esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
PIN diodes consume a relatively low amount of power when switching states, especially in comparison to continuously active tuning components like Varactor, which makes them suitable for battery-operated devices, such as IoT sensors and mobile communication systems. Lately, various studies have been reported in the literature where frequency reconfigurability has been achieved using PIN diodes [21; 22; 26; 32; 33; 34] for multiple wireless communication applications. In [21], a Substrate-Integrated Waveguide (SIW)-based antenna supporting S-band and C-band applications is proposed, for which frequency reconfigurability is achieved by using several PIN diodes. Although reconfigurability for two modes of operation is achieved for this design, use of several PIN diodes as well as the SIW increases the complexity of this design. The authors in [22] reported a frequency-reconfigurable monopole antenna for 5G Sub-6-GHz, and WLAN applications. Four different modes were achieved by employing three PIN diodes. Another frequency-reconfigurable monopole antenna operating in the microwave frequency band is presented in [26]. This design involves four PIN diodes to attain five modes of operation. Although this design succeeds in achieving five modes, the geometrical dimensions of this structure are comparatively large. An inverted F-shaped antenna with frequency-reconfigurable characteristics is proposed in [32]. Frequency reconfigurability is achieved by using two PIN diodes. This antenna resonates between 0.841 GHz and 2.12 GHz while covering six different frequency bands. In addition, the authors in [33] proposed a multilayered antenna structure for S-band and C-band wireless communication applications, where frequency reconfiguration was attained by incorporating four PIN diodes. Likewise, in [34], a frequency-reconfigurable antenna for WiMAX and Wi-Fi applications is presented. For this design, frequency reconfigurability is achieved through a PIN diode and allows switching between two different frequency bands. This antenna exhibits comparatively larger dimensions. Frequency-reconfigurable antennas are a cornerstone of modern wireless communication, enabling efficient, flexible, and adaptive connectivity. Therefore, this article proposes a compact-sized antenna with multiband operation for IoT applications. The frequency-reconfiguration capability is attained by using two diodes, where four distinct operational modes are achieved. Serpentine-shaped stubs are introduced to achieve compactness. The proposed antenna supports C-band, 5G-sub-6-GHz, Wi-Max, ISM-band, GSM, and LTE applications, as depicted in Figure 1.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 2 Related Work
### Skeleton-Based Action Recognition Using Transformer  
The Transformer, originally proposed by [PERSON] et al. [28], employs a multi-head self-attention mechanism to replace conventional recurrent structures for sequential data modeling. Due to its capability of parallelizing computation along the sequence dimension, the Transformer greatly enhances both training and inference efficiency. Additionally, its self-attention mechanism captures global dependencies across different temporal or spatial positions, making it particularly suitable for extracting features from long sequential data.  
In skeleton-based action recognition, the introduction of Transformer architectures not only effectively captures long-term dependencies and global spatial relationships but also facilitates effective multi-scale feature extraction from skeleton data. Particularly for action sequences that are longer or structurally more complex, the multi-head self-attention mechanism of Transformers simultaneously attends to interactions across various time steps and crucial joints, significantly enhancing the representation and fusion of local and global information.  
Due to their powerful sequential modeling capabilities and computational parallelism, Transformers have progressively gained prominence in the domain of skeleton-based human action recognition. Transformer-based approaches, leveraging self-attention mechanisms, effectively capture global inter-node relationships [25]. For instance, HyperFormer employs relative position encoding based on shortest graph paths and a hypergraph self-attention mechanism, enabling the attention module to recognize high-order relationships shared among joint groups connected via hyperedges [15]. FG-STFormer combines local and global spatial transformers alongside a joint-part cross-attention mechanism, allowing the attention module to discern discriminative local relationships between joints and cooperative global spatial relationships connected through body parts, significantly improving spatiotemporal feature modeling capability [27].
