esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of SSEP  
As shown in Figure 1, when an electrically conductive part is being probed, the surface being probed is grounded while the electric potential of probing ball is set to be a constant such as 1 V, and then a spherical scattering electrical field is formed. As the gap between the probing ball and the surface decreases, the surface charge on the probing ball tends to concentrate in quite a small area around the probing point, which is the closest point on the spherical surface to the surface being probed. The concentration of surface charge on the probing ball becomes obvious when the probing gap \(\delta\) decreases to a certain level, which is often below the micrometer level. The spherical scattering electrical field and resulting surface charge distribution change drastically when probing gap \(\delta\) changes, so high-resolution displacement sensing can be achieved by charge signal detection. The SSEP features non-contact, 3D isotropic, and approximate point probing characteristics, and can be fitted in a micro/nano-coordinate measurement machine or an analogous instrument to measure the 3D dimensions and geometry of miniature internal structures with high aspect ratios [2]. This paper focuses on the quantitative investigation of the surface charge distribution characteristics on the probing ball as well as the resulting approximate point probing capacity of the SSEP.
## 3 Results and Discussion
### Calculated Core-Level Binding Energies, XP Spectra, and Work Function Changes
#### 3.3.2 Influence of Different Tail Groups on the XP Spectra  
This raises the question, what would happen if the SAM-forming molecules were substituted with tail groups bearing dipoles significantly different from the terminal C-H bond of a phenyl ring. According to Figure 1, this should (significantly) modify the work function shift, while leaving the core level binding energies essentially unchanged, with the exception of chemical shifts experienced by carbon atoms in or directly bonded to the substituents. To test that hypothesis, we simulated systems VII and VIII containing -CN and -NH\({}_{2}\) tail groups. As far as the simulated core level binding energies are concerned, we find them to be very similar for all three systems (I, VII, and VIII), as shown in Figure 5. Concerning the chemically shifted C1s binding energies of the uppermost carbon atoms, for system VII the shift of the top carbon atoms in the phenylene and the carbons in the -CN groups go in opposite directions. Thus, their impacts on the XP spectrum essentially cancel. As a consequence, the peak position of the C1s XP spectra of I and VII are virtually the same. Additionally, in VIII the shift of the peak remains comparably small (amounting to only 0.19 eV), as there are only the topmost carbon atoms in the two molecules per unit cell that are affected by the presence of the amine. This means that for all systems considered in the present study, the position of the peak in the C1s spectrum is a valid measure for the interface dipole.  
As shown in Figure 4, in sharp contrast to the core level spectra, the work function changes for VII and VIII differ significantly from that of I (by 3.63 eV for VII and by \(-\)1.30 eV for VIII). This is again fully consistent with the model described in Section 3.1.
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 3 Deterministic and Game-Theoretical Approaches to Denial-of-Service Attack Modeling
### Game-Theoretic and Optimization-Based Approaches for Attack Modeling
#### 3.2.2 SINR-Based Models
They are expressed as \(\frac{L\eta P}{\delta J+\sigma^{2}}\), where \(L\), \(\eta\), and \(\beta\) are fixed positive constants representing channel properties, \(\sigma^{2}>0\) is the background channel noise, and \(P\in[0,\infty)\) and \(J\in[0,\infty)\), respectively, denote the transmission power of the user and interference power of the attacker. The cost of transmission and the cost of jamming for one unit of power are given respectively by the positive scalars \(E\) and \(C\). Moreover, the utilities of the user and the jammer are, respectively, given by \[V_{\text{User}}(P,J)\triangleq\frac{L\eta P}{\beta J+\sigma^{2}}-EP,\quad V_{ \text{Jammer}}(P,J)\triangleq-\frac{L\eta P}{\beta J+\sigma^{2}}-CJ.\]  
In this formulation, the optimal strategy of the jamming attacker for a given transmission power \(P\) is denoted by \(J^{*}(P)\) and is obtained by finding \(J\in[0,\infty)\) that maximizes \(V_{\text{Jammer}}(P,J)\). Then, based on the jamming attacker's optimal strategy, the user's best strategy is obtained in [81] through solving the problem  
\[\underset{P\in[0,\infty)}{\text{maximize}}\quad V_{\text{User}}(P,J^{*}(P)).\]  
A Stackelberg game was also utilized by [PERSON] [82] to investigate optimal attack and defense strategies in a multi-channel estimation problem. In this problem, a number of sensors acquire measurements of a plant and attempt to transmit these measurements to a remote receiver over insecure wireless channels. Each channel may be subject to a jamming attack and the transmission failure probabilities are characterized through an SINR formulation. In [82], two cases with incomplete information are considered: (i) the defender knows the probability of a possible attack and the total jamming power used on the wireless channels when there is an attack; and (ii) the defender knows the probabilities of possible attacks on each channel with the corresponding power levels. In the context of multi-agent systems, [PERSON] et al. [75] explored a game-theoretical formulation, where a game between a jamming attacker and a defender is considered by extending the infrastructure network modeling approach in [83]. In this game, the jamming attacker's goal is to optimally choose communication links to attack, whereas the defender wants to recover those links. To this end, a new quantitative network connectivity notion is introduced in [75] to find optimal attack and defense strategies for scenarios where the attacker wants to decrease the connectivity by attacking certain communication links, whereas the defender wants to increase it by reestablishing the connection on the attacked links. In the problem, both players are assumed to have energy constraints that vary in time based on the previous actions of the players.
