esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results  
### Simulation Setup  
The LiZhou South Road--South City Road intersection in Jinhua City was chosen as the study site, and a simulation model was built in the SUMO simulation software (1.19.0). The intersection's approach length is 400 m, with a speed limit of 50 km/h and a lane width of 3.75 m. The vehicle length is 5 m. The vehicle-following model adopted is the IDM, with  
Figure 4: Rainbow DQN_AM network structure diagram.  
a maximum acceleration of 2.6 m/s\({}^{2}\)[35], a maximum deceleration of \(-\)4.5 m/s\({}^{2}\)[19], a minimum safe following distance of 7.26 m [48; 49], and a minimum stopping distance of 2.5 m [22]. The road structure of the simulated intersection is shown in Figure 5.  
The signal timing data for the LiZouth Road--South City Road intersection in Jinhua City was collected. The actual signal cycle for the intersection is 179 s, with a yellow light duration of 3 s. Right-turning vehicles at this intersection are not controlled by the traffic signals. The specific phase distribution is shown in Figure 6.  
Considering the traffic flow characteristics during the morning and evening peak hours on urban roads, the traffic volume during peak periods often exhibits extreme fluctuations, where the flow quickly increases within a short period, maintains a peak state, and then sharply decreases. The Weibull distribution is well suited for capturing the significant fluctuations, extreme values, and nonlinear characteristics of peak traffic, making it ideal for simulating traffic distribution during peak hours. Therefore, traffic volume is generated based on the Weibull distribution. The simulation duration is set to 2 h, based on the  
Figure 5: The network structure of the intersection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Straight-Through Ratio (\%)** & **Left-Turn Ratio (\%)** & **Right-Turn Ratio (\%)** \\ \hline East Approach & 10.29 & 4.56 & 5.03 \\ West Approach & 17.03 & 6.28 & 5.07 \\ South Approach & 14.16 & 4.09 & 4.56 \\ North Approach & 17.64 & 4.90 & 6.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vehicle turning ratios for each approach at the intersection.  
Figure 6: Actual signal timing of the intersection.  
collected traffic volume data, and the parameters of the probability distribution function are configured according to the method in reference [35]. The probability density function is as follows:  
\[f(x;\lambda,\alpha)=\left\{\begin{array}{l}\frac{\alpha}{\lambda}(\frac{x}{ \lambda})^{\alpha-1}e^{-(\frac{x}{\lambda})^{\alpha}},x\geq 0\\ 0,x<0\end{array}\right. \tag{21}\]  
Here, \(\lambda\) is the scale parameter set to 1, and \(\alpha\) is the shape parameter set to 2.
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 2 Materials and Methods
### Distributed Denial of Service (DDoS) Attacks  
Denial of service (DoS) is an attack method that employs network protocol weaknesses to consume target resources and prevent victim hosts or network services from operating normally. However, because of the assault's sluggish tempo and restricted attack area, the adversary is unable to initiate a large-scale flood. DDoS is a scaled-up version of a DoS attack. The principle is that hackers hijack a large number of normal hosts to make them puppet hosts and send flood packets to the server to consume network resources and link bandwidth. The DDoS assault is currently regarded as the most dangerous attack method in the area of Internet security. The adversary can use the asymmetry of resources to generate large amounts of attack traffic, which eventually leads to a server crash. Figure 2 illustrates the DDoS attacks on LSCNs. The specific manifestations are as follows:  
* Generate a lot of useless data, blocking satellite communication and making the attacked host unable to respond to user requests normally;
* Utilize the flaws in the network protocol of the attacked host to send repeated service requests repeatedly so that the attacked host cannot process the normal requests of users in time;
* Utilize the flaws of the attacked host's Internet to repeatedly send malformed attack data, thus occupying most of the host's memory and crashing the host.  
