esa-sceva/satcom-chunk-collection huggingface dataset:
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 4 Evaluation
### SARDIMM Performance  
We evaluated the RDA acceleration performance of the SARDIMM. As depicted in Figure 10, the normalized performance of both the baseline system and SARDIMM is presented. For the RDA execution, the SARDIMM conducts three primary operations: range compression, azimuth FFT, and azimuth reference signal multiplication followed by azimuth IFFT. The SARDIMM achieved speedups of 6.89\(\times\), 7.02\(\times\), 7.09\(\times\), and 7.17\(\times\) for images of sizes 8192 \(\times\) 8192, 16,384 \(\times\) 16,384, 32,768 \(\times\) 32,768, and 65,536 \(\times\) 65,536, respectively, in range compression. Similarly, in azimuth FFT, speedups of 6.52\(\times\), 6.68\(\times\), 6.8\(\times\), and 6.97\(\times\) were attained, while in azimuth IFFT after multiplication, speedups of 6.97\(\times\), 7.11\(\times\), 7.2\(\times\), and 7.23\(\times\) were achieved. Between the range compression and azimuth FFT, the host transposes the compression result to the azimuth direction. Additionally, after the azimuth FFT, the host executes the RCMC operation based on the FFT results. Consequently, in the end-to-end RDA encompassing all host actions, speedups of 6.33\(\times\), 6.62\(\times\), 6.8\(\times\), and 6.94\(\times\) were achieved.  
Figure 11 shows the normalized total energy consumption by the DRAM in both the baseline system and SARDIMM. The SARDIMM, with reduced memory accesses, demonstrates energy savings in terms of overall DRAM energy consumption. Specifically, energy  
Figure 10: Normalized performance of the range compression, azimuth FFT, azimuth reference signal multiplication followed by azimuth IFFT, and end-to-end RDA on the baseline system and SARDIMM; MUL denotes reference signal multiplication.  
Figure 9: Execution time ratios of the RDA on the baseline system; MUL denotes reference signal multiplication.  
savings of 43.1, 47.88, 48.5, and 48.57% were achieved for range compression and 39.39, 45.09, 45.87, and 47% for azimuth FFT. Similarly, the azimuth IFFT with multiplication exhibited energy savings of 44.16, 48.55, 49.2, and 49.64%. Notably, the end-to-end RDA achieved energy savings of 41.9, 46.97, 47.74, and 48.21%. In this study, performance evaluation was conducted on a single-channel, dual-rank SARDIMM, and achieved an average speedup of 6.67\(\times\) and overall DRAM energy savings of 46.21% for RDA. Considering that parallelism scales with the number of SARDIMMs, we anticipate further performance enhancements by utilizing multiple SARDIMMs.
# UAV Formation Trajectory Planning Algorithms: A Review
## 6 Problems of UAV Formation Trajectory Planning Algorithm
### UAV Formation Communication Problem  
With the development of science and technology, the application scenarios of drones in the future will become more and more complex. In certain scenarios, there will be communication interference problems, which will cause the UAV to fail to work normally and even cause irreversible damage.
## 3 Results and Discussion
### Multi-Objective Optimization
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Optimization** & & **Objective Function** \\  & **Goal** & **Process Parameters** & **Value** \\ \hline \(\sigma_{x}\) & \(\min\downarrow\) & \(f=500\) mm/min, \(P=53.76\) N & \(f_{1}(x)=-124.87\) MPa \\ \(\sigma_{y}\) & \(\min\downarrow\) & \(f=500\) mm/min, \(P=60\) N & \(f_{1}(x)=-243.14\) MPa \\ \(\mathfrak{t}\) & \(\min\downarrow\) & \(f=1500\) mm/min & \(f_{3}(x)=0.66\) min \\ Sa & \(\min\downarrow\) & \(f=1500\) mm/min, \(P=54.31\) N & \(f_{4}(x)=0.17\)\(\mu\)m \\ Sku & \(\min\downarrow\) & \(f=500\) mm/min, \(P=60\) N & \(f_{5}(x)=2.75\) \\ Spc & \(\min\downarrow\) & \(f=500\) mm/min, \(P=57.69\) N & \(f_{6}(x)=23.24\)\(1\)/mm \\ \(H_{V}\) & \(\max\uparrow\) & \(f=500\) mm/min, \(P=49.57\) N & \(f_{6}(x)=416\)\(H_{V}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of one-criteria problem \(f_{i}(x)\). a dimensionless form taking values from the range [0, 1] for \(x\in E\). After the unitarisation, an optimal solution to the problem is determined according to  