## 5 Literature Review by Domain Area
### Manufacturing and Industrial Processes  
AR in the industrial and manufacturing sectors integrates virtual data into the physical world to create an enhanced version of reality. This technology overlays digital information, such as images, 3D models, and data, onto a real-world environment in real time [81]. It allows workers to learn new procedures in a risk-free environment before applying these skills in the actual workspace. This kind of immersive training helps improve workers' understanding and retention of information, also, AR can provide technicians with real-time information and guidance during maintenance and repair tasks. By overlaying digital information onto the physical equipment, technicians can easily identify issues and understand the necessary steps to resolve them [82].  
Designers and engineers can use AR to visualize and manipulate 3D models of products in the real world. This helps in better understanding the design, enabling modifications before actual production [83]. Quality control is another important use that has been given to AR in this sector, in order to compare the manufactured product with the original design by overlaying the 3D model onto the product [84]. Following are some promising developments in this field:  
* **Tech Live Look:** Porsche uses an AR application called \"Tech Live Look\" that connects dealership technicians with remote experts. These experts can look at a car through the technician's AR glasses and provide real-time guidance and support, significantly reducing service resolution times [85].
* **Vision Picking:** DHL has implemented AR in their warehouses to assist workers with \"vision picking\". Workers wear smart glasses that display where items are located, the quickest route to them, and where they need to be placed on the cart. This results in increased efficiency and reduced errors [86].
* **Siemens:** Siemens uses AR to train their workers on Sematic Controller assembly. With AR, the company shows step-by-step instructions directly on their field of view, reducing errors and the time spent on assembling the controllers [87].
# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 3 Materials and Methods
### The Proposed Detection Model, SUD-YOLO  
This subsection provides a detailed explanation of the semi-supervised learning method applied to underwater object detection. Given the scarcity of labeled data available for training in current underwater object detection research, semi-supervised learning methods have become a research hotspot due to their ability to leverage a large amount of unlabeled data in combination with a small amount of labeled data to improve the model's generalization ability. This method can mine the latent features in unlabeled data, helping the model gain a deeper understanding of the underwater environment. The overall framework of this paper is shown in Figure 2, and the design follows the Mean Teacher semi-supervised learning method [16].  
Figure 1: Some examples from the DUO dataset.  
Figure 2: The semi-supervised learning framework for underwater object detection algorithm we propose.  
In this study, the dataset is divided into two parts: labeled data and unlabeled data. Different augmentation techniques are applied to the labeled and unlabeled data. For labeled data, only strong augmentation is applied. When the teacher network generates pseudo-labels using unlabeled data, a weak augmentation is first applied. During the training of the student network, both labeled and unlabeled data are used, with the unlabeled data undergoing strong augmentation consistent with the labeled data. To ensure the quality of the generated pseudo-labels, we introduce a multi-scale pseudo-label augmentation module during the pseudo-label generation phase. The structure of this module is shown in Figure 3.  
The detection model we proposed in this paper is shown in Figure 4. First, the Backbone component incorporates our proposed receptive-field attention and Local Feature Fusion Module (RFEConv) and Spatial Pyramid Pooling Fast Module (SPPF). Figure 5 illustrates the specific structure of the RFEConv module.  
