esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model and Experiment Setup
### Conceptual Model  
Figure 3 displays the use of Mission Planner software, which is used as a ground control station for the drone, where vital configuration and setup can be performed to attain the optimum performance of aerial vehicles. The strips search technique is used when flying a drone to conduct search-and-rescue missions autonomously. When the drone flies autonomously to a targeted area, two main steps should be considered. The first step involves using thermal camera and a GPS sensor to detect missing people and identify their locations. The second step involves a reliance on the optimized Hata-Davidson propagation model, which enhances connectivity to report cases for rescue and medical aid. These two steps can be visualized in more detail in Figure 4, as a flowchart. The section includes two subsections. The first presents the mathematical concept supporting the proposed framework. The second describes the hardware and software architecture used in the experiment.  
Notably, the experiment focuses on using a drone with a thermal camera and communication payloads to fly over disaster zones in rugged areas and detect lost people as part of robust and efficient search-and-rescue operations. Thus, an optimum-channel wireless communication model should be optimized to achieve four main objectives between the drone and the control station: 1--strong wireless connectivity; 2--wide coverage footprint; 3--high-throughput transmission; and 4--low power consumption and, thus, longer drone flight time.  
Figure 3: Strips searching technique performed by a drone.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 3 Methodology for the Quality Assessment Metrics Evaluation  
In this section, we describe the methodology used to evaluate the performances of the adapted metrics described in the previous section. First, we present the HDR image databases annotated with subjective score or Mean Opinion Score (MOS). They are used to compare the objective metrics quality scores to a ground truth. Then, we present the performance indexes used to perform this comparison.
# A Literature Survey on Offline Automatic Algorithm Configuration
## 3 Comparative Studies
Finally, based on experimental results, the best method is CMA-ES when the quality of parameter configuration is the primary goal, and SPO when detailed information about the parameter space is required. In [PERSON] et al. (2020), since there is not a comparative study about automatic algorithm configuration of vehicle routing algorithms, they addressed this problem and comparedalgorithm configuration methods, and searched for the most convenient method for configuring vehicle routing algorithms. They compared and evaluated the capabilities and robustness of seven parameter tuning techniques for tuning the vehicle routing metaheuristics. They tuned eight metaheuristics for solving two vehicle routing problem variants, and their performance criteria were the solution quality. In this study, compared parameter tuning techniques are CMA-ES, GGA, 1/F-Race, ParamILS, REVAC, SMAC, and URS (uniform random sampling). They demonstrated the improvement obtained by using automatic algorithm configuration methods on vehicle route optimization metaheuristics. The results of their comparison demonstrated that, while some tuning methods perform better in some parameter tuning cases, there is not a unique method that outperforms the others at all times. They also argued how to carefully select and use the right configuration method to improve vehicle routing solver performance. The results of this study can help vehicle routing problem researchers select the most appropriate configuration technique. Previously mentioned offline configuration techniques require performing a significant number of independent runs of the metaheuristic to obtain meaningful information. [PERSON] et al. (2018) investigated the use of this performance information to discard components of a metaheuristic that do not lead to any improvement in its performance, in their terms \"ineffective components\". They experimentally investigated the information obtained from three parameter tuning methods: ParamILS, F-Race, and Revac. Additionally, experiments showed that ParamILS was the best method for identifying ineffective components. They also summarized the main differences between these three tuning methods. For future work, they suggest studying other configuration methods such as the irace procedure and aiming to discover effective components, not ineffective ones. [PERSON] and [PERSON] (2019) suggested an effective model-free fine-tuning method, NODOM-C, for detecting ineffective components/strategies of an optimization algorithm to decrease the target algorithm's complexity without loss of performance (it does not aim to find the best parameter values for a target algorithm configuration scenario like other methods). We listed the comparative studies of automatic algorithm configuration methods in the Table 4. Because we mentioned some of these studies in the previous sections, we did not refer to them again in this section.
