esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 7 Evaluations
### Results and Observations
This illustrates the system's dynamic response to interference, essential for secure drone communication in hostile environments. * Transmission Integrity: Packet integrity checks indicated no unauthorized modifications or data corruption, verifying the encryption and message authentication protocols in place. The HMAC verification success rate was consistent across all drones, reinforcing the communication channel's resilience under QRNG-driven FHSS. Figure 16: Drone 2: Frequency Sequence over 100 hops of the total 100,000 hops. Figure 15: Drone 2: Frequency Distribution, where a noticeable dip around 5500 Hz indicates a reduced frequency count in the jammed region. Overall, the combination of statistical tests and performance metrics provides a comprehensive assessment of the QRNG-based FHSS system's reliability. These results confirm its potential as a robust security solution for multi-drone networks, even in the presence of environmental interference and adversarial actions. Further enhancements to QRNG stability and random sequence generation are anticipated to improve performance in future deployments. To better position our work within the landscape of related studies, we provide a qualitative comparison table (Table 2). This table contrasts our proposed QRNG-based FHSS method with other security approaches commonly employed in wireless communication and drone networks. Key aspects compared include the type of security technique utilized, each study's adaptability to jamming attacks, the computational complexity, and the source of randomness in frequency generation. This comparison underscores the advantages of using QRNG-based true random frequency hopping for enhanced anti-jamming adaptability and security, particularly in applications where unpredictability is crucial.
# Effects of Different Variables on the Formation of Mesopores in Y Zeolite by the Action of CTA+
## 1 Introduction
This technique, described as surfactant-templating, was introduced to reduce the difficulty of preparing zeolites with mesopores, instead of materials composed of distinct regions (one mesoporous and the other zeolitic). USY zeolites with low contents of aluminum are sensitive to treatment with alkaline solution and readily undergo amorphization. For this reason, organic cations such as TPA\({}^{+}\), TMA\({}^{+}\), and CTA\({}^{+}\) alkylammonium cations can be used in the alkaline solution in order to preserve the micropore volume and the associated properties of the zeolite [13; 14]. In this work, USY zeolite (Si/Al = 15) was modified by alkaline treatment in the presence of CTAB surfactant, with subsequent hydrothermal treatment to form a substantial volume of mesopores. The main objective was to investigate the effects of different alkaline medium and surfactant concentrations on the structural, textural, and chemical properties of zeolites with mesopores, as well as to evaluate the role of the surfactant during modification.
## 5 Dynamic Tree (DT) Protocol
### Data Transmission  
Every fifteen minutes, collectors report electricity meter data to the concentrator. However, most of the time collectors are in sleep mode. The concentrator transmits the control message to collectors according to the routing table, while collectors report the data based on the _father_ID_ list. If a child collector detects that one of its parent collectors does not respond, it can choose another spare parent collector to relay the message. If all of its parent collectors fail to respond--the situation may be caused by network structure change--it should rejoin the network. In other words, this collector needs to reset _DtA_ to _DtA_Max_ + 1 and send request messages, RM. If a collector receives an RM from its parent collector and its _packet_DtA_ does not equal _DtA_-1, it needs to remove the corresponding item from the _father_ID_. A special case is when the _father_ID_ list contains only one single item: the collector should also rejoin the network. Making use of this strategy, EMMNet can maintain connectivity without frequent network reconstruction, even if some collectors die because of non-recoverable errors or malfunction.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 4 Location Determination with Mutual Base Station Authentication
### Protocols of the Distance Determination Which Have the Property of Anonymity  
Now, we will consider the protocol where the calculation is carried out at the station side. The corresponding protocol is shown in Figure 8.  
Figure 7: A distance-bounding P-AF-V protocol with the property of anonymity (calculations are made on the vehicle side).  
In this protocol, we use a fully homomorphic encryption (FHE) [88]. This technique is detailed in [89; 90]. We repeat all the steps (excluding the last one) from the protocol of vehicle-side distance calculation, shifting this calculation to the side of the base station. Further, sending the result of the homomorphic transformation \(F(t_{VS})\) to the base station, where the distance to the vehicle is calculated, or using the information received from other stations, its location is determined, the value \(F(d_{VS})\) or the result of a homomorphic transformation of the coordinates of the car. After this, the received information is delivered to the vehicle. The use of a homomorphic transformation obfuscates the true values of \(t_{VS}\) from the base station, and the vehicle determines the true value of the distance to a particular station or its location in this case. This allows for avoiding complex calculations at the vehicle side, shifting the calculation workload to the BS side.  
Here, we compare the discussed protocols and also elaborate on the improved one. Further on, \(n\)--is a message length; \(\lambda\)--the system security parameter. Let's consider our protocols and estimate three factors: number of operations, the storage space, and the complexity of the calculations as: Random number generation--\(O(1)\); Transmission--\(O(1)\); Hashing, Encryption  
Figure 8: A distance-bounding P-AF-BS protocol with the property of anonymity (calculations are made on the base station side).  
