esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 2 Proposed Model
### Cryptographic Methods  
Recently, many cryptography approaches have been proposed for securing MANETs and IoT networks [28]. The objective is to provide secure data communication. In this paper, a source node finds the CK and DK nodes in the routing table to evaluate the secure routes. Validation, encryption, and trust are all achieved through cryptographic calculations [29; 30; 31]. The majority of cryptographic frameworks necessitate a key management mechanism that is safe, dynamic, and efficient. Any protected communication system relies on the management of keys. The proposed approach uses strong encryption algorithms to provide secure data delivery. The main process of the selection of CK and DK nodes is presented in Section 3.1.
## 2 Model and Method
### Dynamic Model of a Complex Network
* Delay matrix: \(D(A,B)=\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\{\delta_{ij}x_{ij}\}_{t_{ij}}=\) \(\{A,B\}\Rightarrow\delta_{AB}x_{AB}=10t\) seconds \(\{A,C,B\}\Rightarrow\delta_{AC}x_{AC}+\delta_{CB}x_{CB}=3t+4t=7t\) seconds \(\{A,D,B\}\Rightarrow\delta_{AD}x_{AD}+\delta_{DB}x_{DB}=2t+5t=7t\) seconds \(\{A,E,F,B\}\Rightarrow\delta_{AE}x_{AE}+\delta_{EF}x_{EF}+\delta_{FB}x_{FB}=4t+2t +t=7t\) seconds
* Load balancing (\(LB\)): \[L(s,d)=\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\bigl{\{}\lambda x_{ij}\bigr{\}}_{t_{ij }}+\lambda \tag{2}\]5 \[L(s,d)=\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\bigl{\{}\lambda x_{ij}\bigr{\}}_{t_{ij }}+\lambda \tag{2}\]6 \[L(s,d)=\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\bigl{\{}\lambda x_{ij}\bigr{\}}_{t_{ij }}+\lambda \tag{2}\]7 \[L(s,d)=\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\bigl{\{}\lambda x_{ij}\bigr{\}}_{t_{ij }}+\lambda \tag{2}\]8 \[L(s,d)=\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\bigl{\{}\lambda x_{ij}\bigr{\}}_{t_{ij }}+\lambda \tag{2}\]9
* Load matrix: \(L(A,B)=\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\left(\{\lambda x_{ij}\}_{t_{ij}} \right)+\lambda=\) Figure 1:0  
Figure 1:1  
Figure 1:2  
Figure 1:3  
As shown in Table 2 considering the influence of delivery delay, communication overhead, and load balancing on routing performances, we observe that there are three paths with the shortest delivery delay (\(7t\)) from device A to device B: \(\{A,C,B\}\), \(\{A,D,B\}\) and \(\{A,E,F,B\}\). However, the length of paths \(\{A,C,B\}\) and \(\{A,D,B\}\) is shorter than that of path \(\{A,E,F,B\}\). Moreover, the longest forwarding delay of the path \(\{A,C,B\}\) is three time periods, from \(t=4\) to \(t=6\) (load balancing = \(0.5t\)), which is shorter than that of path \(\{A,D,B\}\), with four time periods, from \(t=3\) to \(t=6\) (load balancing = \(1.5t\)), so \(\{A,C,B\}\) have better  
Figure 1:4 Illustration of the paths from device A to device B based on the time order in Figure 1:5  
load balancing. Finally, we observe that path \(\{A,B\}\) has the shortest hop number but the longest delivery delay (\(10t\)). In summary, path \(\{A,C,B\}\) is the best path from device A to device B considering the trade-off between delivery delay, communication overhead, and load balancing. Using flooding or epidemic routing, device \(B\) should receive 4 copies of the message. One from device \(A\) at time \(t=10\) and three copies from \(C\), \(D\) and \(F\) at \(t=7\), assuming a total load of \(CO-p_{AB}\lambda+\lambda=\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\biggl{(}\left\{ \lambda x_{ij}\right\}_{t_{ij}}+\lambda\biggr{)}-p_{AB}\lambda+\lambda=12 \lambda-4\lambda+\lambda=9\lambda\) bytes. However, by selecting the best route, overhead improvement can be obtained as \(\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|}\biggl{(}\left\{\lambda x_{ij}\right\}_{t_{ ij}}+\lambda\biggr{)}-p_{AB}\lambda+\lambda-min\biggl{(}\sum_{i=1}^{|V|-1}\sum_{j=2}^{|V|} \left\{\lambda x_{ij}\right\}_{t_{ij}}+\lambda\biggr{)}=6\lambda\) bytes, which is an improvement of 33%.