## 6 Model Analysis
### Throughput Analysis  
Let the total system life time be \(T\). The steady-state probabilities \(\pi_{i}\) can be interpreted as the proportion of time that the CTMC spends in state \(i\). In the good state \(G\) and timing attack state \(T\), the number of jobs precessed is \(\lambda(\pi_{G}+\pi_{T})T\), given that the queues are stable \((\lambda<\mu)\). While in the rekeying state \(R\), the sensors do not transmit any data to the gateway node. In the compromised state \(C\), all of the data dispatched to the gateway node are not secure, so they do not contribute to the throughput. Therefore, the system throughput is:\[X= \frac{\lambda(\pi_{G}+\pi_{T})T}{T} \tag{11}\] \[= (\pi_{G}+\pi_{T})\lambda\.\]
# Post-Quantum Migration of the Tor Application
## 3 Methodology and Design
### Code Performance  
With regard to the three layers, the list of C files below represents the core Tor protocol operations; they will be assessed on the performance of their most computationally expensive functions.  
* The circuit_establish_circuit function in circuitbuild.c handles the process of selecting paths and managing the handshake protocols between nodes to establish circuits.
* onion.c is responsible for the creation, encoding, and parsing of cells (CREATE, CREATED, EXTEND, and EXTENDED). It invokes handshakes.
* channel.c implements the transmission, reception, and processing of cells across different connections.
* relay.c manages RELAY cells, including their encryption, decryption, forwarding, and processing for routing data.  
The following code files will be evaluated to weigh the cryptography operations they represent:  
* onion_ntor.c implements the not handshake.
* onion_ntor_v3.c, as specified in proposal 340 [5], requires the enhanced handshake, ntor v3, for cell fragmentation.
* relay_crypto.c handles relay cell payload encryption, decryption, integrity verification via cryptographic digests, and the initialisation of symmetric keys.
* hs_ntor.c is closer to ntor v3 than ntor; it is the implementation of the handshake for onion services.
* tortls.c is the main TLS implementation file in Tor.
* channeltls.c is the only instantiation of channel abstraction. It handles the v3+ link handshake, certificate verification, and cell processing over OR connections.  
In Tor's \"crypto\" code directory, the deprecated fast handshake file onion_fast.c is ignored. A frequency analysis will be conducted to associate the roles of nodes with the functions they execute the most.
# CDA-Net for Automatic Prostate Segmentation in MR Images
## 4 Experiments and Discussion  
### Implementation Details  
In our experiments, the training and inference of the proposed algorithm CDA-Net were implemented in Tensorflow and Keras with Tensorflow backend. Additionally, all the experiments ran on a PC with Intel(r) Core(tm) i7 3.00 GHz processors, 32 GB of RAM, and 1 NVIDIA GeForce RTX 2080 SUPER Graphics Processing Unit (GPU).  
The model training in our algorithm used Adam [49] optimizer with default parameters \(\beta^{1}=0.9\), \(\beta^{2}=0.999\), \(epsilon=10^{-8}\). Among them, the initial learning rate of RAS-FasterRCNN is set as 0.1, and the iteration is 70 times. The initial learning rate of the RAU-Net model is set to 0.1, and the iteration is 50 times. The total training time of the two networks is about 12 h. The average processing time of each test slice image is approximately 0.4 s.
## 6 Discussion
### Implications of the Work  
This system is designed to address situations where only past decisions of the users are available (e.g., there are no ratings by the users of the items). This is a very common situation, and yet recommendations are still needed and can be produced by estimating the interest drift of user preferences from past choices across items, and the pattern and sequence of past choices across sessions and particularly in the current session.  
The ASLM can be applied by recommender systems in the scenarios of news, music, and e-commerce (e.g., Reddit, Last.fm and Tmall, respectively) where the user's interest drift is the main topic since it takes place frequently. The model depends on the data availability because it is a data-driven method, and continuously requires the user's session data to process when running. During the experiment, it took about 28 min for the ASLM to run every epoch on Reddit dataset which contains 1,135,488 user sessions with a NVIDIA GTX 1080 Ti graphic card. In other words, it took about 1.5 ms for each session, and that is appropriate for real-time recommendation. Therefore, the system can be employed online and executed in real-time, although we conducted the experiment offline.  