(MD5)--\(O(n)\)[91]; Signature--\(O(n)\)[91]; Verification--\(O(n)\)[91]; Measurement--\(O(n)\)[92]; Calculation (subtraction)--\(O(log(n))\)[92]; XOR--\(O(n)\)[93]; and Homomorphic encryption--\(O(\lambda^{35})\)[94].  
From the Table 2 it can be seen that the less computationally expensive protocol is the improved distance-bounding one because there are less operations, as it is shown in Figure 9, and only one random nonce is stored. Of course, the last proposed protocol is the most difficult one which is mainly due to the introduced anonymity improvement.
## 3 Results and Discussion
### Fusion Model Building and Discriminant
#### 3.3.3 Discriminant Model Based on Decision Level Fusion  
The proportion of two independent sub-models to data fusion model plays a key role for decision level fusion. During the data analysis, the non-negative least squares (NNLS) method was used to calculate the proportion of two sub-models. When NNLS is used as a special method to solve the optimal problem with the multiple linear regression method, the coefficient of NNLS should be positive [22; 51; 54]. The principle is shown in Equation (8):  
\[Y=a_{1}x_{1}+a_{2}x_{2}+\ldots\ldots+a_{i}x_{i}+b \tag{8}\]  
where \(Y\) is the prediction value of the fusion model, \(a_{i}\) the proportion value of \(i\)th sub-model, \(Y_{i}\) the prediction value of single band range spectral model, and \(b\) the deviation of the fusion model. Equation of sub-models based on the second derivative spectrum in the range of 500-1000 and 1000-2500 nm, respectively, are shown in Equations (9) and (10):  
\[Y_{1}=\sum_{i}^{665}\beta_{i}x_{i}+b_{1} \tag{9}\]  
\[Y_{2}=\sum_{i}^{1555}\beta_{i}x_{i}+b_{2} \tag{10}\]  
The two intercepts, \(b_{1}\), \(b_{2}\), were 11.22 and 9.39 in two ranges, respectively. The regression coefficient curves of the model on the two bands are shown in Figure 5. The corresponding weights of spectral variables at different wavelengths in Figure 5 are represented by model regression coefficients, which can obviously obtain important wavelength points within the range of two wavebands. By the model regression calculation, the weight of the different spectral variables in the two bands was provided, and the sum was weighted and the intercepts were added to realize the qualitative discrimination of five types of samples. The decision level fusion model is shown in Equation (11); the results show that the identification accuracy of NNLS data fusion model based on PLS-DA is 94%. The proportions of two sub-models are 0.73 and 0.32.  
\[Y=0.51Y_{1}+0.52Y_{2}-0.28 \tag{11}\]  
Points with high weights in Figure 5a are 540, 560, 760 and 840 nm. The main reason is that the absorption of chlorophyll composed of C-H, O-H and N-H groups at this point is stronger, while the chlorophyll in yellow dragon disease leaves is less than that in normal leaves. In Figure 5b, the points with high weights are 1090, 1430, 1930 and 2240 nm. The first two points are mainly caused by the first-order frequency double-stretching vibration of the O-H bond of water and sugar, and the last two points are mainly caused by the stretching and deformation vibration of O-H group [29; 49].
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 3 Adaptive Techniques in DSKF
### LBCA-Based LUT-DSKF  
The convergence of the DSKF's Kalman gains at the steady-state region can reduce the complexity of the algorithm. From the DARE equation (see Equation (31)), the relation between the steady-state Kalman gains \(\mathbf{K_{ss}}\), \(q\), and \(R\) can be derived (see Equation (34)). This relation simplifies the algorithm as shown in Figure 7. Figure 10 presents the LBCA-based LUT-DSKF. The same steps as in the LBCA-based DSKF to calculate the updated loop bandwidth \(B[n+1]\) are followed (see Equations (43)-(46)). In contrast to the LBCA-based DSKF, the mapping between Kalman gains and loop bandwidth is directly done using Equation (38).
The contribution of this study is three-fold: (1) To the best of our knowledge, this is the first study that employs the CWD to analyze EEG signals for detecting pain. Therefore, our work can be viewed as a feasibility study that aims to explore the potential of developing objective clinical pain detection systems based on time-frequency analysis of the EEG signals. (2) A novel set of time-frequency features are extracted from the constructed CWD-based TFR of the EEG signals to model the no-pain and pain classes encapsulated within the EEG signals, (3) Extensive feature and channel selection analyses are performed to quantify the effect of excluding any of the extracted time-frequency features and any of the utilized EEG channels on the accuracy of pain detection. The remainder of the paper is structured as follows. In Section 2, we describe the experimental protocol, the recorded EEG signals, the CWD-based time-frequency analysis, the extraction and classification of the features, and the performance evaluation procedures. In Section 3, we present and discuss the results. In Section 4, we conclude our findings and provide future directions.