# Wideband UHF Antenna for Partial Discharge Detection
## 2 Basic Loop Antenna  
As alluded previously, we will design a loop-shaped antenna suitable for PD detection in the conducting ground wire. Toward this purpose, we considered various shaped loop antennas, including circle, square, rectangle, rhombic, and elliptical loop antennas [14; 15; 16; 17; 18; 19]. Since we investigated the UHF antenna for PD detection from 500 -1500 MHz, various simple loop antennas were designed at 1 GHz, the center frequency of the operating band. In other words, the total circumference was approximately 300 mm, one wavelength at the frequency. In order to choose a proper loop-shaped antenna, we investigated the detection capabilities of various antennas mentioned above. The current source was injected into the conducting ground wire, and the coupling performance of the loop antennas was calculated by the ratio of the received voltage (at the antenna port) to the injected current (into the conducting ground wire). The distance between the injected current source and the plane of the antenna is \(d\), as shown in the simulation models in Figure 3. The voltages received at the loop antennas were calculated by integrating the electric field in the antenna port using the HFSS field calculator function [20] as follows:  
\[V=\int\mathbf{E}\cdot d\mathbf{l}\]  
where \(\mathbf{E}\) is the electric field in the antenna port and \(d\mathbf{l}\) indicates the differential displacement along a line in the antenna port. As shown in Figure 4, rectangle #1 yields the best performance.  
Figure 3: Simulation models of loop antennas: (**a**) top view and (**b**) side view.  
The spectral response of coupling performance for rectangle #1 was also investigated in Figure 5. As \(d\) increased, the coupling performance decreased, especially for shorter wavelengths. Note that the reduction of the received voltage was negligible as the distance increase since the distance difference was very small compared to the wavelength. The simulation results show that the rectangle #1 antenna could have a good ability to sense the near field, but the bandwidth of this basic loop antenna was narrow. In the next section, we focused on increasing the operating bandwidth of the antenna.  
Figure 4: Coupling performance of each antenna.  
Figure 5: Coupling performance by rectangle #1 versus frequency.
# Control-Flow Integrity: Attacks and Protections
## 3 Exploitation
### How to Subvert the Control Flow?
#### 3.1.1 Indirect Jump  
The destination address is not embedded in the opcode but read from a memory address or is the value of a processor register.  
This kind of instruction is commonly found in the supporting structures (PLT Procedure Linkage Table) used by the loader to bind the application with the system libraries. All applications that use dynamic libraries call the library services using indirect jumps. Listing 6 shows examples of indirect jump instructions. These instructions are also used to implement context switches in user space. For example, signal and setjmp/longjmp mechanisms.  
```
jmp QWORDPTR[rip+0x21c4 cc] jmp QWORDPTR[rax+0x10] jmp QWORDPTR[rdx]
```  
Listing 6: Indirect jump instructions.  
Indirect jumps are prone to abuse since the destination address can be manipulated by the attacker. For example, an indirect jump or call that reads the destination from a read only memory area cannot be exploited. In the previous example, [rip+0x21c4 cc] must point to a writable area in order to be exploitable.
## 4 Characterization of Bioadsorbents
BET permits before and after comparisons of any modifications, aiding understanding of ion integration in the adsorption process; for example, the reduction of the surface area by the element zirconium in a peanut husk adsorbent [82]. This method was chosen to characterize a banana peel adsorbent with a surface area of 13 m\({}^{2}\)/g, which indicates a small pore size [60]. AC derived from coconut buttons and rice straw biosorbents were characterized by BET; the former had a surface area of 479 m\({}^{2}\)/g, evidencing a wider pore size distribution and better adsorption capacity, whereas the rice straw presented a specific surface area of 1.95 m\({}^{2}\)/g and average pore size of 12.1 nm [16,37]. As can be seenin Tables 1 and 2, the majority of adsorbents and raw materials have been analyzed by BET and/or BJH, including _Acacia erioloba_ seeds, _Aegle marmelos_ shell, _Argania spinosa_ tree nut shells, cacao shells, siriguela seeds, peanut shell, black tea waste, spent and granular coffee, and papaya, orange, and pomelo peels. These have been additionally characterized using X-ray diffraction, thermogravimetric analysis, differential thermal analysis and thermogravimetry, SEM, field emission scanning electron microscopy, energy-dispersive X-ray, back-scattered laser light diffraction, Raman spectroscopy, electron spin resonance spectroscopy, small-angle X-ray scattering, and the Horvath-Kawazoe method. SEM is another common characterization technique, and allows identification of the morphology of the bioadsorbents before and after adsorption. It is useful for the simultaneous appreciation of the number and size of pores, as well as the verification of whether a fibrous structure is replaced by a smooth one [50]. An improvement on this method is ultra-high-resolution SEM, which is suitable for the visualization of the surface texture of spent coffee by scanning it with a high-energy beam of electrons with higher sensitivity [38; 99]. SEM is usually coupled with EDX, as in an analysis performed on mangosteen pericarp adsorbent where the changes in composition and anthocyanin quantity determined the removal efficiency of radioactive iodine [79]. [PERSON] et al. [16] characterized the surface area, pore volume, size, and chemical composition of rice straw with a particle size between 76-150 \(\upmu\)m using EDX before and after lead adsorption; EDX showed that the presence of organic elements such as Na, Ca, and Mg decreased after heavy metal adsorption, which promoted effective ion exchange and retention of the contaminant. Many other adsorbent materials, including cacao pod husk, palm tree leaves, rubber pod husk, banana peel, barley straw, cauliflower leaves, coffee husk, potato leaves and stem, rice husk, SCB, tamarind seeds, tea mixed waste, and watermelon rinds have been analyzed with SEM-EDX or only SEM (Tables 1 and 2).
