esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antioxidant Activity Assays  
#### 2.4.1 DPPH Radical Scavenging Capacity Method  
The DPPH method is one of the simplest and fastest methods for the analysis of antioxidant activity with multiple applications. This antioxidant assay is based on electron transfer.  
The DPPH radical (2,2-diphenyl-1-picrylhydraz) is a stable and commercially available organic nitrogen radical that has an absorption maximum in the UV-Vis range at 515-517 nm (violet).  
The analysis was carried out as follows [32]: To 1 mL extract of several different concentrations (between 0.5 and 2.5 mg/mL), 0.1 mL methanol was firstly added, and then 2.9 mL \(\times\) 10\({}^{-5}\) M DPPH solution in methanol was added. The samples were homogenized with the help of a vortex and kept for 20 min in the dark.  
The decrease in absorbance was measured using the UV-Vis Spectrophotometer V 630 Jasco; (Jasco, Heckmondwike, United Kingdom) model at 517 nm.  
Trolox was used as a reference standard antioxidant compound, and for the drawing of the calibration line, 5 standard solutions were prepared by dilution in methanol with concentrations in the range of 0.2-1 mg/mL. The absorbance was measured for each standard solution against a blank of methanol.  
The percentage of DPPH remaining was calculated using the following equation (Equation (1)):\[\text{IC}\%=\frac{\text{A}_{\text{blank}}-\text{A}_{\text{sample}}}{\text{A}_{ \text{blank}}}\cdot 10 \tag{1}\]  
where Ablank is the absorbance of the blank (methanol-DPPH, methanol solution) and Asample is the absorbance of the sample mixed with 2.9 mL DPPH solution.  
The degree of inhibition or radical scavenging capacity was expressed as IC50 in \(\text{\mu g}\)/mL. IC50 represents the amount of antioxidant needed to reduce the initial DPPH concentration by 50% or the extract concentration that inhibits DPPH activity by 50%.
## 3 Results
### Generation of Spatial LAI  
The corresponding rice pixels were extracted from the classified map to generate spatial _LAI_. First, _NDVI_ values at corresponding rice pixels were extracted, and due to the chance of noise in rice pixels, _NDVI_ thresholds > 0.4 were considered. To determine RS _LAI_, the linear equation (Equation (6)) generated from the correlation between model _LAI_ and _NDVI_ was used.  
\[\textit{LAI}=9.5093\times\textit{NDVI}_{max}-1.8462 \tag{6}\]  
A spatial LAI map was generated using Equation (6). Figure 7 depicts that the maximum LAI was >4.5 in some areas with irrigation and canal facilities.  
Figure 5: Observed and model-simulated rice yields in the Kharif season.  
Figure 6: Comparison of model simulated LAI with NDVI.  
Figure 7: Spatial distribution of the LAI map for Karimnagar.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 5 RTT Measurements for Each Modulation at Several Temperatures
### Average RTT for 16 \({}^{\circ}\)C  
Figure 15 shows the values of RTT (in ms) at maximum distances as a function of the working frequency for 16 \({}^{\circ}\)C. It is also shown the typical error for each data transfer rate. We observe that the RTT value for 11 Mbps working at 2427 MHz is about 4.85 ms. This data transfer rate presents the greatest distance value in Figure 9. Figure 14 also shows that the highest RTT value is presented for 11 Mbps at 2432 MHz. As we can see that high distances does not mean higher RTT values. At 16 \({}^{\circ}\)C, the best combination of settings is using OFDM modulation at 11 Mbps.
## 2 Materials and Methods
### Methods
#### 2.3.3 Ensemble Classification  
In this study, an ensemble learning classification model based on AdaBoost was designed for land cover classification, where the random forest algorithm was used as the base classifier. Ensemble learning, a machine learning technique, aims to enhance overall performance and prediction accuracy by combining multiple models. Ensemble learning, distinct from reliance solely on a single model, capitalizes on the strengths of multiple models to attain superior generalization and robustness, thereby enhancing its capacity to handle the multifaceted scenarios of big data pertinent to this study. By harnessing the diversity of models to offset the weaknesses or predictive errors of individual models, classification efficiency can be further enhanced.  