## 5 Discussion
### Limitations in Satisfying Security Requirements for Offline Payment Function
#### 5.2.2 Solutions to Satisfy Security Requirements of DDoS Attack Prevention  
* Method of charging a fee when using the withdrawal or deposit function: The charging system used in the withdraw and deposit protocol can be applied [85]. In the current digital payment system, the profit considered when charging a fee that an attacker can be charged is designed to be less than the cost of a DDoS attack. In the model proposed by [PERSON] et al. [95], when using the withdraw and deposit protocol, the motive for attempting a DDoS attack can be eliminated by charging a fee.
* Method of limiting the number of transactions when using withdraw or deposit function: There is also a way to limit the number of transactions, as shown in Figure 6. The source is blocked so that the withdraw/deposit protocol to generate traffic to the server cannot be intentionally used excessively. DDoS attacks can be prevented through this method.
## 2 Guidance Law Formulation
### Carrot Chasing Guidance Algorithm  
In this algorithm, it is assumed there is a virtual point sliding along the path and the UAV is allowed to chase it. Gradually, the UAV is directed toward the path and asymptotically ride it. The VTP is called the carrot and the UAV chases the carrot. The designed algorithm has three steps: first, calculating the cross-track error \(d\), then the location of the VTP is updated and, finally, producing the guidance command. Assume the UAV is at location \(p\) with the current heading angle \(\psi\)  
Figure 2: Straight line path follower.  
The perpendicular distance from the actual position of the UAV to the path is the cross-track error \(d\). To compensate for the LOS angle and cross-track error distance, the subsequent stepwise Pseudo code is followed [15]:  
1. Initialize the waypoints \(\to W_{i}=(x_{i},y_{i},h_{i})\), \(W_{i+1}=(x_{i+1},y_{i+1},h_{i+1})\), UAV current position \(p=(x_{p},y_{p},h_{p})\), and the path parameter \(\delta\).
2. Calculate the LOS angle \(\rightarrow\theta=atan2((y_{i+1}-y_{i}),(x_{i+1}-x_{i}))\).
3. Calculate the angle between the current position \(p\) and the previous waypoint \(Wp_{i}\rightarrow\theta_{u}=atan2((y_{p}-y_{i}),(x_{p}-x_{i}))\).
4. Calculate the distance between the current position \(p\) and the next waypoint \(Wp_{i}\to R_{u}=\sqrt{(x_{p}-x_{i})^{2}+(y_{p}-y_{i})^{2}}\).
5. Calculate the distance between the UAV current position \(p\) and the VTP \(\to S_{1}=\sqrt{\delta^{2}+(R_{u}\sin(\theta-\theta_{u}))^{2}}\).
6. Calculate the cross-track error angle \(\rightarrow\zeta=asin(R_{u}\sin(\theta-\theta_{u})/S_{1})\).
7. Calculate the desired heading angle \(\rightarrow\psi_{d}=\zeta+\theta\).
8. Calculate the current miss distance \(S\) according to desired waypoint radius \(r\to S=\sqrt{(y_{i+1}-y_{i})^{2}+(x_{i+1}-x_{i})^{2}}\leq r\).
9. Calculate the desired guidance law \(\rightarrow\phi_{c}=k\left(\psi_{d}-\psi\right)V_{a}\), which represents the desired bank angle for UAV with airspeed \(V_{a}\).  