The satellite network is characterized by decentralized users and a high degree of node autonomy, and the trust mechanism between nodes has become a major network security risk, especially for DDoS attacks, which have natural vulnerabilities. The study of DDoS attacks on LSCNs is still in its early stages at the moment. Similar to the terrestrial Internet, LSCNs are mainly subject to the following four popular DDoS attacks [20; 25]:  
* ICMP Flood: This attack sends a large number of ping packets to the victim in a short period of time and uses the method of exhausting the victim's resources to achieve the purpose of paralyzing the server so that it cannot continue to work normally;
* TCP SYN Flood: This attack captures the defect of the TCP three-way handshake and four-way teardown protocol and initiates many false SYN connection request packets  
Figure 1: Space-time graph model.  
to the target host, which continuously occupies the resources of the target host and eventually causes the network to be paralyzed;
* UDP Flood: This attack sends a large number of UDP packets to the victim in a short period of time, making the victim overloaded and unable to undertake normal transmission work, exhausting the resources of the target host;
* HTTP Flood: This attack floods normal services in the network by sending malformed HTTP protocol packets, which can cause more damage without high rates.
## 4 Experiment  
In this section, we evaluate the proposed DualET on satellite images and actual PV station data. We first introduce the datasets and data preprocessing. Then, we describe the experimental setting in detail. Finally, we compare the prediction performance of DualET and the baseline models and conduct several ablation experiments.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 8 Conclusions and Avenues for Future Research  
Although the three procedures offer similar performances at low traffic loads, this paper has shown that, at higher traffic loads, relying solely on link status (LS) messages or on the average of link quality indicators (LQI) of received packets to estimate link costs and select routes can degrade the performance of ZigBee's route selection algorithm, particularly in asymmetric scenarios. This conclusion is in agreement with conclusions reached by other authors, which showed, in protocols other than ZigBee, that beacon-based link estimation procedures produce degraded performance.  
Given the problems of LS-based and LQI-based procedures in higher traffic loads, we proposed the U-RR (unicast round-robin) procedure. The U-RR procedure uses MAC information regarding unicast transmissions to estimate the probability of successful transmissions on a link; and uses a  
Figure 14: (a): Cumulative distribution of the number of node 3 retransmissions per 1000 messages generated. (b): Boxplots of the percentage reduction in the number of node 3 retransmissions per 1000 messages generated obtained by the U-RR procedure when compared to the LQI-based and the LS-based procedures.  
Figure 13: Topology where nodes 1, 2, 4, and 5 are randomly located.  
modified route selection mechanism to decide among routes that have the same cumulative route cost. The modified route selection mechanism indirectly makes a node select among such routes in a round-robin fashion in order to improve the link cost estimation of candidate routes. The modified route selection mechanism is particularly important in ZigBee networks because ZigBee nodes have to quantize the link costs in just three bits, causing many route candidates to have the same cumulative route cost.  
Our simulation results show that the U-RR procedure reduces the variance of link cost estimations, allowing nodes to better differentiate among routes, and increases the number of times that the best route is selected. The benefits of the U-RR procedure are more relevant under higher traffic loads and in asymmetric scenarios; however, even in other scenarios, the U-RR procedure performs at least as well as the LS-based procedure.  
Lastly, it should be mentioned that the U-RR procedure is not necessarily the best link cost estimator and route selection procedure, and algorithms such as those mentioned in Section 3.1 would most likely offer improved performance; however, such algorithms would require a significant revision of the ZigBee protocol and frame formats. In contrast, our U-RR procedure offers improved performance to ZigBee networks while neither requiring new protocol messages nor changes to frame formats.