AdaBoost (adaptive boosting) is a commonly used ensemble model, notable for its adaptability. This feature effectively addresses the multi-feature classification task in this study. The model introduces the random forest classifier as a sub-model in each iteration. Following each iteration, the model dynamically adjusts the weights of misclassified samples from the previous basic classifier, while decreasing the weights of correctly classified samples. These adjusted weights are subsequently utilized to train the succeeding base classifiers. Ultimately, upon reaching the predefined maximum number of iterations, a strong classifier is formed through combination.  
The choice of the random forest as the base classifier is due to its long-standing recognition as one of the most commonly utilized algorithms for land cover classification with remote sensing data [37; 38; 39; 40; 41; 42]. Comparative experiments on previously extracted features consistently demonstrate superior results achieved with the random forest method. Hence, random forest is selected as the base classifier for the final ensemble learning  
Figure 4: Network structure diagram of 3 DCNN.  
experiment. The ensemble classification method is depicted in Figure 5. Among them, the weak classifier is a sub-classifier. In this experiment, a random forest was ultimately chosen and integrated via ensemble learning to constitute the final strong classifier.
# VANET Clustering Based Routing Protocol Suitable for Deserts
## 1 Introduction
In cluster-based routing, virtual network infrastructure must be created through the clustering of nodes in order to provide scalability. The common types of cluster-based routing protocols are cluster base routing protocol (CBRP), Clustering for Open IVC (_Inter-vehicle communication_) Network (COIN) and Location Routing Algorithm with Cluster- Based Flooding (LORA-CBF) [12; 13; 14; 15]. However, considering the high mobility of vehicles, irregular roads and environmental factors, the traditional MANET and VANET routing protocols may not satisfy network communication and information exchange in these environments. Therefore, clustering routing protocols became one of the most important choices. This paper presents a VANET clustering routing algorithm appropriate for desert and other rugged environments. The proposed protocol provides a stable clustering structure and a reliable route between the source and destination vehicles. Additionally, it utilizes vehicles equipped with mobile or satellite links to act as a gateway for unreachable destinations. The cluster structure, cluster head election (CHE) and the routing protocol are presented and comprehensively described. A new CHE approach is also proposed and theoretically analyzed. Throughout the paper, numerical simulation is used to evaluate the proposed CHE approach. Simulation results are also conducted to evaluate the proposed routing algorithm, in terms of the packet delivery ration (PDR), end-to-end delay and cluster stability, and to compare it to other alternatives. The rest of the paper is organized as follows. Section 2 briefly introduces the most important related works. Section 3 describes the cluster structure of the general designed model of the network and its corresponding logical model. Section 4 describes the clustering process and presents the CHE procedure. Section 5 explains and extensively discusses the proposed routing protocol. Section 6 is the simulation results and observations. Finally, Section 7 concludes this work.
## 4 Conclusions  
This study focused on the influence mechanism of CO2 NBW as mixing water on the mechanical properties of cement-based materials, and it deeply explored the compressive strength, acoustic emission response and energy evolution of CBM specimens with different fractal dimension aggregate grading under the influence of CO2 NBW. The main research conclusions are as follows:  
1. The UCS of CBM specimens increases first and then decreases with the fractal dimension of aggregate gradation, and the optimal aggregate gradation range is the fractal dimension range of 2.4150 to 2.6084. In addition, CO2 NBW also significantly improves the strength of CBM specimens. However, due to the change in structural porosity caused by the change in aggregate grading, the strength improvement in the fractal dimension range of 2.4150 to 2.6084 is significantly smaller than that in the fractal dimensions of 2.2106 and 2.7824.
2. Acoustic emission testing shows that the aggregate range of 2.4150 to 2.6084 can effectively improve the pore structure of the material. Similarly, the addition of CO2 NBW also improves the structural pores to a certain extent. In addition, the cumulative ringing count first increases and then decreases with the fractal dimension. The addition of CO2 NBW significantly increases the cumulative ringing count and causes the cumulative peak to shift to the fractal dimension of 2.6084.