The UAV location is updated based on the desired waypoint radius \(r\) and the current UAV miss distance \(S\) while the performance of following the path is directly depending on the assumed path parameter \(\delta\). For low \(\delta\), the UAV is directed toward the desired path, therefore an overshoot is carried out. Consequently, the UAV oscillates around the desired path, hence the settling time will be increased. By increasing the path parameter \(\delta\), the UAV will settle on the path quickly.
# Characterization of Six Cold-Pressed Berry Seed Oils and Their Seed Meals
## 1 Introduction  
Berries contain several essential micronutrients, dietary fibers, and polyphenic components, such as ellagitannins, resveratrol, and anthocyanins, the latter giving them their distinctive red color. Berries are grown primarily for consumption as such or for processing into juices [1], cordials, and jams. The byproducts, seeds, and pomace can also be used in food [2] or in cosmetics [3]. The processing steps for the berries are as follows: pressing to obtain the juice, drying the pomace, and sieving out the seeds. There are different options to obtain the seed oil: cold pressing and solvent extraction with hexane or with supercritical carbon dioxide. The seed meal remaining after oil extraction could be used in cosmetic applications, e.g., scrubs, although it is generally seen as a low-value waste product.  
Berry seed oils are utilized in food and cosmetic applications because they contain polyunsaturated fatty acids (PUFAs) [4] and other bioactive components, such as antioxidants and vitamins that potentially are beneficial for human health. Antioxidants slow down or to stop damage to cellular DNA, proteins, and lipids caused by reactive oxygen species [5]. As for all food or cosmetic ingredients, the safety of the oils' use must be guaranteed. It is known that allergic reactions to berry seed oils do not often occur [6]. Toxic compounds are rarely found in berry seed oils, except for those originating frombiocides used to protect the plants during its growing process. Oxidative stability is a very important factor concerning the potential use of berry seed oils in food and cosmetic applications. The oxidative stability of an oil depends on the fatty acid composition and the concentration of minor compounds such as tocopherols and polyphenols. For example, strawberry, blackberry, and blackcurrant seed oils are prone to oxidation due to their high content of linoleic acid, an unsaturated fatty acid with two double bonds. Because of their anti-inflammatory activity, berry seed oils are used in cosmetics and pharmaceutical products. The use of berry seed oils for the prevention of skin lesions such as rash, gingivitis, and eczema has been patented [7]. Dairy products and fruit juices are marketed for their high concentration in omega-3 fatty acids, originating from berry seed oils [8]. Moreover, claims on the presence of bioactive substances and effects on human health are used for marketing dietary supplements containing oil from black currant seeds and sea buckthorn seeds. Berry seed oils are considered specialty oils due to their nutraceutical effects. Thus, to provide guidelines for innovative applications, the chemical and physical properties of different berry seed oils were investigated.
## 3 Resources and Demand Assessment of the Case Study
### Electrical Assignment Criteria
#### 3.6.3 Capacity Shortage Fraction  
Capacity shortage fraction (\(f_{cs}\)) is the ratio of total capacity shortage and total electrical demand as described in Equation (10). HOMER Pro considers a system viable only if the capacity shortage fraction is less than or equal to the maximum annual capacity shortage [35].  
\[f_{cs}=\left(\frac{E_{cs}}{E_{demand}}\right)\times 100\% \tag{10}\]  
where \(E_{cs}\) is total excess electricity (kWh/yr), \(E_{demand}\) is total electrical production (kWh/yr).
## 7 Limitations and Future Works  
The current work uses the RCM411 dataset; however, its size and diversity may be limited. A larger and more diversified dataset from various regions and climates would improve the RMSDC model's generalizability. Predictions of climate change are inherently uncertain. The accuracy of these projections and their capacity to accurately represent future climatic patterns may impact the RMSDC system's performance. The performance of the RMSDC model may be affected by the selection of hyperparameters. A more systematic examination of hyperparameter settings and optimization algorithms could be performed to discover the best configuration. The ConvLSTM architecture can be computationally demanding, mainly when dealing with massive datasets and sophisticated models. To reduce calculation time, efficient model architectures or hardware acceleration methods could be investigated.  