## 3 Aircraft Low Altitude Operation Scene
### Design Requirements of Avionics System for Low-Altitude Operation Mode of Aircraft
#### 4.1.4 Design Requirements of Air-Ground Data Real-Time Transmission  
Usually, when the aircraft is flying at a high altitude, in addition to the necessary ground and aircraft operation communications, the aircraft is basically in a relatively independent operation environment, and the data transmission does not affect flight safety if there is an appropriate delay. When the aircraft enters low-altitude airspace and operates here for a long time, the air-to-ground data of the system needs to be transmitted in real-time for the safety of operation, such as the operation of low-altitude unmanned aircraft. When the avionics systems of low-altitude operational aircraft carry out the real-time transmission of air-ground data, the delay time between airborne systems and between airborne systems and ground networks should be strictly defined; the redundancy design requirements should be defined to ensure the true and real-time transmission of air-ground data.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Regression Strategy and Optimization
#### 3.3.1 Loss Function  
We use the Mean Squared Error (MSE) as the regression loss function:  
\[\mathcal{L}_{\text{MSE}}=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2} \tag{8}\]  
where \(y_{i}\) is the ground-truth carbon stock, \(\hat{y}_{i}\) is the predicted value, and \(N\) is the number of samples. MSE is suitable for continuous regression tasks and penalizes larger prediction errors more heavily.
# Digital Holographic Positioning Sensor for a Small Deployable Space Telescope
## 4 Data Processing Algorithm and Sensitivity
Then we calculate the measurable misplacement as an abscissa shift along this tangent, which corresponds to the intensity change by 1/256 fraction. Then the minimum deviation of the primary mirror segment position measurable with the symmetrical \(m_{1}\) metric varies from 17.9 to 2.1 \(\upmu\)m--see points 1 and 2, respectively. Similar values for the \(m_{2}\) metrics are 0.8 \(\upmu\)m and 2.9 \(\upmu\)m. So, the criterion based on the Fourier orders asymmetry appears to be much more efficient. Finally, the limiting value for the \(m_{3}\) metric proposed for the secondary mirror position changes from 0.1 to 0.5 nm. Thus, the sensor allows to measure the element position with an accuracy exceeding the requirements found in Section 2. Figure 8: Metrics sensitivity to the optical elements misplacement: (**A**) primary mirror vertical segment; (**B**) primary mirror tilted segment; (**C**) secondary mirror.
## 5 Conclusions  
Single-channel blind source separation algorithm (SCBSS) has important theoretical and practical values in the multi-fault feature extraction for mechanical equipment. The main work in this paper is summarized as follows: (1) A novel regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) method was introduced for signal decomposition. It not only inhibited the mode mixing problem during the whole decomposition process, but also had superiority without parameter selection. As it produces the better performance of adaptive signal decomposition, it was first applied to the blind source separation of a single-channel composite signal in health monitoring of key mechanical equipment; (2) Based on the Bayesian information criterion and singular value characteristics generated by SVD model, the number of source signals was accurately estimated; (3) Through theoretical analysis and experimental tests, it was shown that the method proposed in this paper could effectively extract the fault features and solve the problems of multi-fault separation. Therefore, the proposed method could serve as a powerful tool in multi-fault identification.  
Figure 13: Result of blind source separation provided by EEMD. (**a**) The first component obtained by EEMD; (**b**) The second component obtained by EEMD.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China under Grants Nos. 51475339, 51405353 and 51105284; the Natural Science Foundation of Hubei province under Grant No. 2016 CFA042 and the State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology under Grant No. ZR201603.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 System Overview
### Adaptive Performance Control  
The AMDS scheduler, proposed in this paper, combines the multi-distance scheduler approach with some adaptive methods to further reduce sensor scan sizes, scan matcher search window sizes, and iteration for pose and submap generation based on SLAM computational power load. If the load is heavy where the system cannot achieve the real-time online computation, the SLAM tries to reduce these variables to reduce the performance penalty. The load weight is obtained by measuring the time needed to generate a pose each time. If the time needed is more prolonged than a specified threshold, it activates the adaptive control. The threshold values are dependent on the sampling rate of Cartographer. A 20 Hz sampling rate of pose generation requires a processing speed less than 50 ms to achieve real-time performance. These threshold values must be set much lower than the sampling rate so that the delay does not overstep the boundary limit of real-time speed. The reason for this adaptive control is that the real-time performance is very crucial in autonomous vehicles. As the SLAM progresses through the unknown lane, building more maps, the SLAM is burdensome as the map grows [25]. In addition, the accuracy of the map and pose likely have a low impact when using adaptive methods to reduce the scan matcher, as the accurate initial map obtained from the initial SLAM is still utilized to create a bigger map. Similarly, a good initial map computation can result in correct end pose computation. Figure 4 shows the algorithm of the proposed method:
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 3 Status Quo of Key Technologies of Smart Water Conservancy
### Security Technology Based on Blockchain  
At present, the challenges of data security and authenticity exist in the field of water conservancy digital twin simulation. An innovative technology [78] combines blockchain and smart sensors to establish a blockchain network for water conservancy projects, realizing real-time collection, transmission and monitoring of water conservancy service data, ensuring that data cannot be tampered with and traceability, and meeting the needs of multi-party collaboration and real-time monitoring. Another technology [79] inputs digital scene data and hydraulic service data into the blockchain module, realizes intelligent perception of hydraulic service data by using intelligent sensors, and establishes a data flow mechanism by using a distributed ledger to enhance data credibility and authenticity. These technologies ensure high reliability and fidelity of data through blockchain, realize data exchange and sharing, and ensure the security of water conservancy digital twin data links.