3. The energy evolution law of CBM specimens was obtained by calculating the energy evolution parameters. The results show that the energy evolution of CBM specimens can be divided into five stages, and the peak point of elastic strain energy corresponds to the peak value of the stress-strain curve. The addition of CO2 NBW and a reasonable aggregate grading interval effectively improve the values of various energy parameters. However, the addition of CO2 NBW reduces the proportion of elastic strain energy in the total energy. The reason is that excessive CO2 concentration will affect the hydration reaction of cement-based materials, deteriorating the overall bonding effect of the specimen.  
Conceptualization, H.S.; writing--original draft preparation, X.C.; writing--review and editing, A.H.; validation, T.S. All authors have read and agreed to the published version of the manuscript.  
Financial supports for this work was provided by China Scholarship Council (202206420019) and Obayashi Foundation (2023-research-33-57).  
Not applicable.  
The raw data supporting the conclusion of this article will be made available by the authors on request.  
The authors would like to thank the editor for handling this submission and the anonymous referees for reading the manuscript. In addition, this work was also strongly supported by the China Scholarship Council and the Obayashi Foundation, to which we would like to express our gratitude.  
The authors declare no conflicts of interest.
## 2 Propagation Mechanism and Particle Distribution
### Propagation Mechanism  
The scattering of electromagnetic waves is related to heterogeneity, such as the heterogeneity at the molecular scale and the aggregation of multiple molecules. Regardless of the type of heterogeneity, the basic physics of scattering is the same for all systems. As shown in Figure 1, if an obstacle, such as a single electron, atom, molecule, or solid or liquid particles, is irradiated by electromagnetic waves, the electric field of the incident wave causes the charge in the obstacles to oscillate. It can accelerate the charges to radiate electromagnetic energy in all directions. This secondary radiation is called the radiation of obstacle scattering, which means that the scattering equals excitation plus reradiation. In addition to reradiating the electromagnetic energy, the stimulated base charges can convert part of the incident electromagnetic energy into other forms, such as thermal energy, which is called absorption [28].
## 4 Performance Evaluations
### Simulations
#### 4.1.3 Comparison  
In order to compare and analyze the performance of the DAM-TEQ and the conventional TEQ based on timescale estimation under different underwater acoustic channel conditions, a comparative simulation is set up on the basis of the above simulations: \(v_{p}=1\) m/s, S1 is \(N_{p}=10\) and \(\Delta\tau=2\) ms, which simulates the situation that the channel is stable and good, and the relevant peaks of the LFM signal can be accurately obtained; and S2 is \(N_{p}=20\) and \(\Delta\tau=5\) ms, in which there is a propagation path similar to the energy level. It simulates the situation that the channel is bad, the multipath and attenuation are serious, and the relevant peaks cannot be accurately obtained. Under different SNRs, two equalizers are used to process the communication signals in different situations, and the processing results are displayed through the BER performance curve, as shown in Figure 7.  
The simulation results show that under the S1 condition, the conventional TEQ and the DAM-TEQ show excellent performance due to the accurate LFM signal correlation peak, and the conventional method works on an accurate timescale. However, under the condition of S2, the conventional TEQ cannot work accurately due to the influence of a poor channel, and the BER is maintained at a high level. In contrast, the proposed equalizer can still work normally, and with the increase in the SNR, the BER gradually decreases. The BER performance is significantly better than that of the conventional TEQ. The comparative analysis results verify the effectiveness and good performance of the DAM-TEQ.
### The Present Study  
The present study aimed at investigating the accuracy and reliability of consumer-grade portable and wireless EEG as compared to standard, wired research-grade EEG, using the Emotiv EPOC 14-channel headset and the Brainvision 32-channel headset. While, as already mentioned, there are many empirical studies and exhaustive reviews which have focused on addressing the validity of wireless consumer-grade EEG devices including Emotiv EPOC, few have directly addressed the issue of reliability of measurement, independent of the content-validity of the measurement; among the latter small literature, even fewer have compared EEG features in data collected from the same subjects using both types of devices, that is, using a repeated measures (i.e., within-subject sample-matching) design.  