Future research could concentrate on gathering more diverse and larger amounts of information from various regions and climates. Incorporating data from many sources and types of sensors would enrich the dataset even more. Future research can study the incorporation of additional external elements--such as traffic volume, road type, and building materials, which may affect road conditions--to improve the robustness of the RMSDC model. Ensemble learning techniques could be investigated to merge different RMSDC models or integrate other machine learning algorithms, thereby enhancing prediction accuracy and lowering uncertainty. Creating a real-time version of the RMSDC system would allow for continuous monitoring and adaptive road maintenance tactics, allowing quick reactions to changing weather conditions. Field trials of the RMSDC system on actual road networks can be conducted to examine its practical performance and effectiveness in real-world circumstances.  
Conceptualization H.E. and E.E.-S.; methodology, E.E.-S.; software, E.E.-S.; validation, A.A.; formal analysis, H.E. and E.E.-S.; writing--original draft preparation, E.E.-S. and H.E.; writing--review and editing, M.A. and M.I.I.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Researchers Supporting Project number (RSPD2023R636), King Saud University, Riyadh, Saudi Arabia.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this paper are available on request.  
**Acknowledgments:** This work was supported by Researchers Supporting Project number (RSPD2023R636), King Saud University, Riyadh, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Setup
#### 4.1.2 Cse-Cic-IDS2017 Dataset  
CSE-CIC-IDS2017 [28] contains realistic network traffic and diverse attack scenarios, including Brute Force, Botnet, PortScan, DoS, Web Attack, and infiltration. Among them, Bot and infiltration classes are highly imbalanced, and thus serve as the primary targets for augmentation.  
**Dataset Splitting:** We split the dataset into 70% for training and 30% for testing. This fixed partition is used consistently across all experiments to ensure fair and reproducible evaluation.
## 2 The Measurement Partitioning Problem
### The Target Measurement Partitioning Algorithm Based on Target Predictions
It is very likely to cause an underestimation of target number. From the above comparison of the existing measurement partitioning algorithm, it can be concluded that fast tracking multiple targets without a loss of tracking performance is still a challenge for tracking multiple extended targets (especially for closed spaced extended targets). In order to partition the measurement set accurately and quickly when extended targets of interest are spatially close, a more general prediction-driven measurement sub-partitioning algorithm (i.e., PMS) is proposed in this paper. The detailed procedures of the proposed PMS are in the next section.
## 4 Creep Model of Surrounding Rock and Parameter Determination
### Inversion of Creep Parameters of Surrounding Rocks
#### 4.2.2 Orthogonal Test Design  
The parameters in the Burgers-Mohr model are classified into two parts, namely the Burgers parameters and the Mohr-Coulomb parameters. Here, the cohesion c and frictional angle \(\varphi\) of the rock mass are determined using lab tests, and the creep parameters \(E_{\text{M}}\), \(\eta_{\text{M}}\), \(E_{\text{K}}\), and \(\eta_{\text{K}}\) are to be inversed. The ranges of these parameters are listed in Table 5. A total of 100 orthogonal tests with four factors and ten levels are designed. Table 6 shows the creep parameters in all tests.  
The excavation process of the tunnel is modeled using FLAC(30) (6.0). The creep characteristics of the tunnel during excavation are not considered because of the tunnel's rapid excavation. After the completion of the tunnel excavation and support installation, the constitutive model for the surrounding rock is changed to the Burgers-Mohr model using the creep parameters of the rock mass listed in Table 4. The calculation time for creep is set as 31 days. Figure 9 shows the numerical model. Figure 10 shows the deformation data monitored at the tunnel arch and sidewall.  
Figure 8: Procedure of parameter inversion using the GA-BP algorithm [31].