\[\text{min}(\sum_{i=1}^{k}u_{i}\frac{f_{k}(x)-\text{min}(f_{k}(x):x\in E)}{max(f_ {k}(x):x\in E)-\text{min}(f_{k}(x):x\in E)}:x\in E) \tag{17}\]  
The optimal solution of function (17) is an effective solution to the multi-criteria problem. The form of the solution depends on the adopted weight value \(u_{i}\). If it is assumed that all parameters have the same meaning (\(u_{i}=0.142\)), the objective function achieves the minimum value for the feed rate \(f=1500\) mm/min and the burnishing force \(P=50.14\) N (Figure 13a). This makes it possible to obtain surface roughness parameters: \(Sa=0.28\) um, \(Sku=3.93\) um, and \(Spc=35.88\) 1/mm with the stress value in the surface layer \(\sigma_{x}=-123\) MPa and \(\sigma_{y}=-202\) MPa and microhardness \(H_{v}=362.56\) N/mm\({}^{2}\). The parameters determined in this way also guarantee thehighest efficiency of the burnishing process (burnishing time \(t=0.66\) min). If it is assumed for the calculations that the value of compressive stresses in the surface layer has the greatest impact on the fatigue strength of the joints (\(u_{1,2}=0.25,\ u_{3-7}\ =0.1\)), a compromise solution can be obtained at the feed rate \(f=500\) mm/min and the burnishing force \(P=57.70\) N (Figure 13b). Burnishing of joints with these parameters allows us to obtain the level of compressive stresses in the surface layer \(\sigma_{x}=-126\) MPa and \(\sigma_{x}=-240\) MPa. A higher value of compressive stresses is associated with the deterioration of the roughness parameters \(Sa=0.506\) um and \(Sku=5.10\) with more rounded surface tops (\(Spc=23.23\) 1/mm), slightly higher microhardness of the surface layer of the joint (\(H_{v}=372.18\) N/mm\({}^{2}\)) and significantly increased the time consuming of the burnishing process (\(t=2\) min).
### Paper Organization and Notation  
This work is delivered in the following organizational format. The system model description and the characterization of the SINR and the DASR are presented in Section 2. In Section 3, the CSI estimation and the training sequence design in the downlink MMIMO with the FDD mode are discussed, and the related formats are provided. In addition, the problem formulations of the DASR maximization are provided. Furthermore, the power allocation optimization is also developed in this section. In Section 4, an analytical solution of the RZFP is developed using the approach of random matrix theory. Section 5 presents performance evaluation results for the DASR with proposed power allocation optimization in comparison with the conventional method of uniform power allocation. Finally, Section 6 concludes the paper.  
This research employs boldface symbols to represent matrices and lower boldface symbols for vectors. The Gaussian distribution is symbolized as \(\mathcal{CN}(\mathbf{0},\mathbf{H})\), thus indicating a mean of zero and a covariance matrix of \(\mathbf{H}\). The notation \(\mathbb{E}[\cdot]\) is utilized for the expectation operator, and \(|.|^{2}\) denotes the absolute square, which is the power in the signal. Various mathematical operations are used in this paper, such as transpose, trace, and Hermitian transpose, which are given as \((\cdot)^{\mathrm{T}}\), \(\mathrm{tr}(\cdot)\), and \((\cdot)^{\mathrm{H}}\), respectively.