Accordingly, to advance current research in the field, the present study contributed a novel approach for the direct assessment of the reliability and accuracy of wireless consumer-grade EEG devices, using as paradigmatic example case Emotiv EPOC. Specifically, we investigated the features of EEG power and amplitude measurements for specific frequency bands of matched electrodes (the 14 channels these headsets share are AF3 (FP1), F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8 and AF4 (FP2)) between the Emotiv device and the Brainvision device during different mental states in the same participants. In the next sections, we describe the EEG features and the level of psychological states considered.
## 7 Calculation Example
Figure 10 also shows that the damping effect, \(\xi\), is visibly worse than that of the critical-damping anti-swaying system. Figure 10: Influence of the hoisting rope length on the critical-damping anti-swaying system. Figure 12: Effects of \(e\) and \(f\) on \(k\). (**a**) The joint effect of \(e\) and \(f\) on \(k\). (**b**) The influence of \(e\) on \(k\) (\(f\) is constant). (**c**) The influence of \(f\) on \(k\) (\(\epsilon\) is constant). A comparison of Figures 7, 10 and 11 shows that, at the same system frequency of the anti-swaying system, with increases in the damping coefficient, \(\xi\), the attenuation of the load swaying angle changes from oscillation attenuation to exponential attenuation to hyperbolic sinusoidal (or hyperbolic cosine) attenuation successively. According to Equation (23), when the pressure and size of the anti-swaying hydraulic cylinders A, B, C, and D in the hydraulic system are determined, the longitudinal distance, \(e\), and the transverse distance, \(f\), of the anti-swaying fixed pulleys jointly affect the structure parameter, \(k\). As shown in Figure 12, when \(f\) decreases or \(e\) increases, \(k\) is likely to increase, which is favorable for the load anti-swaying effect. In engineering applications, if common standard containers and anti-swaying cylinders that are convenient to install are considered, \(e=6m\) and \(f=2.5m\) can be selected.
## 2 Materials and Methods
### Equipment  
For the electrical measurements inside the endometrial cavity, a custom-designed and -manufactured, flexible, low-profile, electrode array was used, with gold-plated electrode pads and dimensions of 400 mm (length) \(\times\) 1.5 mm (width) \(\times\) 0.25 mm (thickness). The signals of the electrode array were recorded using a commercially available Electromyography (EMG) Device [11]. For the TVUS assessment and measurements, the E8 Voluson (GE Healthcare, Chicago, IL, USA) ultrasound (US) device with abdominal and transvaginal probes was used.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Deployment Related Problems in WSNs
#### 3.3.4 Accuracy of Measurements  
Accuracy of measurements from the sensing area is of paramount importance for extracting any conclusion from the observed data. Efforts have been made towards acquiring accurate information relating to the area or the phenomena under observation. For example, deployment of optimal sensor hasbeen addressed in [113] by using a multi-objective optimization technique for simultaneous optimization of the probability of a successful search and the probability of false search. In [114] a multi-objective optimization algorithm has been suggested for optimal spectrum sensing in cognitive radio sensor network by achieving a trade off between probabilities of detection and probabilities of false alarm.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 4 A Proposed New Secured Unclonable Medical Watermarking Scheme
### System Analysis: Benefits of Combining SUC and Watermarking
#### 4.3.1 Protocol 1: Secured Logging of a Medical Image Transaction  
The first proposed generic protocol is designed to illustrate the process of generating a clone-resistant watermarked medical image. Medical device A generates a watermarked image and sends it to the TA server. Then, the TA server verifies the watermarked image and stores it in the DB.  
Figure 11 shows the proposed protocol which can proceed as follows:  
1. Medical device \(A\) asks the TA server to start the process of generating a watermarked image.
2. The TA server randomly selects a ticket \(\left(x_{A_{i}},y_{A_{i}}\right)\) from the medical device A's secret record in DB.
3. The TA server answers with \(y_{A_{i}}\).
4. Medical device \(A\) computes \(x_{A_{i}}\) by using its SUC as \(\text{SUC}_{A}^{-1}\left(y_{A_{i}}\right)=x_{A_{i}}\).
