esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Image Acquisition and Pattern Dataset Creation  
In this study, the image acquisition system was composed of a dome light source, a 1.3 megapixel color industrial camera, an LCD backlight and a servo motor module (see Figure 2). A vertically installed industrial camera with a camera lens overlooked the fabric. The dome light source that illuminated the fabric surface uniformly wasplaced in front of the fabric. In order to sample multiple parts of the piece, the system contained a servo mobile module that could move industrial cameras in a flat range. Figure 3 shows the six types of pattern images \(F_{i}\), \(F_{r}\), \(F_{c}\), \(F_{s1}\), \(F_{s2}\) and \(F_{s3}\) (the size was \(1024\times 1280\) pixels) acquired by the image acquisition system, where: \(F_{i}\) is the pattern with irregular shape. \(F_{r}\) is the pattern with circular shape. \(F_{w}\) is the pattern with the wavy shape. \(F_{s1}\), \(F_{s2}\), \(F_{s3}\) are patterns with a strip shape. The acquired images were transmitted to the data processing system (seeFigure 2) to be processed by the Faster R-CNN based algorithm. The image data processing is shown in Section 2.3.  
In this paper, 5000 unique images with size of \(640\times 512\times 3\) pixels were contained in the pattern dataset, and each image contained approximately 20 to 50 patterns. These images were cropped from approximately 400 original images with size of \(1280\times 1024\times 3\) pixels. To obtain the best training effect, each image was labeled in detail. The key details for labeling each image were as follows:  
* In order to avoid image over-fitting problems, each image contained 20-50 patterns, which were a critical metric for detection and recognition.
* The bounding box completely enclosed the pattern and kept the center coordinates of the pattern the same as the center coordinates of the bounding box.  
Figure 3: The sample images of different kinds of pattern piece. (**a**) irregular-shaped pattern \(F_{i}\), (**b**) circular-shape pattern \(F_{r}\), (**c**) wavy-shaped pattern \(F_{c}\), (**d**) strip-shaped pattern \(F_{s1}\), (**e**) strip-shaped pattern \(F_{s2}\), (**f**) strip-shaped pattern \(F_{s3}\).  
Figure 2: Image processing system. The image acquisition system on the left was used to acquire the partial texture image of the pattern, and the acquired images were three-channel RGB images and the size was \(1024\times 1280\) pixels. The acquired images were transmitted to the data processing system on the right for data processing.  
* There are no overlapping regions between the bounding boxes and the dimensions of each bounding box remain the constant.
###### Abstract  
The main challenge of precise point positioning (PPP) applications is the long convergence time of typically a half hour, or even more, to achieve centimeter accuracy. Even when the multi-constellation is involved and ambiguity resolution is implemented, it still requires about ten minutes. It is becoming a hot spot to incorporate the low Earth orbit (LEO) satellite constellation for enhancing the Global Navigation Satellite System (GNSS), named here as LEO-enhanced GNSS (LeGNSS). In this system, the LEO satellites cannot only receive GNSS signals, but also serve as GNSS satellites by transmitting similar navigation signals to the ground users, but with higher signal strength and much faster geometric change due to their low altitude. As a result, the convergence time of PPP is expected to be shortened to a few minutes, or even seconds. Simulation software is developed to simulate GNSS and LEO observations for ground stations taking into account tropospheric delay, satellite clock errors, observation noises, as well as other error sources. Then the number of visible satellites, the geometry dilution of precision (GDOP), and the convergence time of the kinematic mode of PPP are evaluated on a global scale compared to those of GNSS systems. The simulation results show that LeGNSS can decrease the PPP convergence to 5 min. If there are more LEO satellites included in the LeGNSS, it is expected that the initialization of PPP can be further shortened.  