## 5 Review of Case Studies: Flying Vehicle Routing Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Problem Addressed** & **Objective(s)** & **Optimization Approach** & **Key Contributions** \\ \hline [PERSON] et al. [6] & GA-based routing (GAR) protocol for & Improve routing in FANETs & Significant improvements in network \\  & FANETs & considering link stability, link bandwidth, and node energy & GA & throughput, delay reduction, and \\ [PERSON] et al. [54] & Path planning of UAVs in a 3D environment & Real-time path planning, generating multiple trajectories based on a Pareto & GA, A-Star algorithm & Enhanced path planning solutions, improved fitness of A-Star solution, flexibility, and real-time capability \\ \hline [PERSON] et al. [55] & Quality-aware coverage and path planning for UAV networks in complex environments & Achieve full coverage with satisfactory resolution, energy-efficient path planning & Waypoint generation algorithm & Improved coverage, energy consumption, suggested future research directions \\ \hline [PERSON] et al. [56] & Optimization of task allocation and path planning for fixed-wing UAVs in the presence of steady wind & Minimize time required for UAVs to complete tasks in steady wind & GA & Variable-speed Dubings path vehicle routing problem (VS-DP-VRP) model, effective solution for UAV task allocation and path planning in steady wind \\ \hline [PERSON] and [PERSON] [57] & Motion planning for a miniature UAV considering computational constraints, obstacle avoidance, and priority assignment & GA & Modular approach for generating state trajectories, efficient motion planning in 2D and 3D environments \\ \hline [PERSON] et al. [58] & UAV path planning using improved GA and particle-swarm-optimization-based ant colony optimization algorithm & Solve the TSP for UAVs, find optimal path planning solutions & Improved genetic algorithm, particle swarm optimization, ant colony optimization & Effective solution compared to contrast approach \\ \hline [PERSON] et al. [59] & Coverage path-planning algorithm for quadrofos in a 2D workspace with obstacle avoidance & Find optimal paths between points and their neighbors, connect points to form shortest path & RRT+FN algorithm, CAs & Effective solution for coverage scenarios with obstacle avoidance and energy efficiency \\ \hline [PERSON] et al. [60] & Performance comparison of truck-drone network, standalone truck, and drone delivery systems & Minimize delivery time and energy consumption & GA, K-means clustering & In tandem delivery outperforms standalone systems, closed-form mathematical solutions for optimal number of launch locations and delivery time \\ \hline [PERSON] and [PERSON] [22] & Bi-objective model for the IRP & Optimize fuel consumption and pollutant emissions & GA & Novel approach for solving IRP considering fuel consumption and environmental impact \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of flying vehicle routing optimization papers.
## 2 Background
### Performance Metrics  
In general, the performance of a multi-objective optimization method is evaluated by assessing its convergence, uniformity and spread. This method examines the approximation degree (distance) between the solution set and the real Pareto front, the uniformity of the solution set distribution on PF and the wide distribution of the solution set in the target space. Generally, the achieved solution set is expected to be as close to the PF, as evenly distributed on the PF and as complete to express the PF as possible. In our case, two widely used performance metrics, namely IGD and HV, are adopted for the comparison.  
* Inverted Generational Distance (IGD) The IGD metric is one of the most used metrics in the field of MOPs, through which measuring both the convergence and diversity of the algorithms is carried out. It is mathematically formulated as [27]: \[\text{IGD}(A,P^{*})=\frac{\sum_{i=1}^{|P^{*}|}\min(\text{Dist}(p_{i}))}{|P^{*}|},\text{Dist}(p_{i})=\big{\{}d\big{(}p_{i},a_{j}\big{)}\big{|}a_{j}\in A,j=1,2, \cdots,|A|\big{\}}\] (4) where \(A\) is the solution set achieved by the algorithm, \(P^{*}\) denotes the solution set uniformly sampled along the real PF and \(d(p_{i},a_{j})\) indicates the Euclidean distance between the two points. Note that a lower IGD stands for better performance.
* Hyper-Volume Metric (HV) HV is used to calculate the volume of the region in the target space surrounded by the non-dominated solution set and the reference points. Although it can simultaneously evaluate convergence and diversity without providing real PF, it has high computational complexity [28]. Mathematically, HV is formulated as: \[\text{HV}=\delta\Big{(}\cup_{i=1}^{|S|}v_{i}\Big{)}\] (5) where \(\delta\) is the Lebesgue measure, \(s\) denotes a non-dominant solution set and \(v_{i}\) denotes the super volume.
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 4 Methodology
### Metrics for Assessing Models' Accuracy  
To measure the accuracy of the models, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics were used. MAE measures the average absolute differences between predicted values (\(y_{i}\)) and the factual values (\(y_{i}\)) over \(n\) observations and is mathematically expressed as shown in Equation (1):  
\[MAE\ =\ \frac{1}{n}{\sum_{i=1}^{n}}\mid y_{i}-y_{i}^{\prime}\mid \tag{1}\]  
RMSE is defined as the square root of average of squared errors [52]. This metric is widely used because it amplifies the impact of larger errors while deemphasizing smaller ones. The RMSE is mathematically expressed as shown in Equation (2):  
\[RMSE\ =\ \sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{\left(y_{i}-y_{i}^{\prime}\right)}^{2}} \tag{2}\]  
These metrics were chosen as they are widely used in wind power prediction and provide complementary insights into model performance. MAE indicates the average prediction error, while RMSE, due to its squared error component, is more sensitive to the outliers.  