Next, the Neck component utilizes a Feature Pyramid Network (FPN) structure to perform multi-scale feature fusion using the feature maps from the Backbone.  
Finally, the Head component introduces our proposed Task-Aligned Lightweight Detection Head Module (TAE). This object detection process adopts an anchor-free approach, eliminating the need for predefined anchor boxes, and is better suited to detect objects of  
Figure 4: The detection model for underwater object detection algorithm we propose.  
Figure 3: Multi-scale Pseudo-label Box Augmentation Module (MPA).  
varying scales and shapes. Figure 6 shows the structure of the TAE module. Upon receiving the feature maps from the Feature Pyramid Network (FPN), the detection head decomposes the task into two sub-tasks: classification and regression. The classification task aims to predict the category of the detected object, while the regression task is responsible for accurately predicting the position and dimensions of the bounding box enclosing the object.
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 4 Performance Evaluation
### Ratio of Necessary Chunks in a Buffermap  
Figures 10 and 11 show the ratios of necessary chunks in a buffermap while changing the number of peers and the number of chunks per buffermap. The ratio of necessary chunks in a buffermap indicates the ratio of actually needed chunks among available ones in a buffermap. Therefore, the ratio of necessary chunks in a buffermap and the jitter ratio show contrary results. Thus, the adaptive buffering scheme in all numbers of peers and chunks per buffermap has a relatively higher ratio of necessary chunks in a buffermap. The adaptive buffering scheme shows 24.3%, 23.5%, and 22.6% higher ratios of necessary chunks per buffermap than the conventional scheme as the number of peers  
Figure 8: Jitter ratio according to the number of peers (number of chunks per buffermap: 512).  
Figure 9: Jitter ratio according to the number of chunks per buffermap (number of peers: 1200).  
changes between 1200, 1800, and 2400. It also shows 24.6%, 24.3%, and 22.2% higher ratio as the buffermap size changes between 256, 512, and 1024 chunks.  
Consequently, our simulation results show that our proposed adaptive buffering scheme significantly improves performance in all our simulation settings by effectively adjusting the ratio of caching and prefetching areas in the buffer of each peer.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 3 VGRQ: The Proposed Routing Mechanism
### Phases of VGRQ
#### 3.2.3. Updating Mobile Sink Position  
In VGRQ, the sink is considered to have a random mobility pattern in the sensing field. Consequently, the sensor nodes need to possess knowledge about the sink's current position to ensure the transmission of their data in the appropriate direction, thereby facilitating its successful delivery to the sink. In the absence of this information, sensed information may lost or delivered with unacceptable delay. However, broadcasting the sink position information increases the communication activities in the sensor field. In the resource-constrained WSNs, this is not an optimal way. Thus, instead of providing and maintaining the location information on each sensor node, the mobile sink provides it to the CHs only as follows:  
_Step 1_: The sink node broadcasts _hello_ message.  
_Step 2_: Upon receiving _hello_ message, the CH replies with _hi_ message to the sink.  
Figure 3: Chain formation in VGRQ.  
_Step 3_: The sink sends its position information and sojourn time to the closest CH, then the CH shares this notification with neighboring CHs following the chain.  
_Step 4_: If the sink walks to a new location within the sector, then CHs belonging to that sector keep the location information with themselves and do not send it to other CHs belonging to other sectors, as shown in Figure 4a.  
_Step 5_: If the sink walks to a new sector, then CHs share the sink's updated location with all the CHs across the sectors, as shown in Figure 4b.  
In this way, the mobile sink's location is provided and shared among CHs in a selective way.
# Correction Strategy of Online Midcourse Guidance for High-Speed Gliding Target Interceptor
###### Abstract  
During the interception of a high-speed gliding vehicle (HGV), the predicted intercept point (PIP) is updated many times, so the interceptor must continuously adjust its trajectory. In this paper, we propose an online guidance correction algorithm for the interceptor, which can effectively meet the position constraints and energy management of the guidance process. Firstly, we simplify the interceptor's maneuver problem to a two-body orbital transfer problem. The Lambert orbit transfer method is used to enable the interceptor to maneuver towards the target's PIP. Secondly, by comparing the target's high probability existence area (THPEA) with the interceptor-interceptable area (IIA), the engine start threshold is set in stages to avoid frequent redundancy maneuvers caused by frequent engine starting during missile correction. This approach achieves energy management. Simulation results show that our midcourse guidance strategy is effective.  