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 3 AI-Based Optical Performance Monitoring  
OPM is key to ensure the reliability of optical networks [16]. According to [2], monitoring techniques can enable several essential and advanced network functionalities. Firstly, a precise monitoring of QoT and impairments [55; 56] can make the control plane accurately assess the signal quality. Therefore, the monitoring information can guide the network self-reconfiguration and also enables receivers to adapt some impairment compensation algorithms. Secondly, the real-time monitoring can continuously obtain the condition of the physical layer. If the QoT deteriorates, monitoring agents can detect failures. Then, the controller can reconfigure the network to avoid further degradations. Finally, monitoring data from real scenes can be used to retrain the planning model. This retraining scheme can improve the accuracy of planning tools and make the design margin lower. At the same time, there are also some challenging requirements for deploying an OPM in an EON, such as how to track the real-time change of the optical networks accurately in a short response time and how to monitor multiple impairments simultaneously. These challenges have been elaborated in Section 1. ML shows its potential to fulfill these challenges. In this section, we review various works using ML for OPM. According to their different functions, these approaches are divided into two categories. We firstly introduce some use cases of monitoring the QoT and impairments of a lightpath. Then, we review the monitoring techniques for detecting, identifying and localizing soft failures in a network. These two aspects are discussed as follows.
## 3 Phase Modulation Effects of the Parameters and Parameter Selection
### Phase Modulation Effects of the Parameters
#### 3.2.3 Phase Modulation by Varying \(f_{k}\), \(S_{k}\) and \(\delta_{k}\)  
The fringe density \(f_{k}=g(X_{k})\), and the step size \(S_{k}\) varies according to the fringe density. Here, we set \(S_{k}\) equal to an integer \(N_{steps}\) multiplied by a variable period value. \(N_{steps}\) is chosen to be the same constant expressed with Equation (16). The variable period value can be chosen as the fringe period in the adjacent last scan. \(S_{k}\) can be expressed as  
\[S_{k}=N_{steps}/f_{k-1} \tag{22}\]  
The phase difference can also be compensated with \(\delta_{k}\) in Equation (20). With \(f_{0}=n_{g0}-n_{g1}N_{g}/2\) and the different phase compensation with \(\delta_{k}\), \(\Phi_{e}(x)\) and \(\gamma(x)\) are shown in Figures 7 and 8, respectively. Except for the condition of \(\delta_{k}=\delta_{k-1}-\ \varphi_{kkc}\), \(\Phi_{e}(x)\) is small, and the average contrasts are close to 1. The phase error from adjacent scans still causes \(F_{\rm P2}\) fluctuations in \(\Phi_{e}(x)\) and \(\gamma(x)\).  
As shown in Figure 7, \(\Phi_{e}(x)\) with \(\delta_{k}=0\) is coincident with \(\Phi_{e}(x)\) with \(\delta_{k}=\delta_{k-1}-\ \varphi_{kdx}(X_{k})-\ \varphi_{kdc}\), which implies that the step size expressed with Equation (22) can compensate for the phase difference at \(X_{k}\). We assume that the phase difference at \(x_{comb}\) must be compensated, where \(x_{comb}\) is located between \(X_{k-1}\) and \(X_{k}\). \(x_{comb}\) can be expressed with \(X_{k-1}\) and \(X_{k}\) as  
\[x_{comb}=X_{k-1}+r(X_{k}-X_{k-1}) \tag{23}\]  
where \(r=(x_{comb}-X_{k-1})/(X_{k}-X_{k-1})\) is the ratio. If \(\delta_{k}=0\) for all scans, from Equations (9) and (23), the phase difference at \(x_{comb}\) in the \(k\)-th scan and \((k+1)\)-th scan can be expressed as  
\[\begin{array}{ll}\varphi_{kd}(x_{comb})&=\varphi_{kdx}(x_{comb})+\varphi_ {kdc}\\ &=-2\pi S_{k}[(rf_{k-1}+(1-r)f_{k})]\end{array} \tag{24}\]  
From Equation (24), \(\varphi_{kd}(x_{comb})\) can be compensated with \(S_{k}\), expressed as  
\[S_{k}=\frac{N_{steps}}{rf_{k-1}+(1-r)f_{k}} \tag{25}\]  
Figure 6: Exposure contrast under the condition of \(f_{k}=g(X_{k})\) with different \(\delta_{k}\).  