In addition to the conventional metrics, normalized error metrics nMAE and nRMSE were computed as well. The normalized metrics are useful for facilitating scale-independent comparisons across different systems or scenarios and are mathematically expressed as shown in Equations (3) and (4):  
\[nMAE\ =\ \frac{MAE}{N_{max}}\ =\ \frac{1}{N_{max}}\ \cdot\ \frac{1}{n}{\sum_{i=1}^{n}}\mid y_{i}-y_{i}^{\prime}\mid \tag{3}\]  
\[nRMSE=\frac{RMSE}{N_{max}}=\frac{1}{N_{max}}\sqrt{\frac{1}{n}{\sum_{i=1}^{n}} {\left(y_{i}-y_{i}^{\prime}\right)}^{2}} \tag{4}\]  
where \(N_{max}\) represents the maximum generated power, which is equal to 2000 kW in this study.
## 5 Future Research Perspectives
* Investigation of potential environmental, economic, and social strains and benefits by using a dynamic input-output analysis method is essential. This enables evaluating the supply chain effects caused by the current and future changes in demand and technological developments at different levels (building, neighborhood, city, region, country, global). * Activities related to refurbishment and adaptive reuse of existing buildings, with environmental and social benefits proven by LCA studies, should receive financial support, incentives, and subsidies to support and emphasize the role of historic/heritage buildings in climate change mitigation.
# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
### Noteworthy Survey and Key Aspects  
Many related surveys on IoT already exist in the literature that cover different aspects of deep learning in cybersecurity. Our comparison of previous studies is based on several key properties as shown in Table 1. These surveys [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13] provide a modest focus on IoT intrusion detection. Most studies are either descriptive of the IoT architecture, or they present the various IDSs as a general overview for a particular project evaluation and verification purpose. References [2; 3; 6; 11] are completely dedicated to IoT architectures and include an incomplete assessment of some applications and protocols. References [4; 12] propose a six-layer architecture for the IoT domains. However. IoT security and IDSs were not considered in their study. In [13], the architecture, protocols, and privacy are described only as brief IoT security concepts, including the interconnection between the objects of things. In [7], the authors presented a survey of IDS in IoT but nothing about DL/ML techniques in IDS. Several attacks targeting protocol topology (the Routing Protocol for Low-Power and Lossy Networks (RPL), IPv6 over Low-Power Wireless Personal Area Networks (6 LoWPAN)) are discussed in [5] without classifying those attacks on an IoT layered architecture connected with IDS. Reference [8] similarly provides a classical comparative analysis for several existing papers based on advantages and disadvantages. Their focus, furthermore, concentrates on the attacks without due consideration of the ML/DL methods as a general solution.  
A few studies provided an extensive background on all IoT areas through an enhanced IoT security (based on IoT-specific threats) approach [6; 11; 12; 14; 15]. However, they did not examine, as we have, all of the domains of anomaly-based-IDS for IoT. Table 1 describes those studies that show the preeminent role of anomaly-based IDS for the security of things. This table identifies the gaps in the previous survey studies within the standard architecture layers of IoT systems and then links them with IDSs, such as anomaly-based IDS, with the intent to clarify their solution context and mechanism (An anomaly-based intrusion-detection system is an intrusion detection system for detecting both network and computer intrusions and misuse by monitoring system activity and classifying it as either normal or anomalous).
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 4 Theoretical Framework
### SPR in Prism Configuration  
The Kretschmann configuration [44] is commonly used in most SPR applications nowadays, as shown in Figure 4a, and was developed and improved from the Otto configuration [12] shown in Figure 4b. In the Otto configuration, an air gap layer (dielectric constant, \(\varepsilon_{s}\)) lies between the prism (dielectric constant, \(\varepsilon_{p}\)) and the metal layer (dielectric constant, \(\varepsilon_{m}\)). The evanescent wave at the prism-air interface can excite the SPs at the air-metal interface. Total internal reflection takes place inside the prism when the electromagnetic field propagates from the prism (high refractive index, \(n_{1}\)) to the air gap layer (low refractive index, \(n_{2}\)).  
Figure 3: Schematic illustration of surface plasmons propagating at the interface between metal nanoparticle and dielectric materials [40].  
