad-CAM) is a technique to extract the gradients of the deep learning models' convolutional layers and use them to provide graphical information on the inference step. Briefly, the gradients capture high-level visual patterns and can describe which areas of the input image have influenced most of the model output decision. Furthermore, the convolutional layer preserves spatial information; thus, Grad-CAM uses this data to provide a heat map of the input image. This heat map highlights the input image area that was used by the deep learning model to classify a specific input; it provides a visual \"explanation\" to a certain decision. The Grad-CAM adopted in this work is an implementation of the one introduced by the paper [34].
# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 3 Hyper- and Multispectral Imaging
### Chemometric Models
In Equation (2), Y is the reference value, X is the spectrum matrices (m \(\times\) n), B is the PLS-R coefficient, and E represents the error:  
\[\mathrm{Y}=\mathrm{XB}+\mathrm{E} \tag{2}\]  
Similar to PCA, PLS-R has the ability to reduce the high dimensionality of data matrices [67]. Practically, PLS-R has the ability to predict plant traits [76; 77], food chemical contents [78], and other characteristics. In addition, various chemometric techniques, such as partial least squared discriminant analysis (PLS-DA), support vector machine regression (SVM-R), and least squared support vector machine (LS-SVM), can be applied. Despite the development of the aforementioned chemometric techniques, at present, deep learning (DL) has been favorably developed and used for HSI/MSI data analysis. The concept of DL architecture imitates the principle of the human brain's visual cortex. DLoffers some advantages: (a) it discovers properties on its own; (b) it saves the process by reducing the need for computation; (c) it creates features manually; and (d) it uses large, annotated images that researchers already have access to [79]. However, supervised DL needs a large-scale dataset. In addition, DL-based spectroscopy is rarely used to quantify plant phenotypic features. Nevertheless, this method can automatically extract raw spectra and improve the performance of prediction models [80]. Hence, it is still possible to combine DL with spectroscopy during plant phenotyping. In Table 2, we summarize recent applications of DL for monitoring plant growth status. The detailed information of the use of deep learning in combination with spectroscopy can be found in [PERSON] et al. [81]. [PERSON] et al. [81] provides details of major types of DL (convolutional neural network/CNN, fully convolutional network/FCN, tensor learning model/TL, deep belief network/DBN, stacked auto-encoder/SAE, recurrent neural network/RNN, semi-supervised learning, generative adversarial network/GAN, and active learning model/AL) and challenges in the agricultural field.
## 4 Multi-Objective Optimization of Frame Structures
### Optimized Design  
A multi-objective genetic algorithm (MOGA) [37] is employed to design the dimensions of a frame structure. The goal is to reduce the structure's maximum axial amplitude and enhance axial modal frequencies. Additionally, the algorithm considers the maximum equivalent stress and maximum deformation, ensuring that these objectives remain within a safe range.  
The multi-objective optimization model is represented as follows:  
\[\left\{\begin{array}{l}x_{\min}\leq x\leq x_{\max},x\in D\\ F(x)=\min\{F_{1}(x),F_{2}(x),\cdots,F_{n}(x)\}\\ G_{i}(x)\leq 0\hskip 28.452756 pti=1,2,\ \cdots,m\\ H_{i}(x)=0\end{array}\right. \tag{13}\]  
In Equation (13), \(x\) is the design variable; \(x_{\min}\) and \(x_{\max}\) are the range of values for the design variables; \(F(x)\) is the optimization objective; \(D\) is the design space; \(G_{i}(x)\leq 0\) and \(H_{i}(x)=0\) are the \(i\)-th constraint inequality, which is used to determine the feasibility range of the optimal solution.  
This paper constrains the structure size and provides an optimization target range. The priority of multi-objective optimization from top to bottom is vibration response, modal frequencies, maximum deformation, and maximum equivalent stress. The results show that both the maximum equivalent stress and maximum deformation are within the requirements for use. The Pareto solution set for maximum vibration response and modal frequency is shown in Figure 16.  
