esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Proposed Solution
### ANN-Based Detection System
#### 7.1.1 Dataset Description  
The \"Novel SDN Dataset for Intrusion Detection in IoT Networks\" [53] is used in this work. This dataset is designed to support research in intrusion detection for IoT networks using SDN. It contains network traffic captured from a software-defined network (SDN) in an Internet of Things (IoT) environment during various cyberattacks launched by four attacker hosts against a server and other simulated IoT devices exchanging a large amount of data. The attacks include DoS, DDoS, port scanning, OS fingerprinting, and fuzzing, and were performed with different configurations 10 to 12 times each, depending on the attack type. The testbed used in the experiment was set up using Mininet to simulate an IoT network. It was similar to the Bot-IoT setup and managed by the ONOS controller. The size of the normal traffic is about 2.67 M representing 8.84% of the whole traffic, while DDoS traffic size is around 182 K; the rest of the traffic is related to the other attacks.  
This dataset provides a rich set of features that can be used to develop and evaluate intrusion detection systems for IoT networks such as packet counts, byte counts, source and destination IP addresses, port numbers, category, and more. These features are used to develop machine learning models or other analytical techniques that can automatically identify and flag anomalous network activity.
# Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach
## 2 Related Works  
Although the battery limited problem can be solve through the wireless power transfer technique [14], the resource allocation problem in the wireless powered IoT system, especially the power control problem, is still an urgent problem that desperately needs to be solved. A large number of works have been done in this area [15; 16; 17; 18]. In [15], the authors solve the resource allocation problem in cyber-physical IoT to maximize the energy efficiency. The proposed resource allocation scheme is based on the mixed integer non-convex programming theory and can be divided into two sub-problems, the power allocation problem and the channel allocation problem, and the Dinkelbach's algorithm is used to solve the proposed optimal allocation problems.  
In [16], a green resource allocation method is proposed, which considers the QoE as the main influencing factor. Then the authors use the deep reinforcement learning to solve the QoE based resource allocation problem.  
In [17], a utility-lifetime maximization problem is considered for resource allocation. The authors use the Lagrange multiplier method to solve the proposed distributed dual subgradient algorithm. The wireless energy harvesting, wake-up radio and error control coding are all considered in model formulation.  
In [18], the authors formulate a distributed power control problem in the wireless powered communication networks as a utility maximization problem, to guarantee the QoS demand and to achieve efficient energy management. In this paper, the authors propose an energy-efficient communication approach considering both the WET and WIT phase. The optimal charging power for the energy source can be determined.  
The resource allocation problem in wireless powered IoT system has been considered and researched by lots of academies, but most of the previous works do not consider the size of the IoT system. When there are mass sensors in the IoT system, it is difficult to obtain the optimal power control strategy for each sensor. Meanwhile, the dynamic variation of the battery's energy of the sensor node will also affect the resource allocation strategies, which is also not considered in the previous works.
## 3 Improvement of DSOGI-PLL  
When the grid voltage harmonic content is minimal, the DSOGI-PLL effectively utilizes its inherent filtering capabilities to precisely extract the positive-sequence phase and frequency signals of the grid voltage. However, in scenarios with high grid harmonic content, the extraction accuracy of positive-sequence voltage-related signals may be compromised. In this section, we propose an enhancement to the traditional DSOGI-PLL by incorporating an Advanced Harmonic Elimination (AHE) module prior to the extraction of positive and negative sequences. This enhancement involves cascading multiple AHEs to eliminate harmonics of various orders, thereby enabling accurate extraction of positive-sequence-related signals even when the grid voltage contains substantial asymmetric harmonics [17; 18; 19].