Figure 9: Cold-start effect at the start of a new session for the Tmall dataset. Please note that no data are provided for the cold-start problem by the SWIWO method.
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
## 2 Comparison Analysis  
In order to further analyze the simulation results, the effects of fluid-structure interaction in the grouting process are investigated through a comparison with the conventional CFD simulation neglecting these effects.  
It can be seen from Figure 10 that the variation trend of the grout diffusion length in each fracture obtained by conventional CFD simulation is same as that obtained by fluid-structure interaction simulation. However, at the same grouting time, the fluid-structure interaction simulation results display a larger grouting diffusion length than the conventional CFD simulation results, that is, the grouting diffusion rate is faster. This can be explained thus: when considering the fluid-structure interaction effect, the fracture deformation occurs due to the grout pressure induces stresses on the surface of the fracture during the grouting process, which will improve the grout penetrability because the grout will diffuse more easily in a fracture with larger aperture. Therefore, ignoring the fluid-structure interaction effects in the simulation of grouting will lead to an underestimation of the grout diffusion ability.
# Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service
## 6 New Scheme of DDS Protocol
### New Scheme Model Security Verification  
The security evaluation of the new scheme model is compared with the one prior to the improvement. Table 6 presents the state space data of the three attack models before and after improvement. We can find that the number of nodes increases due to the timestamp and verification key consistency steps introduced in the data transfer process. From the table, we can see that the dead node of REY-ATK has been reduced from 2 to 1. By writing a query function to query 12,196 nodes, it shows that this is the termination state of the protocol, which is in line with the expected result. Among them, DeadMarking() is used to judge whether the mark of the specified node is dead, that is, whether there is an enabled binding element. If it returns true, there are no enabled binding elements, which is the protocol termination state. NodeDescriptor() is used to print the actual state of the node, it can be seen that the Num in S8 is increased by 1, indicating that the protocol operation is completed, as shown in Figure 22.  
Compared to the replay attack against the previous protocol, it can be seen that the probability of replay attack is reduced by about 8%. At the same time, the introduction of the new security mechanism increases the protocol processing process. By comparing the number of steps, we see 5% of increase in processing step. The addition of timestamps ensures the freshness of messages and resists replay attack. New security measures appear to act as an effective deterrent against the aforementioned man-in-the-middle replay attack and enhanced the overall security of the protocol. By comparing the dead nodes before and after the improvement of the protocol, it is calculated that the overall security performance of the improved protocol has increased by about 16.7%.  
Figure 21: Attacker model for the new scheme.
## 4 Results and Discussion
### Comparing Our Findings with Previous Studies  
The authors in [38] used a state-machine architecture to find Krack attacks by monitoring numerous wireless channels. To specifically identify the Krack symptoms at various points of a handshake session, they undertook deep packet inspection and created a grouping method to group Wi-Fi handshake packets. They used supervised machine learning models based on gradient boosting, and their accuracy was around 93.39% with a false positive rate of 5.08%.  
In [52], the authors proposed a framework for unsupervised classification and data mining of tweets about cyber vulnerabilities; this vulnerability included the Kr00K attack, which allows unauthorized decryption in Wi-Fi chips. The best accuracy that they achieved was 88.52%  
[PERSON] et al. applied deep learning and machine learning techniques on the AWID3 benchmark dataset [53], in order to answer questions about the competence of 802.11-specific and non-802.11 features when used separately and in tandem in detecting application layer attacks and to know which network protocol features are the most informative to the machine learning model for detecting application layer attacks; the performance of the detection model achieved 96.7% accuracy.  
Due to the increased urgency for unrestricted network data access to improve cyber-AI efficiency in unfamiliar threat scenarios, the authors in [54] proposed an automated network scanning and data-mining technique through open-source service discovery tools for deep reinforcement learning-based cognitive network intrusion detection systems. They obtained the lowest false alarm rate and a 98.68% accuracy.  