precise point positioning (PPP); LEO enhanced Global Navigation Satellite System (LeGNSS); dilution of precision (GDOP); convergence time +
Footnote †: journal: Remote Sensing, 10, 984; 10.3390/rs10070984
## Appendix A Calculation of the Coverage Angle \(\beta\)  
To calculate the coverage angle \(\beta\), we need to obtain coordinates \(x_{1}\) and \(y_{1}\) of the intersection point \(A\) first, as shown in Figure 11. The circle is denoted by the equation  
\[x^{2}+y^{2}=R_{i
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 2 Channel Model
### Birth-Death Process
The expected number of new clusters generated by Markov processes during the period [\(t\), \(t+\Delta t\)] is  
\[E\{new(\Delta t)\}=\frac{\lambda_{G}}{\lambda_{R}}(1-e^{-\lambda_{R}\frac{ \delta_{P}(t,\Delta t)}{D_{c}}}). \tag{17}\]We use scenario movement \(\delta_{P}(t,\Delta t)\) to describe the correlation between two clusters. When the \(\delta_{P}(t,\Delta t)\) value is high, the correlation between the properties of the existing clusters at \(t\) and the newly generated clusters at \(t+\Delta t\) decreases. Figure 2 shows the image of the multipath components (MPCs) of the birth-death process, which assumes \(\lambda_{G}=0.13/\)m and \(\lambda_{R}=0.03/\)m.  
Since both SBS and TMS are moving, the distance between clusters is time-varying, during the generation process of the new cluster, the time delay can be approximated as  
\[\tau_{l}=\tau_{LoS(t)}, \tag{18}\]  
where \(\tau_{LoS(t)}\) represents the time-varying delay of the LoS path, and can be expressed as  
\[\tau_{LoS(t)}=D_{LoS(t)}/c, \tag{19}\]  
\[\delta_{\text{SBS}}(t,\Delta t)=\int_{t}^{t+\Delta t}v_{\text{SBS}}(t)dt, \tag{11}\]0  
Indeed, at the beginning, some scatterers are generated at the TMS end. Specific clusters may be removed from the CIR as time progresses, while new clusters may emerge. Additionally, the parameters associated with the surviving clusters changes within a time interval (\(\Delta t\)) due to the movement of SBS and TMS. To capture this dynamic evolution of the surviving clusters, it is necessary to incorporate an appropriate model that accounts for the changes in cluster parameters over time (\(\Delta t\)). This evolution process allows for adapting cluster characteristics in response to the movement and varying conditions in the wireless channel. By integrating these dynamics into the model, a more accurate representation of the CIR and their correlation over time can be achieved. \[\delta_{\text{SBS}}(t,\Delta t)=\int_{t}^{t+\Delta t}v_{\text{SBS}}(t)dt, \tag{11}\]1 An example of the MPCs evolution process in the time domain (\(\lambda_{G}=0.13/\)m, \(\lambda_{R}=0.03/\)m).
# Optical Lensless-Camera Communications Aided by Neural Network
## 2 Proposed Scheme
### Adaptive Training Sequence Adjustment Mechanism
#### 2.2.1 Frame Structure  
In our proposed system, before communications, communication parties establish connection through exchanging control frames. The structure of the control frame is shown in Figure 4. It includes \"SFD\" (full name is shown in Table 2), which separates the starting bits from the remaining information bits. The field \"Type\" differentiates control frame and data frame. The field \"Subtype\" consists of four bits, which indicates the types of frame. The \"Duration\" field of the control frame indicates the time between the end of the current control frame and the completion of data reception. Here, the \"Duration\" field is reserved for occasions in which multiple users have simultaneous communications in the future. The field \"Length of Training Sequence\"(LTS) is used by the terminal device to notify the AP the length of the training sequence in the next cycle. The LTS is \"000000\" if the length does not need change or if this confirmation frame comes from the AP. The field \"Frame Check Sequence\" (FCS) is used to verify whether the received data is correct.  