# Examining Gender Bias of Convolutional Neural Networks via Facial Recognition
## 4 Discussion
### Conclusions  
There were several clear winners in our experiments, with respect to both accuracy as well as gender bias resistance. VGG-16 in particular showed great accuracy across all datasets and image subsets scoring the highest out of all of our tested CNN models in the majority of our testing. In addition, the VGG-16 architecture continued to have a gender disparity of less than 10 percent across all tests that were conducted. On the other hand,  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**CNN Model** & **Balanced** & **Male Dominant** &
\begin{tabular}{l} **Female** \\ **Dominant** \\ \end{tabular} & **Disparity \textgreater{} 10\%?** \\ \hline AlexNet & 68\% & 74\% & 62\% & YES \\ Xception & 62\% & 66\% & 54\% & YES \\ Inception v2 & 76\% & 77\% & 65\% & YES \\ Inception v3 & 78\% & 81\% & 70\% & YES \\ ResNet50 & 80\% & 82\% & 75\% & No \\ ResNet101 & 77\% & 79\% & 68\% & YES \\ VGG16 & 83\% & 83\% & 78\% & No \\ VGG19 & 84\% & 85\% & 79\% & No \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of tested CNNs by gender, CASIA-FASD Dataset, 80/20 split.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**CNN Model** & **Balanced** & **Male Dominant** &
\begin{tabular}{l} **Female** \\ **Dominant** \\ \end{tabular} & **Disparity \textgreater{} 10\%?** \\ \hline AlexNet & 76\% & 80\% & 69\% & YES \\ Xception & 68\% & 76\% & 63\% & YES \\ Inception v2 & 82\% & 82\% & 75\% & No \\ Inception v3 & 81\% & 84\% & 72\% & YES \\ ResNet50 & 85\% & 89\% & 83\% & No \\ ResNet101 & 82\% & 85\% & 79\% & No \\ VGG16 & 88\% & 91\% & 84\% & No \\ VGG19 & 88\% & 90\% & 85\% & No \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of tested CNNs by gender, SiW Database, 80/20 split.  
while VGG-19 attained accuracy nearly on par with its counterpart, that model stumbled with the WMCA and combined datasets where gender bias resistance is concerned.  
ResNet50 was another bright spot for our CNN models. While having accuracy that was slightly lower than either VGG model, ResNet50 was able to attain good ratings with the gender disparity testing, with results no worse than 8 percent. Due to these results, in future work concerning this topic, VGG-16 and ResNet50 should be put forth in the attempt to create a hybrid CNN model for the purpose of mitigating gender bias.  
Unfortunately, AlexNet and Xception performed poorly in all of our testing when compared to the other CNN models. Not only did they show lackluster accuracy levels, they also showed the potential for gender bias in every single test conducted. While the Inception models showed better accuracy than AlexNet or Xception, they too showed that they were not immune to potential gender bias, having a gender disparity score of greater than 10 percent in all but one test that were conducted.
# SD-BROV: An Enhanced BGP Hijacking Protection with Route Validation in Software-Defined eXchange
## 2 Background and Related Work
### Route Validation with RPKI  
Currently, RPKI is expected to be a way of protecting BGP routers against poisoning. By leveraging crypto signature method [4], IP prefix announcement will be binding with the advertised AS originally. This mechanism provides a route validation operation with reliable, trusted infrastructure [3] for routers connected to a global network.  
**Route Origin Authorization:** Since the route exchange procedure is based on peering among ASes, it is a challenge to avoid routers suffering from learning poisoned advertisements from its neighbors. Therefore, the practice of RPKI is to add an out-of-band controlplane to help the router to determine which AS can announce which route. During the validation, RPKI provides the necessary information of IP and ASN pairs, which is called Route Origin Authorization (ROA [13]). It is a signed object that makes use of RFC3852 and RFC3779 with X.509 certification. A ROA record is shown in Figure 1. By comparing received advertisements with acquired ROA records, RPKI-enabled routers are able to filter the irrational routes to prevent BGP hijacking.  
**RPKI Certification:** To establish RPKI, Internet Routing Registry (IRR) plays a vital role in its deployment and operation. The reason is that IRR has correct registration information for determining which AS is the real owner per IP prefix. In the RPKI certification scenario, IRR acts as the certificate authority, called Trust Anchors (TA). At present, the operational RPKI infrastructure in the world has five default TAs: AFRINIC, APNIC, ARIN, LACNIC, and RIPE NCC [14]. RPKI downstreams (i.e., BGP routers) have to keep in syncing validation information from repositories of TAs, making sure the ROA matches the latest allocation. The used protocol, RPKI to Router (RTR), carries the serial number, IP prefix (both IPv4 and IPv6), ASN, and max length parameter (optional) to help router make validation. The communication between router and RPKI validator is using TCP, and it is encouraged to enable TLS for strengthen transmission security. More detailed specification information can be found in [15].  
**Hosted and Delegated RPKI Services:** By today, the default five IRRs have hosted RPKI service [16]. However, due to the scope of global network, it is not suitable to count on such a centralized service in supporting large-scale and distributed network architecture. In production operation, it is more graceful to interact with Regional Internet Registry (RIR), National Internet Registry (NIR), or Local Internet Registry (LIR) for processing RPKI validation. Furthermore, it is also available for a small-scale organization to set up delegated RPKI service in its domestic area. With authorization from parent TAs, a delegated RPKI server can be the child service of RIR/NIR/LIR, signing ROA for local end-customers.
# Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems
###### Abstract  
The Internet of Things (IoT) brings internet connectivity to everyday devices. These devices generate a large volume of information that needs to be transmitted to the nodes running the IoT applications, where they are processed and used to make some output decisions. On the one hand, the quality of these decisions is typically affected by the freshness of the received information, thus requesting frequent updates from the IoT devices. On the other hand, the severe energy, memory, processing, and communication constraints of IoT devices and networks pose limitations in the frequency of sensing and reporting. So, it is crucial to minimize the energy consumed by the device for sensing the environment and for transmitting the update messages, while taking into account the requirements for information freshness. Edge-caching can be effective in reducing the sensing and the transmission frequency; however, it requires a proper refreshing scheme to avoid staleness of information, as IoT applications need timeliness of status updates. Recently, the Age of Information (AoI) metric has been introduced: it is the time elapsed since the generation of the last received update, hence it can describe the timeliness of the IoT application's knowledge of the process sampled by the IoT device. In this work, we propose a model-driven and AoI-aware optimization scheme for information caching at the network edge. To configure the cache parameters, we formulate an optimization problem that minimizes the energy consumption, considering both the sampling frequency and the average frequency of the requests sent to the device for refreshing the cache, while satisfying an AoI requirement expressed by the IoT application. We apply our caching scheme in an emulated IoT network, and we show that it minimizes the energy cost while satisfying the AoI requirement. We also compare the case in which the proposed caching scheme is implemented at the network edge against the case in which there is not a cache at the network edge. We show that the optimized cache can significantly lower the energy cost of devices that have a high transmission cost because it can reduce the number of transmissions. Moreover, the cache makes the system less sensitive to higher application-request rates, as the number of messages forwarded to the devices depends on the cache parameters.  
IoT; Age-of-Information; edge-caching; cache refreshing +
Footnote †: journal:
## 5 Conclusions  
Aiming at the problems such as unpredictable precision, construction cost, manufacturing cost and time cost, a mathematical model of mobile robot system construction for multi-mounting surface milling of large spacecraft cabins was proposed, and a multi-objective optimization method for mobile robot system construction based on a genetic algorithm was proposed, which gave the optimal solution of system construction. The effectiveness and feasibility of the method were verified by experiments.  
The method in the current paper can build an effective milling system of mobile robots according to the needs of machining objects, and estimate its construction cost, machining accuracy and machining efficiency, so as to realize the prediction of milling accuracy in advance. In addition, under the existing equipment and processing conditions, this method can be used to reasonably select mobile robots, measuring equipment, omnidirectional mobile platform, and other units, which can ensure the processing accuracy, maximize the productivity, and provide guidance for the construction of the on-site engineering system.  
Figure 8: On-site construction of mobile robot systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Final Milling Error** & **Total Cost\(f_{2}\)** & **Milling Time\(f_{3}\)** \\  & \(f_{1}\) (mm) & **(Ten Thousand Yuan)** & **(Hour)** \\ \hline Scheme 1 of System 1 & 0.384 & 265 & 0.615 \\ Scheme 2 of System 2 & 0.356 & 280 & 0.152 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The actual processing result of the mobile robot.  
**Author Contributions:** Conceptualization, K.W. and J.Z.; methodology, K.W.; software, K.W.; validation, K.W., T.C. and X.Z.; formal analysis, Z.Z.; investigation, K.W.; resources, W.Z.; data curation, Z.Z., T.C., X.Z. and K.W.; writing--original draft preparation, K.W.; writing--review and editing, K.W. and Z.Z.; visualization, K.W.; supervision, J.Z.; project administration, X.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 62003346, 52075533, 52275517 and by National Defense Basic Scientific Research program of China, grant number 2021203B035.  
**Institutional Review Board Statement:** No applicable.  
**Informed Consent Statement:** No applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Meander Line Super-Wideband Radiator for Fifth-Generation (5G) Vehicles
## 3 Antenna Design
### Design of 5G FR1 Meander Line Antenna  
The 5G frequency range 1 (FR1) antennas operate in the frequency range from 410 MHz to 7.125 GHz. They are commonly used for 4G LTE, 5G sub-6 GHz, and other wireless communication standards commonly used for cellular networks and offer good coverage but limited data transfer speeds [2].  
