esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 3 Background  
### Reliable Network Reconstruction  
To improve the reliability of network data reconstructions, advanced filtering algorithms can be employed to reduce noise, while anomaly detection techniques can help in identifying erroneous data patterns. However, challenges arise when statistical analyses, like packet sniffing in [22], yield data with compromised timeliness, making them unsuitable for many research applications [25]. True reconstruction of mutable network entities remains a demanding task [26]. Efforts like [27] grappled with these issues but depended heavily on extensive training data, which might not always be available. Thus, in our pursuit of MO-1 and MO-2, we utilize _nmap_[28] as a more efficient alternative that bypasses the need for vast training datasets. Nmap is an open-source tool widely used for high-speed network scans using raw IP packets to determine running service applications, operating system (OS) versions, and type of packet filter/firewall network device.
## 2 Equations of Motion
### Natural Frequency Relationships in a Linearized System  
The equations of motion (9) are nonlinear, and include viscous damping and sliding friction. To understand the characteristics of this nonlinear motion, assuming that the displacements are small, the undamped linearized equations of motion can be obtained:  
\[\begin{array}{l}\ddot{x}+(2\pi f_{y})^{2}\Big{(}\frac{y_{st}}{R}\frac{1}{(1 -\alpha^{2})\sqrt{1-\alpha^{2}}}+\frac{\alpha^{2}}{1-\alpha^{2}}\Big{)}x\ =\ 0\\ \ddot{y}+(2\pi f_{y})^{2}y=0,\end{array} \tag{12}\]  
where \(\alpha=a/R\). In this result, the natural frequency of the horizontal motion \(f_{x}\) is expressed as:  
\[f_{x}=f_{y}\sqrt{\frac{y_{st}}{R}\frac{1}{(1-\alpha^{2})\sqrt{1-\alpha^{2}}}+ \frac{\alpha^{2}}{1-\alpha^{2}}}=f_{y}\sqrt{\frac{g}{(2\pi f_{y})^{2}R}\frac{ 1}{(1-\alpha^{2})\sqrt{1-\alpha^{2}}}+\frac{\alpha^{2}}{1-\alpha^{2}}}. \tag{13}\]  
The relationship between \(f_{x}\) and \(f_{y}\) is determined by the radius of the concave surface \(R\) and installation distance ratio \(\alpha\) of the spring-damper, as indicated in Equation (13).  
In order to numerically investigate the natural frequency variation in the \(x\) direction due to the influence of the design parameters, it was analyzed for \(R\) = 0.6 m and \(f_{y}\) = 5 Hz and is shown in Figure 5. As illustrated in Figure 5a, the change in \(f_{x}\) is insignificant in the area where \(f_{y}\) is small, but when \(f_{y}\) is large, \(f_{x}\) increases rapidly, and it changes more steeply as \(\alpha\) increases. It can be observed that, when \(\alpha\) is large, the change in \(f_{x}\) with respect to \(f_{y}\) is large, resulting in the same effect as the small curvature radius \(R\). That is, a larger installation distance ratio (\(\alpha\)) results in greater stiffness in the \(x\) direction.  
Figure 5: Natural frequency of \(x\) direction, \(f_{x}\): (**a**) \(f_{y}\) when \(R\) = 0.6 m. (**b**) Curvature radius \(R\) when \(f_{y}\) = 5 Hz.  
If \(\alpha=0\) in Equation (13), this is equivalent to using a single spring-damper. In this case, the natural frequency of the \(x\) direction becomes \(f_{x}=\frac{1}{2\pi}\sqrt{g/R}\), independent of the vertical motion, which is the same as that of the rolling (sliding) type isolation system [18].
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 6 Limitation and Future Work  
First, the diversity of color mapping, as shown in Figure 9, is problematic. Deciding on the appropriate palette required considerable time and effort. Although clinical physicians choose the color mapping based on their diagnostic perspective, this approach is inherently subjective. It is likely that among the color mappings we have not yet tried, there are those that could yield significant results. I would like to emphasize once again that our standard was based on a series of color mappings over time. This temporal approach to color mapping was selected to potentially reveal changes or patterns that might not be as noticeable with static or single-instance color mappings.  
Secondly, the accurate visualization of blood vessels requires precise masks. However, data with masks is exceedingly rare, and even when this is available, the quantity is very limited. For datasets without masks, we relied on the results from training and evaluating datasets that do have masks, which makes it difficult to ensure the completeness of the vascular images. In particular, distinguishing between arteries and veins is crucial, as their differences can be pronounced in the presence of lesions. We aimed to construct data incorporating both arteries and veins, but assembling such datasets poses challenges, primarily due to the difficulty in obtaining continuous co-operation from experts. This limitation hinders the potential to fully leverage detailed vascular features for diagnostic enhancements.  