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Types of Cyber-Physical Attacks
#### 3.2.3 Denial of Service Attacks  
Arguably one of the most common types of attacks, denial of service (DoS) attacks, are specifically aimed at crippling systems by denying access to any form of computational resources, effectively bringing the process that the system is in control of to a halt. For example, a server that controls industrial processes can be prevented from communicating with lower-level industrial control systems by denying these systems access to the server network [3]. There have even been cases where DoS attacks have been used as a decoy by cyber attackers to cover their tracks after an attack of another form has been carried out [33].  
Another form of DoS attacks, namely distributed denial of service (DDoS) attacks, makes use of multiple infected systems to distribute cyber-attacks on a larger scale. This is one particular concern regarding the security of next generation industrial control systems. Since many next generation industrial sensors and items of control systems equipment have the capability to connect directly to computer networks and the Internet, a DDoS attack in such an environment could have catastrophic consequences for production lines, where such equipment is critical for efficient and safe operations.
## 2 Preliminaries
### Load-Capacity and Allocation Mechanisms  
Sensor load refers to the total amount of data traffic collected and forwarded by a node per unit time, and the capacity represents the upper limit of load that a node can bear [39]. Hierarchical industrial wireless sensors usually consist of cluster head nodes and general nodes. The general node is responsible for collecting information in the covered area and aggregating the data to the cluster head node. The cluster head node not only handles the information collection within the cluster but also undertakes the task of forwarding data from other cluster head nodes. For general nodes and sink nodes, the capacity is always guaranteed to be larger than the load at the beginning of building the network. However, cluster head nodes need to transmit data in real-time, so their load is updated at each time. Therefore, it is vital to quantify the cluster head node's load and capacity. There are two methods that are often used to define the load on cluster head nodes. The first method is measured in degrees [23; 39]:  
\[L_{i}(0)=k_{i}^{\sigma}, \tag{1}\]  
Figure 1: Hierarchical topology of industrial wireless sensor networks.  
Figure 2: Cascading-failure process between cluster head nodes.  
where \(L_{i}(0)\) is the initial load of the node \(i\), \(\sigma\) is used to adjust the variability of the initial load, called the weight factor, and the number of edges connected to the specified node is used to describe the load of the initial node. However, this approach considers only the degree of the nodes. Some metrics describing the importance of the nodes are ignored.  
The second method is to use betweenness [28] to measure the load of the nodes:  
\[L_{i}(0)=\left(B_{i}/\frac{N(N-1)}{2}\right)^{\sigma}, \tag{2}\]  
where \(B_{i}\) is the betweenness of the node \(i\). Although this takes into account some necessary information about the network topology, it is not suitable for large-scale network architectures, because this approach requires a lot of computation.  
The capacity represents the upper limit of the load a node can carry. For cluster head nodes, the capacity is generally fixed and positively related to the initial load of the node [23; 39].  
\[C_{i}=(1+\lambda)L_{i}(0), \tag{3}\]  
where \(C_{i}\) represents the capacity of the node, and \(\lambda\) is the overload tolerance factor.  