Figure 3 displays the construction of Antenna 1 (Ant 1): a meander line antenna that operates at 2.4 GHz and uses the 5G FR1 mathematical expressions from Equations (8)-(11). Ant 1 through Ant 4 were analyzed using the High-Frequency Structure Simulator (HFSS) EM suite version 2020R1. The geometry and structure of Ant 1 are depicted in Figure 3a, and the dimensions are 42 mm \(\times\) 30 mm \(\times\) 1.6 mm. Initially, a meander line monopole antenna with a continuous ground was designed, but the results were inadequate as it had dual broadband frequency at 2.2 GHz and 8.5 GHz center frequencies and resonated from 2.14 GHz to 2.30 GHz and 7.76 GHz to 8.57 GHz. The vertical length (\(L_{g}\)) of the Ant 1 bottom was analyzed for optimization purposes to achieve the required bandwidth at a central frequency of 2.4 GHz. The results indicated that Ant1 with a partial ground structure had a broad dual frequency compared to Ant1 with a full ground, as shown in Figure 3a. Ant1 covers the 2.24 GHz to 2.74 GHz and 4.89 GHz to 7.62 GHz frequency ranges with a \(-19.97\) dB and \(-16.87\) dB reflection coefficient at 2.4 GHz and 5.8 GHz, respectively. Figure 3b highlights the \(-19.97\) dB reflection coefficient and peak gain of Ant1, which is 2.32 dBi for the 2.4 GHz operating frequency. The antenna gain at 5.8 GHz exhibits a modest 1.25 dBi--though it is important to note that this design serves as our baseline design and is primarily utilized for validating our mathematical analyses. Our primary focus lies in the creation of an antenna that operates for 5G-enabled vehicles functioning within the 28 GHz band. Our efforts in this regard have resulted in good antenna gain that surpasses 7 dBi in the 28 GHz band, which meets the essential requirements for 5G vehicular applications.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{\(\lambda_{g}\)} & \multicolumn{6}{c}{**Theoretical**} \\ \cline{3-8}  & & **h** & **w** & **d** & **L\({}_{m}\)** & **L\({}_{s}\)** & **W\({}_{s}\)** \\ \hline
**Ant1** & 6.04 & 20.37 & 3.02 & 9.66 & 28.38 & 30 & 42 \\
**Ant2** & 5.56 & 3.37 & 2.33 & 2.64 & 9.95 & 12 & 22 \\
**Ant3** & 5.56 & 5.11 & 1.39 & 1.39 & 12.35 & 15 & 34 \\
**Ant4** & 5.56 & 5.19 & 5.22 & 4.67 & 19.83 & 20 & 34 \\ \hline \end{tabular}
\end{table}
Table 1: Meander line theoretical formula and experimentally calculated values (in mm).
## 3 Results
### Kinetics of Photocatalytic Oxidation  
Because the photocatalytic oxidation process mainly involves two stages, one of adsorption of the organic compound on the catalyst surface and another of photocatalytic oxidation itself, the experimental results were interpreted based on the Langmuir-Hinshelwood (L-H) model which takes into account these two main stages. The Langmuir  
Figure 4: Adsorption efficiency of 2,4-DCF from aqueous solutions under different pH conditions and different initial concentrations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**pH**} & \multicolumn{8}{c}{**Initial Concentration, mg OC/L**} \\ \cline{2-13}  & \multicolumn{4}{c}{**37.50**} & \multicolumn{8}{c}{**75.00**} & \multicolumn{8}{c}{**112.5**} \\ \cline{2-13}  & **k 1** & **qe 2** & **h 3** & **t\({}_{\text{1/2}}\) 4** & **k** & **qe** & **h** & **t\({}_{\text{1/2}}\)** & **k** & **qe** & **h** & **t\({}_{\text{1/2}}\)** \\ \hline
2 & 0.033 & 8.120 & 2.176 & 3.732 & 0.010 & 14.347 & 2.058 & 6.993 & 0.009 & 18.553 & 3.098 & 5.988 \\
3 & 0.037 & 8.489 & 2.666 & 3.185 & 0.011 & 15.244 & 2.556 & 5.952 & 0.013 & 19.417 & 4.901 & 3.968 \\
5 & 0.023 & 6.807 & 1.066 & 6.410 & 0.009 & 13.193 & 1.566 & 8.403 & 0.008 & 15.601 & 1.947 & 8.000 \\
7 & 0.017 & 5.485 & 0.512 & 10.753 & 0.007 & 11.186 & 0.876 & 12.820 & 0.005 & 12.788 & 0.818 & 15.625 \\ \hline \hline \end{tabular} \({}^{1}\) (g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\cdot\))\({}^{2}\) (mg/g)\({}^{3}\) (mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-1}\cdot\))\({}^{4}\) (min).  
\end{table}
Table 3: Fitting parameters for pseudo-second-order kinetics of 2,4-DCF adsorption on TiO\({}_{2}\)/stainless steel photocatalytic membrane.  