As shown in Figure 5, the field of Training sequence of data frame consists of alternating \"1\" and \"0\" bit, which makes the number of two types of images in the training set approximately the same.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Environment** & **Specification** \\ \hline The size of input image & \(48\times 48\) [Pixel] \\ Number of input layer nodes & 2304 \\ Number of hidden layer nodes & 48 \\ Number of output layer nodes & 2 \\ Initial learning rate & 0.01 \\ Momentum & 0.9 \\ Optimization method & gradient descent (SGD) \\ Transfer Function of hidden layer & Logsig \\ Transfer Function of output layer & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters.  
Figure 4: Structure of control frame.  
Figure 5: Structure of data frame.
## 3 A Corpus for Routing Evaluation in OppNets
### Scenario Characterization  
Characterizing a scenario basically consists of defining the characteristics that describe their network structure and behavior entirely. This study identifies two types of characteristics, namely direct and indirect. Direct characteristics are the ones that can be identified or defined directly, for example, by counting the number of nodes or measuring the speed of the nodes. On the other hand, indirect characteristics represent characteristics that can not be directly configured but, instead, can be estimated. For example, nodes' centrality refers to betweenness centrality in a trace and cannot be configured with state-of-the-art tools.  
A trace could be considered a scenario after the trace has been characterized, that is, when the contact or position trace has a vector describing its network structure and behavior. The characteristics had been identified from the literature review of OppNets routing algorithms. Table 1 shows the list of seventeen characteristics that has been used to characterize the network behavior of a contact trace. Seven characteristics are direct (D) and the rest are indirect (I). The number of nodes, node speed, studied area, movement pattern, node centrality, node contact time, and total encounters are among the direct and indirect characteristics.  
The measurement of the characteristics only concerns indirect characteristics. The measurement of those indirect characteristics listed in Table 1 follows the directives depicted in Table 2 and the next paragraph.  
In Table 2, the number assigned to the characteristic corresponds to the number defined in Table 1. With the exception of characteristic number seventeen (total encounters) the characteristics shown in Table 2 are calculated in a two-step process. The first step is to calculate the characteristic individually in each node. Then, as a second and final step, the mean, variance and standard deviation values of the characteristics are calculated within the values of all or some nodes included in the scenario. For characteristic seventeen, the second step is the sum of the individual values of all nodes. The particular considerations are listed as follows:  
* Centrality, inter-contact time, contact time, contact node ratio and encounters: mean of the individual measures of all nodes.
* Popularity and sociability: mean of highest ten percent measurements.
* Contact time per minute, window betweenness centrality: mean of metrics within a period.
* Total encounters: accumulative measurement.  
The following section introduces the concept of a corpus in the context of the OppNets. The concept of a corpus will be used across this article as the principle for standardizing the evaluation and comparison of OppNet routing algorithms.
# Encoding the Intensity and Phase Gradient of Light Beams with Arbitrary Shapes
## 2 Materials and Methods
### Experimental Setup  
The experimental setup is depicted in Figure 2. We use a solid-state laser (Opus 532, from Laser Quantum, Stockport, UK) with a wavelength of 532 nm, we can operate it at low power for interference experiments and at high power (up to 2 W) in case of optical trapping. A reflective SLM (LC-R 2500, from Holoeye Photonics AG, Berlin, Germany) is used with a resolution of 1024 \(\times\) 768 pixels (19 \(\upmu\)m pixel pitch) and a phase modulation of 2\(\upmu\).  
Figure 1: Algorithm for the estimation of parametric equations of a closed curve.  
In Figure 2, a laser beam that has been expanded and collimated through lenses L1 and L2, is incident upon the entire active area of the spatial light modulator. After reflection a pair of selected shapes were encoded side-by-side over the active area of the SLM. Therefore, a pair of beams engineered in intensity and in phase, propagate and interfere at a distance, where a charge-coupled device camera is located. Considering that the SLM in use has an efficiency of 80% and that a pair of beams are reflected, each beam represents, at most, 40% of the power emitted from the laser. Implementing a configuration where the complete active area is used for displaying one phase pattern, improves the conversion efficiency to 80%.  