When the network suffers from overload impact resulting in the cascading phenomenon, a common method of allocation mechanism is based on the static metrics [17; 25] (e.g., degrees, betweenness) of neighboring nodes. We call it static metrics load distribution (SMLD), which can be expressed as follows.  
\[\eta_{j}=\frac{k_{j}}{\sum_{n\in\Gamma_{i}}k_{n}}, \tag{4}\]  
where \(\eta_{j}\) denotes the load distribution rate from \(i\) to \(j;k_{j}\) is the degree of the node, and \(\Gamma_{i}\) collects the neighboring nodes.
## 4 Evaluation
### Experiments and Results
#### 4.4.2 Experiment: General Anomaly Detection  
The objective here was to evaluate our approach in detecting broader classes of attacks that were not seen before. For example, the NSL-KDD dataset can be broadly categorized into the following classes: DoS, Probe, R2L and U2R. Similarly, the CIC-IDS2017 dataset, when classified broadly, is made up of the following classes: DoS, port scan, heart bleed, Botnet, SSH-Patator, FTP-Patator, and web-based attacks  
To perform the experiment, we applied a similar logic as in experiment 1. For instance, to detect the probe class of attack in the NSL-KDD dataset, our meta-training set consisted of all other attack classes (DoS, R2L, and U2R). While the meta-testing set comprised the attack we wanted to classify, which was the probe attack in this case. We applied the same procedure as for the CIC-IDS2017 dataset.  
Figures 7 and 8 present the results of our experiments when the number of samples selected to train our classifier was limited 10 for both NSL-KDD and CIC-IDS2017, respectively.  
Figure 6: Detection rate (DR) comparison of various classification methods on DoS subclasses for CIC-IDS2017 datasets.  
For the NSL-KDD dataset, (Figure 7), our approach performed reasonably well, especially for DoS and probe classes. Our model achieved the highest DR score of 90% with the DoS class and 70% on probe class. However, overall, our model's performance was low compared to the results of experiment 1. Some attacks were poorly detected. For example, our model's DR scores was 66% for the R2L class, and it completely failed to detect the U2R attack class, scoring a DR of 0.0%.  
Similarly, the result of our experiment on CIC-IDS2017 (Figure 8) follows similar trend. There is drop in performance, our model performance was not as good as that  
Figure 8: Result of experiment 2: general anomaly detection for CIC-IDS2017 dataset.  
Figure 7: Result of experiment 2: general anomaly detection for NSL-KDD dataset.  
of experiment 1. The highest DR score was 94.1%, which was achieved for the DDoS attack class. Our model poorly detects the Patator attack classes, scoring 34.8% and 47.6%, respectively.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 2 Differential Evolution
### Control Parameters Adaptation of DE Algorithm  
Experimental results conducted by [PERSON] et al. [38] have shown that parameter adaptation schemes in JADE [40] is the most effective. As a result, its procedures are extended here to adapt the parameters of gravity inversion.  
In JADE, for each vector, the parameters \(F_{i}\) and \(CR_{i}\) are independently generated according to the successful \(F\) and \(CR\) values in last generation. The formula of generating \(CR_{i}\) can be expressed as follows:\[CR_{i}=randn_{i}(\mu_{CR},0.1) \tag{16}\]  
where \(randn_{i}(\mu_{CR},0.1)\) returns a random value with a gaussian distribution. 0.1 is standard deviation; \(\mu_{CR}\) is initialized to 0.5 and calculated using the following formula:  
\[\mu_{CR}=(1-c_{CR})\mu_{CR}+c_{CR}\ mean_{A}(S_{CR}) \tag{17}\]  
where \(c_{CR}\in(0,1)\) is a constant, \(S_{CR}\) is the set of all successful \(CR\) values in last generation; \(mean_{A}(\cdot)\) is the usual arithmetic mean.  
Similarly, the adaptation of \(F_{i}\) is as follows:  
\[\left\{\begin{array}{l}F_{i}=rand_{ci}(\mu_{F},0.1)\\ \mu_{F}=(1-c)\mu_{F}+c\ mean_{L}(S_{F})\\ mean_{L}=\frac{\sum_{F\in S_{F}}F^{2}}{\sum_{F\in S_{F}}F}\end{array}\right. \tag{18}\]  
where \(rand_{ci}(\mu_{F},0.1)\) means Cauchy distribution with location parameter \(\mu_{F}\) and scale parameter 0; \(mean_{L}(\cdot)\) is the Lehmer mean; and \(S_{F}\) is the set of successful scale factors.