## 2 State-of-the-Art  
### Land Use and Urban Planning Applications  
Recent proposals for urban growth cover aspects such as the potential exploitation of land uses: those that allow to accommodate a high urban diversity. Land uses are thus defined as a measure of the diversity of uses contained in a given space [13], which allows in a specific way to identify nearby uses or nearby activities developed in a limited spatial range. The term _land use_ refers to the employment that human beings give to a portion of land based on the activities carried out in it. However, in order to use land uses in urban management and planning, an updated map with the most recent and accurate information is necessary [4; 14] to make the right decisions [15]. Under this situation, several terms emerge, such as _urban computing_[16] and urban planning applications [17], which have become an emerging research area where urban problems in cities are studied using different data sources such as electronic devices--mainly smartphones, location-based social media, web pages--e.g., analyzed with Robotic Process Automation technologies, among other digital information.  
Many approaches include the use of images (e.g., from satellites), sometimes together with some structured data (e.g., points of interest). For example, [PERSON] et al. [1] use natural physical features from high spatial resolution images (HSR) and socio-economic semantic features (frequent characteristic words related to an urban land type) from social data to create a dictionary of land use words, including data from multiple sources such as OpenStreetMap road networks, Gaode's Points of Interest, and Tencent's real-time user density. On the other hand, [PERSON] et al. [18] use large-scale Twitter log data, and they propose a pre-processing method in which the raw data only contains coordinates and activity category information. One of the few papers that mentions data pre-processing is [8], which used Twitter posts and metadata. From each tweet, the authors use text to analyze whether a space is a restaurant, airport, or stadium. They perform pre-processing of the text of each tweet before using a term frequency-based technique, previously removing emoticons and other non-ASCII characters, as well as hashtags and the \"@\" character.
# SSNet: Learning Mid-Level Image Representation Using Salient Superpixel Network
## 2 Related Work
### Research Work about Burstiness Issue  
In [10], [PERSON] et al. point out the burstiness phenomenon and analyze its effect on object recognition tasks. In the context of image classification, burstiness weakens the discriminative ability of an image representation.  
In order to deal with the burstiness interference originating from the background area of an image, the studies [12; 13; 47] use a detector to locate the object first and then pool the foreground and background features separately to form the mid-level representation. In [12], [PERSON] et al. propose a framework that directly optimizes the classification objective with treating object detection as an intermediate step. For the fine-grained object classification task, [PERSON] et al. [13] introduce the object detection and segmentation algorithm to localize and normalize the object. They segment the possible object of interest before trying to recognize it. Although this method alleviates the impact of the burstiness to some extent, the obtainment of pretrained detectors is a time-consuming procedure. In addition, some methods [14; 15; 48; 49] are proposed to weight different regions by saliency values. In [15], [PERSON] et al. extend the notion of discriminative visual saliency by including discriminative spatial information and obtain a more discriminative image representation for visual classification. [PERSON] et al. [14] propose a new feature generation approach by encoding the salient object region information to the histogram-based features for image classification. However, in these methods, the difference between features in the detected salient regions is ignored.  
For the burstiness phenomenon happening in the foreground region, researchers [11; 17; 50] try to solve it by the proposed normalization or pooling operation. In [50], [PERSON] et al. use the power normalization technology to adjust the feature distribution. In [11; 17], the authors adopt separately the Max-pooling and Generalized Max Pooling operation to reduce the impact of the burstiness.  
In the SSNet framework, a novel saliency detection method is proposed first to alleviate the side effect of the burstiness inside the non-salient regions of an image. Then, we further put forward a novel weighting scheme to balance the influence between the frequently occurring and rarely occurring features, and reduce the side effect of the burstiness in the object regions.
## 2 Coaxial Magnetic Gear Train (CMGT)  
There are various magnetic gear mechanism topologies. The CMGT, i.e., a flux-modulated magnetic gear mechanism with coaxial topology, has been developed rapidly in recent years. Most of the designs focus on the torque density achieved by changing their topologies. Furthermore, to obtain the optimum scheme of the CMGT model, [PERSON] et al. [13] proposed an analytical model considering magnetic saturation. In order to support an understanding of the working principle of the CMGT. some equations are then discussed [14; 15].  