The incident angle, \(\theta_{a}\), should be greater than the critical angle, \(\theta_{c}\), in order for the total internal reflection to take place. Nevertheless, some part of the electromagnetic field is propagated at the prism-air interface as an evanescent wave. However, as it decays over a distance of the interface of the entail air gap, the SPR efficiency is reduced.  
Hence, in the Kretschmann configuration, the prism was coated by the metal directly without an air gap and the other side of the metal layer was designed for placing a dielectric sample. In order to enhance and maintain the propagation of this evanescent wave, a gold or silver film with a suitable thickness is placed between the two media and dielectric sample (air). The most used metal is either gold or silver, which can provide a real but negative dielectric constant, whereas for the dielectric sample (air) it is positive.  
The wave vector for the evanescent wave, \(k_{ew}\), is given as  
\[k_{ew}=\frac{2\pi}{\lambda}n_{1}\sin(\theta), \tag{1}\]  
where \(\lambda\) is the incident wavelength, \(n_{1}\) is the higher refractive index of the prism, and \(\theta\) is the incident angle.  
The wave vector of the SPs is explained with the dielectric constant of the metal (refractive index, \(n_{g}\)) and dielectric sample (refractive index, \(n_{2}\)) and is given as  
\[k_{SP}=\ \frac{2\pi}{\lambda}\sqrt{\frac{n_{2}^{2}n_{m}^{2}}{n_{2}^{2}+n_{m}^{2}}}. \tag{2}\]  
In Equation (2), \(n^{2}=\varepsilon\). Here, \(\varepsilon\) is the dielectric constant and \(n\) is the refractive index. When the propagation constant of the evanescent wave is equal to that of SPs (\(k_{ew}=k_{SP}\)), SPR can be achieved by the transfer of light wave energy into the SPs. The evanescent wave in the metal film is expressed as  
\[k_{SPR}=\ \frac{1}{n_{1}sin\theta}\sqrt{\frac{n_{2}^{2}n_{m}^{2}}{n_{2}^{2}+n_{m} ^{2}}}\, \tag{3}\]  
where \(\theta\) is the resonance angle.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 6 Security Evaluation
### Formal Verification  
Scyther Tool is a formal verification tool commonly used for validating security protocols [26]. Scyther offers four security statements to ensure protocol consistency and detect various attacks like message forgery, replay, and man-in-the-middle (MITM). These include \"Aliveness\" for completing protocol steps with active responders, \"Niagree\" for the correct variable receipt without one-to-one communication, \"Nisynch\" for the expected protocol operation without one-to-one synchronization, and \"Weakagree\" for one-to-one communication within the same group of initiators or responders.  
The verification results are shown in Figure 6, where Figure 6a demonstrates the first MTCD handover authentication phase, and Figure 6b demonstrates the group handoverauthentication phase. Specifically, the model is created with three roles: t-gNB, \(MTCD_{1}\), and \(MTCD_{1}\). Initially, the BSPGH scheme achieves mutual key agreement using the Elliptic Curve Diffie-Hellman (ECDH). To simulate the ECDH key exchange between two parties, two functions are defined as \(g1\) and \(g2\). Then, \(n_{1}\cdot P\) is set to \(g1(n1)\), \(n_{1}\cdot P\) is set to \(g1(ni)\), and \(n_{\text{t}}\cdot P\) is set to \(g1(nt)\). The confidentiality of the ECDH private keys is first verified using the declarations of Secret \(n1\), Secret \(ni\), and Secret \(nt\). Next, the derivation of ECDH public keys is performed using \(K1t\) (i.e., \(g2(nt,\ g1(n1))\)) and \(Kit\) (i.e., \(g2(nt,\ g1(ni))\)). The confidentiality of the ECDH private keys is ensured by declaring Secret \(ni\), Secret \(nt\), and Secret \(n1\). Additionally, the confidentiality of the ECDH public key is validated by the SKR declaration. Our protocol assumes that \(MTCD_{1}\) needs to use the group key \(GID\) to verify the identity of \(MTCD_{1}\). To ensure that adversaries cannot obtain the group key \(GID\) in any way, its secrecy is checked using the Secret \(GID\) declaration. Finally, the verification results reveal that all participants in the system satisfy the properties of synchronous (Nisynch), consistent (Niagree), active (Alive), and weak consistency (Weakagree). All the keys meet the security requirements. Therefore, our protocol is deemed secure by the verification using Scyther.