If \(S_{k}\) is other values, such as an actual step size with a stage motion error, \(\delta_{k}\) can be used for phase compensation, which implies that \(S_{k}\) and \(\delta_{k}\) have similar phase modulation effects.  
In this manner, the groove density coefficients with different phase compensations are shown in Table 2.
# Analysis of Heavy Metal Sources in Xutuan Mining Area Based on APCS-MLR and PMF Model
###### Abstract  
The present study aims to determine the concentrations and forms of Copper (Cu), Lead (Pb), Zinc (Zn), Chromium (Cr), Cadmium (Cd), and Arsenic (As) in water and sediments of the Xutuan mining area. The geoaccumulation index (Igeo) and ecological risk assessment coding (RAC) methods were used to assess heavy metal pollution levels and ecological risks in sediments. The positive matrix factorization (PMF) model and the absolute principal component score-multiple linear regression (APCS-MLR) model were used to quantitatively analyze the sources of heavy metals in the evaluated sediments. The results showed good water quality in the mining area. Cu, Cr, Zn, and As in the sediments were mainly in the residual form, while Cd and Pb were mainly in the organic matter combined form. The Igeo and RAC results showed that the Cd pollution degree and ecological risk were higher in the sediments. The APCS-MLR and PMF models analyzed the contributions of natural sources (72.5% and 25.1%) and anthropogenic sources, respectively, while the PMF further distinguished the contributions of coal mining (26.4%), agricultural (21.44%), and traffic (27.05%) sources.  
heavy metals; source analysis; PMF model; APCS-MLR model 2025
# Cavity Design in Woodpile Based 3D Photonic Crystals
## 2 Calculation Methods  
We first use the plane-wave expansion (PWE) method [52] to calculate the band structure for the woodpile template without any defect in the frequency domain. This gives us the dispersion relation of the woodpile template and the position of the complete photonic bandgap. We then use our in-house three-dimensional finite-difference time-domain (FDTD) software [17; 20; 21; 53] with perfectly-matched layer (PML) boundary and a non-homogeneous mesh of around \(10^{7}\) cells to implement each simulation when various defects are introduced into the woodpile template. A broadband dipole source and multiple field probes are placed inside the defect to excite and monitor the field decay over time. The resonant frequencies \(\mathrm{f_{c}}\), linewidths \(\Delta\mathrm{f}\) and \(\mathrm{Q}\) factors (defined as \(\mathrm{f_{c}}/\Delta\mathrm{f}\)) are then estimated by analysing the field decay in the frequency domain via the fast Fourier transform (FFT) and the filter diagonalization method using the Harminv software [54]. Effective mode volumes are also calculated using the definition,  
\[\mathrm{V_{eff}}=\int\!\!\int\!\!\int\!\!\varepsilon(\mathbf{r})\,|\,\mathrm{E }(\mathbf{r})\,|^{\,2}\;\mathrm{d}^{3}\tau/[\varepsilon(\mathbf{r})\,|\, \mathrm{E}(\mathbf{r})\,|^{\,2}]_{\mathrm{max}}, \tag{1}\]  
where \(\mathrm{E}(\mathbf{r})\) is the resonant field and \(\varepsilon(\mathbf{r})\) is the dielectric constant at position \(\mathbf{r}\). For each resonant mode, we take the integration of the mode energy through sufficient computational volume to ensure convergence.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 3 Modeling of UWA Channel Problems and Challenges
Adaptive equalization methods and adaptive channel tracking have been used to handle operations in these types of dynamic environments, specifically by employing the algorithm called exponentially weighted recursive least squares (EW-RLS). This subdivision provides some features and factors associated with the modeling of the UWA channel. The channel parameters considered in this article are given in Table 4.