5. Medical device A generates or selects a medical image \(Ml_{1}\).
6. Medical device \(A\) generates a watermark \(WM_{1}\) from \(Ml_{1}\).
7. Medical device \(A\) signs the generated watermark \(WM_{1}\) by using a standard cipher \(E\) with the secret key \(x_{A_{i}}\) as: \(Z=E_{x_{A_{i}}}(WM_{1})\).
8. Medical device \(A\) embeds the signed watermark \(Z\) in the original image \(Ml_{1}\) to generate the clone-resistant medical watermarked image \(WMI_{1}\).
9. Medical device \(A\) sends \(WMI_{1}\) to the TA server.
10. TA server reverses the embedding algorithm to extract \(Z\) and to recover the medical image \(Ml_{1}\) from the received watermarked image \(WMI_{1}\) and then uses \(x_{A_{i}}\) to recover the watermark \(WM\prime_{1}\).
11. TA server generates \(WM_{1}\) from the recovered medical image \(MI_{1}\) and rejects if \(WM_{1}\
eq WM_{1}\).
12. TA server stores and registers the medical image transaction \(MI_{1},\ WMI_{1},\ WM_{1}\), and \(ID_{A}\), together with the used ticket \(\left(x_{A_{i}},y_{A_{i}}\right)\) in DB for later use.  
Protocol.1 attains the following security functions:  
1. Medical device \(A\) generates a clone-resistant watermarked image by deploying its \(\text{SUC}_{A}\).
2. The resulting watermarked image is authentic and tamper-resistant.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
## 4 Experiments of Medium-Baseline RTK Positioning
### Positioning Results of the 22.4-km Baseline
Similar results were also derived, e.g., in the work by [PERSON] and [PERSON] (2016). Anyway, the positioning performance with the inter-system differencing model is always better than that with the classical differencing model.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 4 Simulation Environment and Performance Metrics
### Malicious Nodes  
Nodes that intentionally drop data packets instead of forwarding them are known as malicious nodes. They are introduced to the network to test and analyze the performance of the proposed model. The TCL (Tool Command Language) script depicted in below and shows various nodes that act as malicious in a network with 20 nodes (Box 1).  
Standard AODV is used as a reference to check whether the proposed protocol can efficiently mitigate against malicious attacks without compromising the performance.
## 3 Experimental Setup
### Scenario 1: DRL-Based Scenario at the Signalized Intersection
#### 3.2.1 State Space  
The state of the agent is used to represent a description of the environment at a given time t and is usually denoted with \(s_{t}\). This state needs to provide sufficient information about the vehicle distribution on each approach so that the agent can learn effectively to optimize traffic.  
In this model, we applied the Discrete Traffic State Encoding (DTSE) [52] with a small adjustment to show the state space, inspired by an advanced technique in computing the discretization and quantization of elements. The model used a partially observed environment with data collected only from platoon vehicles (CVs). The whole incoming lane is divided into small cells from the stop line. The length of each cell c will affect the behavior of the model. If this length c is many times larger than the average car length, the individual vehicle dynamics may be lost. However, if this length is too small, it will increase the computational cost, leading to unnecessary complexity. The value in each cell is a binary value used to represent the presence or absence of vehicles in its cell.  
Based on this approach, we divided the segment from the starting point to the stop line into cells to discretize the traffic scene. The closer to the intersection, the smaller the width of the cells because it provides important information about the vehicle's state. The further away from the intersection, the larger the cell size, so as to reduce the amount of computation. There were 30 cells between the beginning of the road and the stop line of the intersection on each approach, as shown in Figure 7. Therefore, there were 120 cells in the whole intersection of different sizes.  
The model includes 2 types of vehicles: CVs and RVs. Depending on the operating modes, CVs include leader, follower, catch-up, catch-up follower, and waiting modes. Each cell in the state presentations only contains information about platooning vehicles (CVs) as shown in Figure 8. Based on CV presence, it has a binary value of 0 or 1. Many vehicles can be in a single cell, but the value of that cell is still only 1. In this model, the state description includes the following information: the number of CVs in each direction, CV position (distance to the intersection and lane position), and signal timing information.  
The state of the agent at time t can be expressed as \(\text{St}\in\text{S}\). With this state-space definition, the input data are taken from the Sumo simulation to the controller.