The CMGT consists of four mechanical parts: a frame, two rotors mounted with a different pole pairs number of permanent magnets, and a carrier with ferromagnetic pole-pieces sandwiched between two rotors. The inner permanent magnets are mounted on the outer surface of the inner rotor with a high rotational speed. The ferromagnetic pole-pieces are arranged in carrier slots. The outer permanent magnets are mounted on the inner surface of the outer rotor, which has a slow rotational speed. The inner rotor, carrier, and outer rotor are separated by the inner and outer air-gaps. From this configuration, the modulation effect caused by the pole-pieces is a vital part in transmitting torque from the input link, and driving the output link at a specific rotational speed. The detailed depiction of the CMGT topology is shown in Figure 1.  
The numbers of magnet pole pairs of the inner and outer rotors, and the number of pole-pieces on the carrier can be determined by Equation (1) [8]:  
\[N_{c}=p_{i}+p_{o}, \tag{1}\]  
where \(p_{i}\) is the number of magnet pole pairs on the inner rotor, \(p_{o}\) is the number of magnet pole pairs on the outer rotor, and \(N_{c}\) is the number of ferromagnetic pole-pieces on the carrier. Obtaining the magnet pole-pair number affects the gear ratio of the CMGT. The kinematic relationship of the inner rotor, the carrier, and the outer rotor of the CMGT can be expressed as Equation (2) [9]:  
\[p_{i}\;\omega_{i}-N_{c}\;\omega_{c}+p_{o}\;\omega_{o}=0, \tag{2}\]  
where \(\omega_{i}\) is the rotational speed of the inner rotor, \(\omega_{o}\) is the rotational speed of the outer rotor, and \(\omega_{c}\) is the rotational speed of the carrier.
# Parallelization of Array Method with Hybrid Programming: OpenMP and MPI
## 1 Introduction
The hybrid MPI-OpenMP programming model intuitively matches the characteristics of a cluster of SMP nodes, since it allows for a two-level communication pattern that distinguishes between intra- and inter-node communication [2,8]. Similarly, this type of model exposes most coordination issues such as problem decomposition, communication and synchronization to the programmer, although low-level models offer extensive tuning opportunities for expert programmers at the cost of significant effort [9,10]; this programming, also considered as low-level abstraction and explicit control, enables manual optimization and may result in high performance on a single architecture [10]. These languages are heavily used in the industry [10,11]. The development of this research work focuses only on converting a sequential application to a parallel application [12], taking as an example some references in the literature that have made this conversion [3,13,14,15]. The work focuses specifically on the conversion of the array method for task scheduling using the array method [6] (for a broad reference to the literature on task scheduling, the reader can consult [6]), with the search for free resources through Levy random walks considering the works of this research area [16,17,18,19,20] and the design of the parallel application with existing criteria in the literature [4,21,22,23]. Converting a sequential application to a parallel application is a common practice to speed up application performance and exploit multi-core architectures as well as make applications more energy efficient. Task planning is an application that, due to its intrinsic nature of being an NP-complete (non-polynomial) problem [24], can be approached in its two phases (planning and assignment) through architectures that support the development of parallel software systems with languages such as C and C++ and the OpenMP and MPI libraries, because they produce large execution times. Research works related to task planning in HPC systems do not carry out the search phase for free resources and use different mechanisms to identify them. In this work, we do not overlook this process, and [PERSON]'s random walks are proposed as a new mechanism that performs complex searches in computational resource environments while attending to the different investigations in the design of parallel applications. As mentioned, in this research work the parallelization of the array method proposed in [12] is presented using hybrid programming OpenMP and MPI in a cluster of servers. The parallelization of the system has been divided into the parallelization of the scheduling phase and the parallelization of the allocation phase. The scheduling phase searches for the best assignment of the resources of the clusters to the tasks, with the use of the UMDA algorithm, and the parallelization of the allocation tasks to the resources phase, which uses a search for resources in the different clusters of the HPC system using Levy random walks, also mentioned in this work as Levy flights.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 2 Materials and Methods
### Koch Fractal Antenna Patch Development  
The proposed Koch semi-circular patch has evolved through the novel concept of the binary addition of a circular patch (evaluated at a design frequency of 3.5 GHz) and a Koch fractal with two iterations. The effective radius of the circular patch (\(\text{a}_{\text{e}}\)) is evaluated as 11.98 mm using Equations (4)-(7) [36; 52].  