high-speed gliding vehicle; online correction strategy; midcourse guidance
# Broadband Holography via Structured Black Silicon Nano-Antennas
## Material and Methods  
### Binary Computer Generated Hologram Algorithm  
The CGH was generated using the conventional ray-tracing approach, where each object point in the 2D array is treated as a source of spherical wave, modelled by:  
\[E_{o}=A_{o}\frac{exp(ikr)}{r} \tag{1}\]  
where \(A_{o}\) is the light wave amplitude (it can take values of either 1 or zero, since it is a binary hologram), \(exp(ikr)\) is the phase factor, k is the wave number, and \(r\) is the distance between the object point at the \(x,y\) plane, and \(x^{\prime},y^{\prime}\) parallel plane, given by:  
\[r=\sqrt{\left(x-x^{\prime}\right)^{2}+\left(y-y^{\prime}\right)^{2}+z^{2}} \tag{2}\]  
The \(z\)-axis was chosen to be perpendicular to the hologram, where the hologram was set at \(z=0\) and the object plane was set to \(z=-60\) cm. The reference wave was chosen to be a plane wave (as it can produce clear reconstruction images), described as:  
\[E_{R}=A_{R}exp(ik(x\alpha+y\beta+z\gamma)) \tag{3}\]  
The total amplitude is computed by summing up all the contributions of the object wave and spherical wave at each point,  
\[E_{R}=\sum_{p}\frac{A(p)exp(ikr_{p})}{r_{p}} \tag{4}\]  
where \(p\) is the index at each object point. The intensity of the total field is calculated as follows:  
\[I=E_{T}{E_{T}}^{*} \tag{5}\]  
At this stage, the intensity is a continuous representation of the interference pattern. In order to obtain a pattern suitable to be transferred onto the BSi material, binarization based on a certain threshold was applied to the intensity matrix. The CGH pattern was generated using a MATLAB platform, and the simulation parameters are set as follows: the wavelength is set to 532 nm (\(\lambda\) = 532 nm), hologram dimension of 3 cm \(\times\) 3 cm, and sampling distance of 10 \(\upmu\)m, which in turn leads to a pixel resolution of 3000 \(\times\) 3000 pixels. The reference wave (plane wave) was designed such that it illuminates the hologram plane at a perpendicular direction. Once the hologram matrix was calculated (as the sum of the object wave and the reference wave matrices), the hologram iwass binarized in such a way that the amplitude values across the interference pattern can only assume the values of 0 or 1.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 4 Location and Installation of the Measuring Devices
### Citizen Weather Observer Networks  
The web of both networks shows the location of their AWSs, registered by the users themselves, without confirmation or the provision of detailed information concerning the characteristics of the surroundings; however, it has been verified in situ that, as in \"u\", the AWSs are not exactly or really in the specific locations that have been registered. In any case, it has also been noted that neither of the two CWON, Nt (AWSs denominated as \"a\" to \"j\") or WU (AWSs from \"\(\alpha\)\" to \"t\") present a uniform distribution in Caceres (Figure 6). It could be said that the most probable placements, in the case of citizens, are balconies, terraces, or windows of the houses and, for buildings belonging to institutions, they could also be on the roof. In addition, it is not possible to know whether the users have taken into account the recommendations of the manufacturers concerning the installation of each AWS. Nt advises installing them in a place protected from the rain, direct sunlight, and other possible adverse weather conditions, as well as being far from sources of heat or cold. WU recommends a height of between 1.2 and 1.8 m above the ground, and a vertical distance from paved surfaces of at least 15 m. As for horizontal distances, at least four times the height of the nearest obstacle [96; 97]; furthermore, the sensor should be well ventilated and should never receive direct sunlight, in which case a radiation shield must be used.