When only one side of the SLM is in use, the camera can capture the intensity of the arbitrary shape previously designed. When both sides of the SLM are in use, interference can be achieved, and an analysis of the resulting interferogram would indicate the phase of the superposed light beams. An example of the interface executed on the computer and the corresponding phase images displayed on the SLM is presented in Figure 3.  
The user interface from Figure 3a allows to select a focal length and a shift in the horizontal and vertical directions for a given shape, showing the selected values on the blue rectangles at the bottom. A topological charge is also selected for each shape and the corresponding phase is displayed on the SLM, as seen in Figure 3b.  
Specifically, for demonstration of the phase gradient, a vortex beam can be encoded as a ring shape with our algorithm, allowing the generation of a pair of vortices that interfere, where the resulting interferogram could clearly demonstrate the topological charges interacting, as described in [23]. In the following section we present eight arbitrary shapes used as input for our algorithm and demonstrate their intensity and phase under this experimental setup.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 1 Introduction
### Research Contributions  
It is critical that memory, energy, communication, and computation complexities be kept at a minimum in smart home sensors and switches. Smart home networks comprise devices that are resource-limited in terms of memory, energy, and computation power. Such devices include smart switches and sensors; hence, any ideal authentication and key agreement (AKA) protocol must be lightweight. In addition, the massive data items exchanged over smart home networks are private andmay potentially reveal people's lifestyles if captured by adversaries. It is, therefore, important that data be protected while in transit between the remote users and the ISDs. In this regard, the following contributions are acclaimed in this paper:  
* Lightweight cryptographic primitives are deployed in the proposed protocol so as to render it efficient for resource-limited indoor smart home sensors and switches.
* Session-specific parameters are incorporated in the developed protocol to offer dynamism to the session state parameters. This eventually works towards the elimination of packet replay attacks.
* Extensive formal verification of the proposed protocol is executed using the most common Burrows Abadi-Needham (BAN) logic, which shows the establishment of a message protection session key among the communicating parties.
* Informal security analysis is carried out, which demonstrates the resilience of the proposed protocol against packet replays, impersonation, eavesdropping, Denial of Service (DoS), session hijacking, and man-in-the-middle attacks.
* Comparative performance evaluation is executed, which shows that the proposed protocol offers enhanced security and privacy protection at lower energy, communication, and computation complexities.  
The rest of this paper is structured as follows: Section 2 presents the related work, while the proposed scheme is discussed in Section 3. Similarly, Section 4 presents the security analysis of this protocol, while Section 5 discusses its performance evaluation. Finally, Section 6 concludes the paper and gives future research directions.
## 1 Introduction
However, the optimization ability of PSO, DE, and CS algorithms is mainly based on a random search, which cannot ensure convergence toward the optimal solution. Moreover, because of the multi-orientation and multiscale nature of the filter bank, there is redundant information in the process of texture feature extraction, and it contributes little to the classification [22]. Thus, numerous feature selection approaches based on Gabor filter have been proposed to reduce the data dimension [23; 24], but the previous works have ignored the relevance of parameter optimization and feature selection. In essence, parameter optimization and feature selection are both considered combinatorial optimization problems, and the integrated optimization of the parameters and features of Gabor filter could be obtained by swarm intelligence algorithms at the same time. Ant lion optimizer (ALO) [25] is a novel swarm intelligence algorithm. Nowadays, ALO has been applied in various fields, such as power system design, fault detection, schedule planning, path searching and so on [26; 27; 28; 29]. The optimization ability of ALO does not rely on any parameters, and it is more likely to obtain the satisfactory solution. However, feature selection is considered a discrete optimization problem and is difficult to solve using ALO with decimal coding. [PERSON] [30] presented a binary coded ALO (BALO) to select the optimal feature subset for some well-known datasets from the UCI repository. BALO obtained superior results by searching for the best feature subset, and the performance was independent of the step-length and classifier. Hence, in this paper, a new texture classification method is proposed that blends the use of Gabor filter and a hybrid binary-decimal coded ALO (HALO) to, respectively, solve the problems of parameter optimization and feature selection. The rest of this paper is structured as follow. The basic principle of HALO is illustrated in Section 2. In Section 3, the proposed method to obtain the integrated optimization of the parameters and features of Gabor filter is detailed. Section 4 displays the experimental results and discussion. Finally, the paper is concluded in Section 5.