Circular patch antenna radius:  
\[\text{a}_{\text{e}}=\frac{\text{F}}{\left\{1+\frac{2\text{h}}{\pi\epsilon_{ \text{r}}\text{F}}\left[\text{ln}\left(\frac{\pi\text{F}}{2\text{h}}\right)+1. 7726\right]\right\}^{\frac{1}{2}}} \tag{4}\]  
where h is given in cm, and  
\[\text{F}=\frac{8.791\times 10^{9}}{f_{r}\sqrt{\epsilon_{r}}} \tag{5}\]  
Feed width:  
\[\text{W}_{\text{F}}=\frac{7.48\text{h}}{\text{e}^{Z_{0}}\frac{\text{d}\text{ e}+1}{8\text{f}}} \tag{6}\]  
Initial substrate dimensions:  
\[\text{W}_{\text{S}}\times\text{L}_{\text{S}}=4\text{a}_{\text{e}}\times 4 \text{a}_{\text{e}} \tag{7}\]  
The binary operation of the circular patch is used to develop the iteration-0 Koch operation, as illustrated in Figure 2. The development of the two-stage Koch fractal and the patch of the proposed antenna are represented in Figure 3a-c. The basic concept of the Koch fractal, as explained in Section 2.1, has been applied to develop the shape of the antenna patch. In iteration 0, the Koch length is 16.94 mm, i.e., \(\text{K}/2^{\text{N}}\), where N = 0 and 2 is the Koch fractal factor (chosen instead of 3, as mentioned in Section 2.1). The length of the iteration-0 line is equal to the side length of the circle of radius 11.98 mm with four segments. The K\({}_{0}\) is measured at 16.94 mm, as displayed in Figure 3a. In iteration 1, the length of the Koch triangle's base cut is evaluated using \(\text{K}_{1}=\text{K}_{0}/2\), i.e., half of the total length of iteration 0-8.47 mm. A circle with four-line segments of this diameter (8.47 mm) is subtracted from iteration 0. It generates the iteration-1 patch, as displayed in Figure 3b. In iteration 2, the same process is repeated. In this step, the diameter of the Koch fractal is kept at just half of that of iteration 1 with four-line segments, and it is subtracted from iteration 1. This gives rise to the novel proposed patch shape, as displayed in Figure 3c.  
Figure 1: Koch fractal principle.
## 4 Application to Power Electronics
### The Optimization Procedure  
The optimization challenge is approached through the application of two distinct algorithms, as follows:  
* The SQP algorithm, derived from the SciPy package, employs a <<multi-start>> strategy. This algorithm is executed ten times, each time initiated from randomly selected starting points, and the optimal solution that most effectively minimizes the objective function is subsequently selected. Thus, the robustness of the SQP will be improved;
* The stochastic differential evolution algorithm, also derived from the SciPy package, operates with a population of 100 individuals and is constrained in regard to a maximum of 500 generations. Given its stochastic nature, this algorithm is executed thrice to ensure that the optimal solution remains consistent across all the iterations.  
To evaluate the efficacy of the gradient computation, the final outcome from the initial optimization (SQP with <<multi-start>>) is further refined using SQP, albeit with gradients calculated via the finite difference method.  
The optimization process is conducted on a computational server running Ubuntu 20.04, equipped with an Intel XEON Silver 4210R processor, featuring 10 cores (2.4 GHz) and 32 GB of RAM.