Three groups of solutions satisfying the multi-objective optimization of frame structures are selected from the Pareto solution set, and the first- and second-order modal frequencies are required not to be within the operating shaft frequency range. The structural design variables and corresponding optimization results are presented in Table 7.  
Figure 16: Pareto solution set.  
The comparison between the optimized predicted values and calculated verification values revealed that the maximum absolute error for the predicted maximum axial amplitude is 0.4872 mm/s, and the maximum absolute error for the predicted results axial modal frequency is 2.333 Hz. The results show that multi-objective optimization is effective for the vibration reduction design of fan frames. Both the maximum deformation and the maximum equivalent stress were increased, thus satisfying the structural strength requirements.
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 2 Design Issues and Routing Challenges in Green Industrial IoT Networks
### Data Heterogeneity and Flow  
Different applications may need to simultaneously transmit different types of data (critical, non-critical, real-time, etc.) over the same link and to the same destination. In some cases, the traditional wireless networks are assumed to be homogeneous in terms of the data generated by the devices, but IIoT networks are heterogeneous. In IEMS applications, it is normal to have multiple devices installed in different places for specific purposes. Hence, it generates different data traffic depending on the use cases, such as heat monitoring, water and gas leakage, emergency alarm, fault detection, etc. This requires that the underlying communication mechanism reliably handles data delivery with specific requirements in terms of service availability. An efficient channel assignment algorithm and an interference-aware routing scheme could be a solution to some extent for the heterogeneous nature of IoT networks [22].
## 5 Design of the MES System
### Modeling of MES System  
The goal of the MES development proposed in this paper is to develop an integrated platform that can be used as a collaboration system for different stakeholders related to the value chain, from product development to delivery to the customer. Therefore, the collaboration system should include four companies (brand manufacturer, fabric supplier, fabric printing, and sewing) and involve the customers who purchase the final product (sportswear). Production managers and employees of the four companies in this production network were identified as relevant stakeholder groups for the collaboration scenario. Real-time process information (order information) and information on quality issues (especially for the printing and sewing companies) were identified as the main needs of these stakeholders.  
Through the analysis process, we present four UML diagrams as follows.  
1. Component Diagram As the component diagram illustrates (Figure 8), a total of six components are included with the cloud platform as the focal component: * Cloud: central repository actor, where all information about the production process is stored. * Customer (individuals): user of the web store, who orders products. * Customer (brand manufacturer): actor that instructs the production by confirming the customer order. * Supplier (raw material): actor who supplies fabrics. * Manufacturer (printing)--thermal transfer printing company: actor who receives and prints the fabric. * Manufacturer (assembly): actor that performs sewing work after receiving the printed fabric.  
Important information exchanged in this collaboration scenario includes raw material information (RMI) of the garment fabric, WIP, e.g., for garments with sublimation printing, or finished goods inventory (FGP) for the garment to be delivered to the customer.  
When a brand manufacturer receives an order through the web store, it not only provides a set of information to process the order, but also receives information about the status of goods in and out, from the material order to production process, through the dashboard in real time. In doing so, it is possible to create efficient and flexible production schedules by ensuring the flow of information between companies via the cloud and building a system that can actively respond to disruptions.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 5 Performance Evaluation
### Implementation  
The experiment was conducted in the Ubuntu 20.04 environment using Odroid, a single-board computer that can be mounted on a real drone. A network interface controller (NIC) was installed on Odroid to communicate, and the GPS address was obtained through MAVROS. The obtained GPS information was reflected on the private A-class IP address and connected through an ad hoc network to form a UAV swarm network.  
The UAV server used Python to configure the simulation environment, and the location information of the UAVs was updated when they arrived at the server [33]. It was assumed that the UAV swarm followed a constant formation topology and a 2 \(\times\) 2 cube topology was used [34]. After the positions of all UAVs in the swarm were updated on the simulation, when a data transmission request arrived from one UAV, the source node and destination node were set, and the appropriate FR angle was set as the default at the current position. Learning was started to optimize the fixed FR angle according to the situation.  