Hinshelwood (L-H) model was initially used to quantitatively describe solid-gas reactions. Currently the Langmuir-Hinshelwood model is also used to describe the kinetics of reactions that take place in the solid-liquid system by a rate law in which the reaction rate (r) is directly proportional to the fraction of surface area occupied by the substrate (\(\theta\)) [29; 30]:  
\[\mathrm{r}=-\mathrm{d}\mathrm{C}/\mathrm{dt}=\mathrm{k}\theta \tag{10}\]  
Considering the form of the Langmuir equation:  
\[\theta=\mathrm{K}\mathrm{C}/(1+\mathrm{K}\mathrm{C}) \tag{11}\]  
and substituting in the above equation obtains:  
\[\mathrm{r}=-\mathrm{d}\mathrm{C}/\mathrm{dt}=\mathrm{k}\theta=\mathrm{k} \mathrm{K}\mathrm{C}/(1+\mathrm{K}\mathrm{C}) \tag{12}\]  
where k represents the rate constant, which is influenced by several parameters including catalyst mass, photon flux, etc. K represents the Langmuir-Hinshelwood equilibrium adsorption constant. Usually, the value of K is obtained from the Langmuir equation from kinetic studies performed in the presence of light, the results being much better than those obtained from studies in the dark. C represents the concentration of the organic substrate at time t. By integration, the above equation becomes:  
\[\mathrm{ln}(\mathrm{C}_{0}/\mathrm{C})+\mathrm{K}(\mathrm{C}_{0}-\mathrm{C})= \mathrm{k}\mathrm{K}\mathrm{t}\] (1
## 4 Materials and Methods
### SLC10A1 Sequencing and Genotyping  
Purification of genomic DNA from EDTA blood samples was performed as described previously [67]. The _SLC10A1_ gene region (chr14:70,242,552..70,264,006; hg19) was analyzed systematically in the 143 individuals for the presence of genetic variations by NGS, Sanger sequencing, MALDI-TOF MS, and microarray technology. The analyzed region covered 2 kb promoter region, all exonic, as well as flanking intronic regions, and the 5'UTR and 3'UTR regions.
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 4 Proposed Approach  
In this section, we present our proposed approach for clustering Arabic text, leveraging the GWO algorithm to enhance the clustering results for SOM. Instead of relying on the default random initiation process, our approach involves optimizing the clustering process by fine-tuning the SOM's initial weights. Our proposed approach has two main phases. First, we run GWO to find the optimized SOM initial weights. Then, we use the output of the previous phase to adjust the SOM's initial weights and run it. The following are the details of these two phases.
## 4 Local-Global Dynamic Multi-Graph Convolutional Neural Network Model
### Local-Global Dynamic Spatiotemporal Graph Convolution
#### 4.1.2 Local Spatiotemporal Module  
Following the dynamic graph generator, three dynamic multi-graphs are generated for each time interval. Since the dynamic distance graph \(G_{dis}^{t}\) reflects local positional semantic information, it is employed in the Local Spatiotemporal Module (Local ST Block). The Local Spatiotemporal Module comprises two Gated Temporal Convolutions (Gated TCN) and one Dynamic Graph Convolution (Dynamic GCN).  
The specific structure of the Gated TCN is illustrated in Figure 7[40]. Dynamic Graph Convolution employs a variant of graph convolution proposed by [PERSON] et al. [42].  
Assuming there are \(N_{F}\) feature sequences, extended to dynamic multidimensional input \(X\in\mathbb{R}^{N\times N_{F}}\). The structure of the Local Spatiotemporal (LDST) Module is as follows: Gated Temporal Convolution-Dynamic Graph Convolution-Gated Temporal Convolution. The calculation formula is given by Equation (6):  
\[\text{LDST}(X)=\Gamma\ _{*_{\mathcal{T}}}\ \text{ReLU}(\widetilde{D}^{-\frac{1}{2}} \ \widetilde{A}_{loc}^{t}\ \widetilde{D}^{-\frac{1}{2}}(\Gamma\ _{*_{\mathcal{T}}}\ X)\Theta_{gen}) \tag{6}\]  
where \(\Gamma\) is the convolutional kernel, \(\ {}_{*_{\mathcal{T}}}\) denotes the convolution operation, \(A_{loc}^{t}\) represents the adjacency matrix of the dynamic distance graph \(G_{dis}^{t}\), \(\widetilde{D}\) is the degree matrix, and \(\Theta_{gen}\) represents learnable parameters.  
Figure 7: Gated Temporal Convolution Module.