In [55], the authors investigated how to map machine learning algorithms to programmable network devices. Furthermore, state-of-the-art and newly proposed in-network ML algorithms are evaluated and compared in terms of functionality, resources, scalability, and throughput. They used six datasets, including KDD99 and AWID3, for intrusion detection purposes. Their accuracy ranged from 97.47% for decision trees to 49.37% for KNN.  
Table 9 summarizes the above-mentioned studies.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 4 Matrices in Image Processing
### Wavelet Representation  
Besides the classical DCT image representation, another modern image representation method, that relies on the 2-D wavelet coding and lies at the basis of JPEG 2000 standard [37], is used extensively. Different from the transform coding, the wavelet coding processes the whole image and results in a modified image consisting of four subimages. For example, after processing Figure 7a using the Haar wavelet, the modified image in Figure 8a is obtained. The four submatrices represent the approximation, the horizontal details, the vertical details and the diagonal details, respectively. One can observe that the three detail matrices can be coarsely quantized since their coefficients roughly present similar values. Moreover, the approximation matrix in Figure 8a can also be decomposed into four sub-subimages as shown in the upper left corner of Figure 8b. In this way, the image is compressed to a higher degree given the premise that the original image can be recovered with a tolerated quality.
# Analysis of Electromagnetic Field Characteristics of Wave Glider
## 2 Analysis of Motion Characteristics of Wave Glider  
A wave glider is a new type of unmanned mobile platform at sea that uses wave energy as the drive force and solar energy as the power source to overcome the bottleneck of energy supply for small underwater sports platforms. Wave gliders have the advantages of long sailing time, long sailing distance, low maintenance cost, suitability for high sea conditions, simple deployment and recovery, no dependence on chemical fuels, and small radar reflection area [22; 23]. Wave gliders have significant research and practical value in both military and civilian applications [24]. For example, as a new type of mobile observation platform in civilian applications, they can be equipped with multiple sensors for research on marine meteorology, resource exploration, marine environment, and marine life. In the field of military defense, they can be used for communication relay, target reconnaissance, etc. [25].
# The Impact of Power Definitions on the Disaggregation of Home Loads for Smart Meter Measurements
###### Abstract  
The use of load-monitoring systems in residential homes is fundamental in the context of smart homes and smart grids. Specifically, these systems will allow, for example, the provision of efficient energy management and/or load forecasting for residential homes. To achieve this goal, these systems can be based on the concept of a smart meter. However, a smart meter provides aggregate power consumption, which makes it extremely complex to identify individual home appliances, even using advanced algorithms. In line with this, this paper proposes to analyze the impact of power definitions on the disaggregation of home appliance loads. Moreover, it will also consider the distortion of the voltage grid, which is usually not addressed in the resolution of this problem. This effect will be verified through an approach that is based on a genetic algorithm. The approach will be tested through the use of several scenarios, in which an aggregation of home appliances is used.  