# A Survey on Cyber Risk Management for the Internet of Things
## 4 Cyber Risk Assessment for IoT Survey Results
### IoT Cyber Risk Calculation
#### 4.2.2 Use of Risk Matrices
The risk matrix describes the compliance class score and an attack's probability of occurrence, shown within a scale of 1 to 10, which reflects critical, high, medium, low, and null risk levels. Since IoT and attacker behaviour are non-deterministic in nature, [PERSON] et al. [63] use a Markov Decision Process model to represent system states. The authors use a transition probability matrix which represents the transition probabilities of all states, since moving from one state to another within an IoT system is probabilistic based on the current state and the action that triggered the transition. This non-deterministic and dynamic nature is also reflected within [PERSON] et al. [44] who use dynamic Bayesian networks to model the dynamic nature of IoT and produce probability of attack matrix is also used to express the conditional probability. [PERSON] et al. [47] propose a methodology for assessing cyber security risks in water management systems. After uncovering the assets in the system and creating a threat list using EBIOS, the authors use a threat-asset matrix to map potential threats to each asset. This mapping allows each threat to be linked to specific security objectives and the countermeasures needed to achieve those objectives. _Insight 9:_ Another method for RQ2 is the use of a well-defined IoT risk matrix, which can be an easy way to show risk. It is integral for a risk matrix to have a defined set of risk values that allow for the evaluation of risk. A basic IoT risk matrix displays risk by mapping a threat by its impact and likelihood value, for example, [PERSON] et al. [74] denote risk on a very low to very high scale depending on impact severity and probability of an attack occurring. Interestingly, IoT risk matrices are often used alongside other methods, for example a risk matrix can be used alongside graph modelling, with [PERSON] et al. [53,54] using graphs and a corresponding adjacency matrix. The non-deterministic nature of IoT and attackers needs to be considered when assessing risk, with an IoT risk matrix potentially being used to model dynamic system nature, for example producing probability of attack matrix from a Bayesian network model [44] or using a Markov Decision Process model to produce a transition probability matrix [63].
## 1 Introduction
At present, there are several putative marker compounds for food irradiation, but there is no confirmation of the correctness of the choice of these compounds as markers [29; 30; 34]. Continuous monitoring of the data obtained by analyzing a large number of samples of different types of products is necessary. In order to extract the most important information when analyzing experimental data, a large number of machine learning methods are applied in modern analytical chemistry. Recently, the following methods have been used to analyze the obtained multivariate data: principal component analysis (PCA), projection to latent structures (PLS), discriminatory analysis using regression to latent structures (PLS-DA), artificial neural networks, and formally independent modeling of class analogies (SIMCAs) [35]. The listed methods allow the classification of the obtained data (spectra, chromatograms, etc.) into groups. One of the simplest variants is the \"fingerprint\" method. Fingerprint modeling and analysis are widely used in food recognition or quality control. In [36], the whole process of converting GC-MS data into olfactory results is presented, including instrumental and sensory analysis, identification of odor compounds, the process of structuring GC-MS data by modeling fingerprint images, and prediction using convolutional neural networks. GC-MS fingerprints are very complex, and similar samples may be indistinguishable. Thus, the usage of unsupervised methods for data complexity reduction should ease the tasks related to the classification of complex mixtures and impact-derived marker discovery, which is especially crucial for food objects. An important task today is the search for marker compounds indicating exposure of products to ionizing radiation. Such markers can be VOCs detected in products of animal and plant origin before and after irradiation. In this study, we aimed to develop an analytical method combining qualitative analysis of products for VOC content by GC-MS and the principal component method for discrimination of irradiated and non-irradiated samples.