Prior to full-scale learning, the location was randomly changed within the mobility error range of the UAV for each episode to form a network suitable for the UAV network. Figure 8a is an example in which 27 UAVs form a 2 \(\times\) 2 cube topology and the network range is set to 42 m. Figure 8b-d are examples with the mobility error set to 13 m.
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 4 Dose Calculation Algorithms for Hadrontherapy
### Monte Carlo Algorithms  
Similarly to their use in photon therapy (see Section 2.3), Monte Carlo calculations offer higher accuracy than analytical methods in hadrontherapy. MC accurately handles heterogeneous material, scattering and fragmentation, at the cost of a considerably higher computational effort. Several general-purpose codes have been extensively used in particle therapy, including FLUKA [63] and Geant4 or TOPAS, its therapy-specific derivative [64]. Both codes have features to support particle therapy, such as the capability to import DICOM CT images. These tools feature powerful simulation capabilities and permit very accurate simulations. As they are not dedicated to therapy applications, they tend to be slow. Especially for proton therapy, tailored solutions are under development, such as Fred [65], gPMC [66] and Mcsquare [67]. In these codes, scoring is adapted to therapy requirements, allowing for faster execution, and careful simplifications are introduced, such as reduced nuclear interactions, neglecting neutrons or the local resolution of delta electrons. This permits streamlined, massively parallel calculations, ideally suited for GPUs, and leading to calculation times of minutes for entire fields, with clinically acceptable inaccuracies. MC calculation is much more complex for heavier particles due to the complex fragmentation, which leads to a significant amount of heavy, energetic secondaries that have to be tracked. The dedicated codes mostly focus on protons, and ion beam calculations are considerably more time-consuming.  
It should be noted that the Monte Carlo code outcome is highly dependent on the quality of the underlying data and models. Cross sections of ions in the therapeutic energy range are not always precisely known, especially for secondary fragments. A recent example of the impact of nuclear cross sections is the experiment of [PERSON] et al. [68], showing significant uncertainty in helium cross sections with a strong dosimetric impact.  
Monte Carlo simulations are essential for the prediction of secondary emissions and isotope production that can be used for range verification, such as prompt gamma detection [69] or PET imaging [53,70]. The emissions do not coincide with the Bragg peak, so MC predictions are necessary for comparison with the acquired data.
# TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks
## 6 Conclusions
In turn, the numbers of data packets received and decoded by GSs from particular satellites are integers (e.g., 925,348 and 827,571, respectively) and were used in Topics 9, 10, 11, 12, and 13. Finally, the downlink frequency of satellites, also referred to as the satellite frequency (the frequency at which the satellite is transmitting the signal and the ground station is receiving it, for example, 436.703 MHz), was used and rounded to whole units in Topics 10 and 12 for histogram creation. It is important to note that some satellites had more than one downlink frequency, for example, they could transmit signals at frequencies of 868.100 MHz and 2422.000 MHz, and in that case, both frequencies were considered. The authors acknowledge the International Partnership CMU Portugal Program and the project consortium entities: Universidade do Minho (PT), Instituto Superior Tecnico (PT) and Carnegie Mellon University (USA). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