# Super-Resolution Multicomponent Joint-Interferometric Fabry-Perot-Based Technique
In this paper, we introduce this with a Michelson interferometer, which can be similarly applied to other two-beam interferometers. The principle is based on the processing of the spectral spectrum; so, theoretically, it can also be applied to dispersive imaging spectrometers, with the caveat that the spectrum needs to be Fourier-transformed first. In this paper, the principle of MJJ-HI and its feasibility are demonstrated via simulation, and the effects of the FPI reflectance and interval on super-resolution spectra are discussed and compared with the super-resolution method of resampling. Compared to a single dual-beam interferometer, our proposed MJI-HI technique can achieve a twofold higher spectral resolution, while being simple, compact, and easy to set up. In addition, it has better stability without increasing the interferometric optical path, hence affecting the luminous flux less. The reflectance and interval of the FPI are the most important indicators in this study. The interval determines the resolution of super-resolution spectra, while the reflectance mainly affects the accuracy of super-resolution spectra, which have been chosen in the range of about 20% to 80%. Moreover, compared to the resampling method, our proposed technology has higher system stability, 7-35 times more-efficient data usage at the same spectral resolution, lower requirements for the FPI index, lower difficulty in spectral restoration, and is more suitable for the spectral super-resolution of interferometric spectrometers. The remainder of this paper is organized as follows: Section 2 introduces the basic principle of the MJI-HI technique. Section 3 describes the inverse calculation method of the spectrum, and Section 4 verifies the feasibility through simulation experiments, discusses the effect of the FPI's reflectivity and interval, and presents a comparative analysis with the same type of study.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 3 Background
### Definition  
Consider a given complex network \(G=(V,E)\), where \(V\) represents the set of vertices and \(E\) represents the set of edges. Furthermore, denote the number of vertices and edges as \(|V|\) and \(|E|\). The node degree and clustering coefficient can be used to describe the degree of connection between nodes. The Jaccard coefficient can measure the number of common neighbors between two nodes. By formalizing these basic definitions, the LBIA algorithm can be explained more concisely and accurately.  
**Definition 1** (Node Degree [18]): _The degree of a node is the number of edges connected to it. It serves as a important metric for describing network connectivity. It is often used to measure the centrality and importance of a node within the network; where \(d\) represents the degree of a node in the undirected network \(G\) and is shown in Equation (1):_  
\[d_{x}=\sum_{j=1}^{N}a_{xj} \tag{1}\]  
**Definition 2** (**Clustering Coefficient [28]): _The clustering coefficient mainly determines whether a node is tightly connected or not by the ratio of the number of connecting edges that actually exist between the neighbors of the node to the number that theoretically exist. It is shown in Equation (2):_  
\[C_{x}=\frac{2*m_{x}}{d_{x}*(d_{x}-1)} \tag{2}\]  
**Definition 3** (**Jaccard Coefficient [29]): _In a graph, the Jaccard coefficient is used to describe the number of common neighbors between two nodes, the larger the coefficient, the more connected the nodes are. It is shown in Equation (3):_  
\[J_{xy}=\frac{|\phi_{x}\cap\phi_{y}|}{|\phi_{x}\cup\phi_{y}|} \tag{3}\]  
_where \(\phi_{x}\) represents the first-order neighbor set of node \(x\) and \(\phi_{y}\) represents the first-order neighbor set of node \(y\). Here, we add the node itself to the set of neighbors._