## 4 Analysis of Power Loss in Topologies
### Buck Topology  
In Table 5, the switching semiconductors of the conventional TSBB converter are S\({}_{1}\)/D\({}_{1}\), and the conducting semiconductors are S\({}_{1}\)/D\({}_{1}\)/D\({}_{2}\). Thus, the switching loss \(P_{CON,SW}\) and conduction loss \(P_{CON,CD}\) can be expressed, respectively, as:  
\[P_{CON,SW}=P_{SW,S1}+P_{SW,D1} \tag{18}\]  
\[P_{CON,CD}=P_{CD,S1,ON}+P_{CD,D2,ON}+P_{CD,D1,OFF}+P_{CD,D2,OFF} \tag{19}\]  
The switching semiconductors of the proposed TSBB converter are S\({}_{1}\)/D\({}_{1}\)/D\({}_{2}\), and those of the conducting semiconductor are S\({}_{1}\)/D\({}_{1}\)/D\({}_{2}\). Therefore, the switching loss \(P_{PRO,SW}\) and conduction loss \(P_{PRO,CD}\) can be expressed as:  
\[P_{PRO,SW}=P_{SW,S1}\biggl{(}\frac{C_{oss,D2}}{C_{oss,S1}+C_{oss,D2}}V_{i} \biggr{)}+P_{SW,D1}(V_{i})+P_{SW,D2}\biggl{(}\frac{C_{oss,S1}}{C_{oss,S1}+C_{oss,D2}}V_{i}\biggr{)} \tag{20}\]  
\[P_{PRO,CD}=P_{CD,S1,ON}+P_{CD,D2,ON}+P_{CD,D1,OFF} \tag{21}\]  
The switching loss varies by the voltage stress; however, the internal voltage of semiconductors is \(V_{i}\) for both the conventional and proposed TSBB converters. Consequently,the switching losses of the semiconductors are the same. Therefore, the difference in power loss between the two converters can be expressed as:  
\[P_{CON}-P_{PRO}=P_{CD,D2,OFF}-P_{SW,D2}\bigg{(}\frac{C_{oss,S1}}{C_{oss,S1}+C_{oss, D2}}V_{i}\bigg{)} \tag{22}\]  
This difference can be determined by the conduction and switching losses in the switch-off section of D\({}_{2}\). Since the switch-off section is \((1-d)\cdot T_{SW}\), the lower the duty ratio, the higher the efficiency of the proposed TSBB converter.
## 4 Applications in Optimization
### Self-Organizing Migrating Algorithm (SOMA)
#### 4.3.2 Performance Comparison of PID Controller Using DE and SOMA Versus PSO
The steps of the ALO for single-objective optimization problems are briefly described in the following pseudo-code [88]:  
* 1: _Define the objective function and population size (NP)_
* 2: _Generate the populations of ant lions and ants_
* 3: _Evaluate the fitness of the ant lions_
* 4: _Initialize the generation counter_ \(t=1\)
* 5: _For_ \(t<\) _maximum of iterations_:
* 6: _For each ant from_ \(1\) _to_ \(NP\)_:
* 7: _Perform a random walk around a random ant lion,_
* 8: _Perform a random walk around the elite individual (best ant lion),_
* 9: _Update the position and check the search boundaries._
* 10: _Evaluate the fitness of the ants_
* 11: _End for,_
* 12: _Update ant lion positions based on the ants_
* 13: _Update the elite individual and keep it in the population_
* 14: _Update the generation counter_ \(t=t+1\)
* 15: _End while,_
* 16: _Return the fittest (elite) ant lion._  
On the other hand, multi-objective optimization refers to the optimization of more than one objective; these are generally in conflict with each other, resulting in no single solution to the problem. For this kind of optimization problems, the aim becomes to find good \"trade-off\" solutions that represent the best possible compromises among the objectives. The tradeoff between obtaining a well-converged and well-distributed set of Pareto-optimal solutions is an important issue in multi-objective optimization. The MOALO is a classical multi-objective variant that uses the crowding distance [89] and the dominance concept for selecting the elite (best ant lion) and a tournament mechanism to select the ant lions to perform the random walk. One of the main pillars of optimization meta-heuristics, such as ALO, is their reliance on randomness or stochasticity, which is used to spark a path towards a desired goal. The current norm is the use of pseudo-random number generators. Unlike the MOALO, the proposed L-MOALO employs a mechanism with a chaotic dynamical behavior based on the Lozi map (similar to (30)) instead of pseudo-random number generators to enhance exploration during the ants' movement. The results of [88] obtained from simulations have shown that the MOALO and L-MOALO algorithms can be successfully applied to a Transformer Design Optimization (TDO) problem with two competing objectives, both providing a set of different solutions that present a trade-off between the objective costs.