ADC Analog-to-Digital converter API Application Programming Interface DC Direct Current FSK Frequency-Shift Keying GEO Geostationary Orbit GFSK Gaussian Frequency Shift Keying GS Ground Station LEO Low Earth Orbit LNA Low Noise Amplifier MQTT Message Queuing Telemetry Transport NOAA National Oceanic and Atmospheric Administration QFH Quadrifilar Helix SatNOGS Satellite Networked Open Ground Station SDR Software-Defined Radio SSID Service Set Identifier UHF Ultra High Frequency VHF Very High frequency
## 3 Results
### Results of the AdvEx-Based Analyses  
After the AdvEx-based analyses from the thermodynamic and economic points of view, AdvEx-based analyses are divided into two parts, with exergy and exergoeconomic analyses as a continuation of one other. First of all, exergy analysis is performed and then, related to the results of this analysis, exergoeconomic analysis is carried out. Therefore,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multicolumn{3}{c}{**TradEx Analysis Results**} & \multicolumn{6}{c}{**TradExEcon Analysis Results**} \\ \cline{2-11}  & \(\hat{E}_{\text{{\tiny{LR}}}}\) & \(\hat{E}_{\text{{\tiny{LR}}}}\) & \(\hat{E}_{\text{{\tiny{D,k}}}}\) & \(\epsilon_{k}\) & \(\epsilon_{k}\) & \(\epsilon_{k}\) & \(\epsilon_{pk}\) & \(\hat{C}_{\text{{\tiny{D,k}}}}\) & \(\hat{Z}_{k}\) & \(\hat{C}_{\text{{\tiny{tot,k}}}}\) & \(f_{k}\) \\  & **(kW)** & **(kW)** & **(kW)** & **(\%)** & **(USD/GJ)** & **(USD/GJ)** & **(USD/h)** & **(USD/h)** & **(USD/h)** & **(USD/h)** & **(\%)** \\ \hline \multicolumn{11}{c}{_LEVEL 1_} \\ \hline VAP 1 & 16,682.38 & 15,365.76 & 1316.61 & 92.11 & 1.20 & 1.81 & 5.69 & 18.60 & 24.29 & 76.58 \\ \hline PRE-HE 1 & 4273.64 & 3634.71 & 638.93 & 85.05 & 2.38 & 3.90 & 5.47 & 14.45 & 19.92 & 72.55 \\ \hline CON 1 & 6069.49 & 4094.42 & 1975.06 & 67.46 & 8.50 & 3.75 & 60.45 & 130.06 & 190.51 & 68.27 \\ \hline TURB 1 & 12,908.34 & 12,066.15 & 842.19 & 93.48 & 3.75 & 6.76 & 11.36 & 119.46 & 130.82 & 91.32 \\ \hline PU 1 & 2323.60 & 397.53 & 1926.04 & 17.11 & 6.76 & 57.82 & 46.85 & 16.55 & 63.40 & 26.10 \\ \hline \multicolumn{11}{c}{RECUP} \\ \hline \multicolumn{11}{c}{_LEVEL II_} \\ \hline VAP 2 & 13,950.34 & 11494.18 & 2456.16 & 82.39 & 3.48 & 0.02 & 30.80 & 19.88 & 50.68 & 39.23 \\ \hline PRE-HE 2 & 5678.41 & 4586.55 & 1091.86 & 80.77 & 2.76 & 4.29 & 10.86 & 14.45 & 25.31 & 57.10 \\ \hline CON 2 & 5553.70 & 3062.96 & 2490.75 & 55.15 & 7.35 & 2.56 & 65.94 & 130.06 & 196.00 & 66.36 \\ \hline TURB 2 & 10,801.98 & 9000.32 & 1801.65 & 83.32 & 2.56 & 6.76 & 16.59 & 119.46 & 136.05 & 87.80 \\ \hline PU 2 & 2192.20 & 274.95 & 1917.26 & 12.54 & 6.76 & 79.98 & 46.64 & 16.55 & 63.19 & 26.19 \\ \hline OVERALL SYSTEM & 53,771.73 & 21,028.56 & 16,876.69 & 39.11 & 8.40 & 9.73 & 510.55 & 707.83 & 1218.38 & 58.10 \\ \hline \multicolumn{11}{c}{\(E_{Lust}=15866.48\) kW and \(\hat{C}_{Lust}=479.99\) USD/h for the overall system.} \\ \end{tabular}
\end{table}
Table 4: The outlined findings of the TradEx and TradExEcon analyses for all the components in the Sinem GPP.  
the methodology given in Section 2.2 for the AdvEx analysis is applied to a GPP and its components. Then, the methodology in Section 2.3 is used for the GPP's AdvExEcon analysis. The obtained results are presented below.