smart meter; appliance identification; load disaggregation; power definitions; grid distortion +
Footnote †: journal: Applied Sciences
## 2 Fundamentals of the New Algorithms
### Acquisition Process
Considering the parameter carrier phase, its influence can be removed by involving two components in the acquisition process that are in-phase component (I) and \(90^{\circ}\) phase-shifted quad-phase (Q) component [19]. Therefore, the test signal \(\overline{r}(n)\) can be written as  
\[\overline{r}(n)=C(nT_{s}-\overline{\tau})e^{2\pi(f_{IF}+\overline{f}_{d})nT_{s}} \tag{8}\]  
where the parameter vector becomes \(\overline{\mathbf{a}}=\left[\overline{\tau},\overline{f}_{d}\right]\), and the energy of \(\overline{r}(n)\) is not related to \(\overline{\mathbf{a}}\). So the accumulation process in acquisition can be expressed mathematically as  
\[y(n)=r(n)+W(n) \tag{2}\]0  
Equation (9) is known as cross-ambiguity function (CAF). Based on Equations (4) and (5), the ML estimate of \([\tau,f_{d}]\) can be obtained [17], as  
\[y(n)=r(n)+W(n) \tag{2}\]1  
where \(|\cdot|\) means the modulus of a complex value, and the range of \(\overline{\mathbf{a}}\), \(ran_{\mathbf{a}}\) will be discussed in Section 3. There are mainly three acquisition schemes [19]: serial search acquisition, parallel frequency space search acquisition and parallel code phase search acquisition. No matter what kind of scheme is used, a two-dimensional search grid (Figure 4) is always obtained, and the resulting estimated vector is selected as the location of the peak cell, and, at the same time, the other cells in the search grid are abandoned. However, because of the large frequency searching step \(f_{sp}\), the frequency estimate error is located in the range \([-\frac{f_{sp}}{2},\frac{f_{sp}}{2}]\), so the initial Doppler frequency estimate is usually not accurate enough to pass to the tracking loop directly. In order to refine the Doppler frequency estimate, a system typically used is the Frequency Lock Loop (FLL) mentioned in Reference [1,2]. An FLL needs additional data and a special structure, which is generally embedded inside the tracking loops. Another typical technique [3,4], which exploits the phase relation of consecutive data (p.150 in Reference [3]), also needs additional data, and, at the same time, encompasses an ambiguity problem in the phase measurement that has to be solved. Actually this technique is essentially similar to an FLL with a particular discriminator. In this paper, we develop new methods to refine the Doppler frequency estimate, based only on the search grid already evaluated by the acquisition; that is, we do not have to compute new correlations, but we only use the neighbor cells of the CAF peak, already available in the search grid.
# Effect of PAS-LuxR Family Regulators on the Secondary Metabolism of _Streptomyces_
## 3 Regulation of Related Product Biosynthesis by PAS-LuxR Family Regulators  
PAS-LuxR family regulators play a regulatory role together with the regulators of other families. According to the product structure classification, they can be mainly divided into polyene and nonpolyene macrolides, which are most studied in detail in pimaricin, nystatin, filipin, tetramycin, candicidin (also known as FR008), amphotericin, NppA1, and CFA-L-Ile. However, the regulatory mechanisms of reedsmycin and wuyiencin need to be further determined.
## 5 Results and Discussion
### Effects of the Parameters on \(\overline{Nu}\)
#### 5.2.2 The Effect of \(Re\) on \(\overline{Nu}\)  
Figure 10 shows the response surface plots of different Reynolds numbers to the average Nusselt number within the range of operating parameters (\(\phi=0.9\)-\(1.2\), \(H/d=1.5\)-\(3.0\)). \(\overline{Nu}\) rises with the increase in \(Re\), meaning that the global heat transfer imposed on the impingement plate is enhanced because the PMIF) with high \(Re\) has more convective heat transfer [19; 58]. The large concentration of reactive species at the flame's inner reaction  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & \(\mathbf{\phi}\) & \(Re\) & \(H\)/\(d\) & \(SV\) & \(PV\) & \(e_{r}\)(\%) \\ \hline
21 & 1.2 & 700 & 1.5 & 2.369 & 3.369 & 0.02 \\
22 & 1.2 & 900 & 1.5 & 2.964 & 2.974 & 0.33 \\
23 & 1.2 & 1100 & 1.5 & 3.540 & 3.541 & 0.02 \\
24 & 1.15 & 1200 & 1.5 & 3.592 & 3.621 & 0.81 \\
25 & 1.05 & 1200 & 1.5 & 3.320 & 3.309 & 0.33 \\
26 & 0.95 & 1200 & 1.5 & 3.133 & 3.129 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The CFD’s simulated values, the KM’s predicted values, and their relative errors for six checking points.  
Figure 9: Response surface plots of the average Nusselt number (\(\overline{Nu}\)) with respect to \(Re\) and \(H/d\) at different \(\phi=\) (**a**) 0.9, (**b**) 1.0, (**c**) 1.1, and (**d**) 1.2.  
region enhances convection heat transfer by their diffusion and exothermic recombination on the impingement surface [22]. The influence of the equivalence ratio on heat transfer performance becomes more significant as the Reynolds number increases.