###### Abstract  
The foundations of particular engineering structures, including marine and jetty structures, mooring systems for submerged platforms or those on the ocean surface, and transmission towers, are subjected to various external loads including compression, uplift, and lateral loads. In such cases, to improve the soil resistance below foundations, pile foundations such as helical piles, anchored piles, and batter piles are commonly preferred, depending on the in situ conditions. Helical piles, increasingly used as an alternative foundation to conventional piles, are placed in the soil body by rotating with torque. This paper deals with the contribution of a helical pile in improving loose sandy soil, and the main purpose is to study the effect of the helix-buried depth on the load-bearing capacity and failure mechanism. The investigated variables include the distance between helixes, the number of helixes, and the diameter of the upper helix. Physical model tests were conducted, and two- and three-dimensional numerical analyses were performed by using the finite element method with an advanced soil model to illustrate the failure mechanisms of helical piles. The aim was to reveal the efficiency of the finite element method in modelling helical piles placed in weak sandy soil. A simplified linear geometry for helixes was established in a two-dimensional finite element model whereas a real geometry for helixes, which was a more realistic approach, was created in a three-dimensional finite element model. The results show that the three-dimensional model indicates better agreement with the physical model compared to the two-dimensional model, and all investigated variables highly affect the load-bearing capacity of helical piles.  
bearing capacity; failure mechanism; finite element model; helical pile; physical model test Article
## 4 Results and Discussion  
### Mechanical Response and Deformation Micromechanisms  
The quasi-static in-plane mechanical response of the material was dominated by the rotation and realignment of fibres towards the loading direction; see Figure 2. The stiffness of the material was directly proportional to the fibre orientation distribution function (ODF) resulting in an equivalent increment of tangent stiffness with strain; see Figure 3a. The fabric also presented a pseudo-plastic response with irrecoverable deformations even at very low strains; see Figure 3b. A large scattering in mechanical properties such as nominal strength was registered due to the stochastic nature of the fibre network; however, similar stiffness and strength values were found for quasi-static strain rates ranging from 0.001 to 0.08 s\({}^{-1}\)[15]. The strain distribution was fairly homogeneous in the central region of the specimens before the maximum load. Afterwards, the failure of the material was triggered, inducing large strain gradients due to damage localisation; see Figure 4. The final failure of the material occurred due to fibre sliding and pull-out from the entanglement points, resulting in a high energy dissipation and a gradual reduction of load carrying capacity. The energy dissipation mechanisms at a smaller scale were quantitatively characterised by individual pull-out tests. A predominance of frictional fibre slippage was found, with a significant amount of dissipated internal energy during the loading  
Figure 1: Split-Hopkinson tensile bar experimental set up. (**a**) Schematic of the different components of the SHTB, (**b**) gripped specimen between bar ends and (**c**) aluminium and brass components of the grips.  
The same deformation mechanisms appeared during ballistic impact. A longitudinal tensile wave was propagated through the fabric, and the material within the wave front dissipated energy through in-plane deformation. As a result of the dissimilar fibre connectivity along material directions, different wave propagation velocities appeared generating an elliptical wave front for the longitudinal and transverse waves; see Figure 5a. The main deformation micromechanisms were characterised such as radial uncurling, rotation and sliding of the active fibres towards the impact point. The tensile wave was followed by a slower transverse wave forming a cone of deformed material, see dashed line in Figure 5a and the experimental deflection in Figure 5b. A higher deformation was localised at the impact point, resulting in a steep strain gradient. Local fabric tearing and fibre pull-out was registered at the impact point for velocities above the ballistic limit; see Figure 5c. A numerical model was developed to predict the ballistic performance of the layers. Although the model captured the ballistic limit and the failure mechanisms for different fibre ODFs, it overestimated the energy absorption above the penetration threshold as a result of inaccuracies when modelling the wave propagation phenomenon. Further details are available in [19; 21].