Thirdly, it was challenging to build a continuous and granular model. While there are studies that have categorized glaucoma into stages such as early, middle, and late to research severity, the available data were severely limited. These data exhibit extreme  
Figure 8: Confusion matrix of ensemble result for each dataset. (**a**) REFUGE; (**b**) ORIGA; (**c**) G1020; (**d**) AI-HUB; (**e**) Private dataset.  
Figure 9: Appearance after applying various palettes.  
data imbalance issues at each stage, necessitating a strategic approach in future research to address this challenge. Effective strategies might include developing sophisticated data augmentation techniques, leveraging synthetic data generation, or collaborating broadly to gather a more extensive and balanced dataset that adequately represents each stage of glaucoma for more nuanced modeling and analysis.  
Lastly, we intend to develop a predictive model that uses time-series data to determine the likelihood of developing glaucoma in the future, even if the current state is normal. Given that glaucoma progresses without pain, predictive models are crucial. However, collecting longitudinal patient data over extended periods poses significant practical challenges and will not be an easy task. Such efforts require robust data infrastructure and long-term patient follow-up, which can be resource-intensive. Nonetheless, overcoming these challenges is essential for advancing early diagnosis and potentially improving outcomes for those at risk of developing glaucoma.
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## Security Issues and Challenges in WSN-IoT
### Trust Management  
In WSN-IoT frameworks, there is a consequence of regional conventions, resources, and limits of distinctive devices, which is a considerable assessment of IoT trust management. Trust is a significant part of WSN-IoT security, data security, administration, applications, and client protection. Trust is a fundamental component of communications among WSN-IoT devices to trade and manage information. IoT layers have a unique assortment of gadgets. Every gadget creates an enormous amount of information vulnerable to various assaults, dangers, and issues. These issues and attacks have the potential to spread across all IoT layers. As a result, the accuracy of information and administration will be reduced [53; 55].  
Trust management in IoT ought to accomplish the accompanying objectives of having faith in IoT nodes and choices to help one another. It should moderate client security, information transmission, and trust correspondence, as indicated by the IoT system's strategy. It should increase the superiority of IoT services, framework security, and reliability [56; 57; 58]. Furthermore, clients should not be aware of it.
###### Abstract
Previous research on continuous casting and hot rolling scheduling have mostly focused on minimizing the completion time by scheduling the order of slabs, without \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Existing Research** & **The Research in This Paper** \\ \hline Research on energy savings of reheating furnace & From the perspective of thermal energy, including furnace temperature control [3], waste heat recovery [4], etc. & Research the reheating furnace cold-charging and heat-charging slabs conversion mode, form the expression of energy consumption. \\  & Divides the scheduling into two parts, one focusing on the production matching problem of continuous casting and hot rolling and the other on minimizing the residence time of the reheating furnace [5] or increasing the hot-charging rate [6; 7] & Research on continuous casting –hot rolling \\ Modeling & Continuous casting–hot rolling model [8] or reheating furnace–hot rolling model [9] & Integrated model of continuous casting–reheating furnace–hot rolling \\  & mixed-integer programming and constraint & \\ Research on solving methods & programming [6], simulated annealing [9], heuristic algorithms [7; 10], intelligent algorithms [11], etc. & Improved bat algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research comparisons. considering energy impact. Or, they take cold slab as the research object, focus on the minimum residence time of the slab in the reheating furnace, and schedule the reheating furnace-hot-rolling process. The impact of slab sequencing in the reheating furnace on continuous casting-hot rolling scheduling and enterprise energy consumption is rarely considered. Therefore, a continuous casting-hot rolling scheduling model was established under the reheating furnace conversion mode with the weighted coupling goal of minimizing completion time and energy waste;
3. For practical engineering problems in steel mills, the problem scale is large, and the solution is complex. It is necessary to find a more suitable algorithm for solving this model. An improved bat algorithm was proposed in this research to solve the model, and its effectiveness in solving practical engineering problems was demonstrated.
## 2 Materials and Methods
### Channel Reduction
#### 2.7.1 Data Mapping Analogous to LiDAR Sensor Processing  
Drawing on the \"bird's eye\" perspective utilized in LiDAR sensor (Light Detection and Ranging) data analysis, this paper proposes a comparable approach for analyzing simulated georadar data. LiDAR sensors provide a three-dimensional point cloud of the environment and are used, for example, in the localization of autonomous robots and the detection of their surroundings. Due to the operation, where a rotating laser outputs the current angle and the measured distance, a point cloud consisting of the nearest reflective surfaces around the sensor is created [18]. The vertical resolution is usually very limited compared with the angle resolution, but here too, the need arises to reduce the horizontal levels. In the case of point clouds, this is performed by encoding the three-dimensional dataset onto three horizontal image planes. The first image plane describes the height of the highest reflected point at each position, while the second plane records its intensity. The total number of reflected points at each position is entered on the third plane [19]. Unlike LiDAR data, where few points are occupied in the entire space, ground radar measurement data have an intensity value for every point, making a simple transfer of the aforementioned encoding impractical. The following are various processing methods aimed at highlighting the characteristic properties of ground radar data.
# Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System
## 4 Proposed System
### A Real-World Case Study Scenario  
In this section we demonstrate an application of our proposed scheme in a real-world scenario. Figure 7 depicts a hospital information system scenario in which users such as patients, insurance companies, government agencies, and smart devices exchange data. IKGSR key generation, which produces the key pairs, initiates a sample set of circumstances workflow. Each smart gadget sends its attributes to the key generation authorities to register. After authentication, IKGSR sends the relevant device the Public Key. The device can use this key to generate the access code and encode its contents using the IKGSR encoding function using a blockchain-based smart contract. The medical supplier receives the uploaded encrypted files and access key. At that moment, the patients or smart devices send a query to HIS, and HIS responds by sending the patients or smart devices encrypted information and an authentication key. Patients or smart devices must finish the decoding procedure in order to access the encrypted message. The steps in deciphering are comparing the access key, trying to match the characteristics of the system administrator, and confirming the name of the system administrator. The adversaries or users won't be able to access the actual data of the owner when any of the three requirements are not met.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 3 Problem Formulation and Analysis  
As stated in Section 2, the cell is divided into two parts, Zone 1 and Zone 2. The carrier frequency in Zone 1 was taken to be 2 GHz. The D2D users reuse the uplink cellular resources for direct communication among each other. The D2D communication takes place in Mode 1.
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
###### Abstract  
Estimating a 2D homography from a pair of images is a fundamental task in computer vision. Contrary to most convolutional neural network-based homography estimation methods that use alternative four-point homography parameterization schemes, in this study, we directly estimate the \(3\times 3\) homography matrix value. We show that after coordinate normalization, the magnitude difference and variance of the elements of the normalized \(3\times 3\) homography matrix is very small. Accordingly, we present STN-Homography, a neural network based on spatial transformer network (STN), to directly estimate the normalized homography matrix of an image pair. To decrease the homography estimation error, we propose hierarchical STN-Homography and sequence STN-homography models in which the sequence STN-Homography can be trained in an end-to-end manner. The effectiveness of the proposed methods is demonstrated based on experiments on the Microsoft common objects in context (MSCOCO) dataset, and it is shown that they significantly outperform the current state-of-the-art. The average processing time of the three-stage hierarchical STN-Homography and the three-stage sequence STN-Homography models on a GPU are 17.85 ms and 13.85 ms, respectively. Both models satisfy the real-time processing requirements of most potential applications.  
homography; spatial transformer network; convolutional neural network +
Footnote †: journal: Applied Sciences
# Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks
## 5 Discussion  
In this section, we present the novelties and limitations of the proposed scheme. First, the novelties of the proposed scheme are as follows.  
Figure 11: F-measure according to the number of top-k events.  
Figure 10: Comparison of F-measure according to the local event detection schemes.  
* Complementing regional information in local event detection: The existing local event detection schemes either use only geo-tagged tweets to extract regional information or use the user's profile and the profiles of the user's friends to infer regional information. However, as mentioned in the existing studies [21; 23], geo-tagged tweets account for less than 1% of the total tweets and are even fewer if they only include tweets associated with real events. The proposed scheme complements regional information through a geographical dictionary and the regional information that is referred to in retweets as well as geo-tagged tweets.
* Event detection through relevant document analysis: In Twitter, retweets and threads explicitly indicate an association with the previous tweet. Based on this information, regional information can be supplemented on a prebuilt graph. In addition, information about the event can be expressed in detail on the graph. Furthermore, the limitations of the proposed scheme derived from experimental evaluations are as follows.
* Difference of detection rate by event: Local event detection schemes show different detection rates depending on the characteristics of the event. For example, COVID-19 frequently generates tweets that contain very close regional information. It shows a very high detection rate through the proposed scheme. Events such as fires and accidents, on the other hand, show relatively low detection rates, as the tweets mainly contain insurance, content related to local fire departments, content related to incidents overseas, and historical content. By reflecting on these characteristics, we need to overcome these limitations in a way that assigns weights to certain words for each event.
* Geographic dictionary dependence: The proposed scheme complements regional information based on a predefined geographic dictionary. However, the proposed scheme has a problem where important events containing specific building names are not frequently detected by the current geographic dictionary. To address such a problem, a novel method is needed to build a learning-based geographic dictionary in which data are constantly added through learning. Furthermore, it is necessary to collect events and geographic information from various cases through more diverse data collection.
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 2 Research on the Strain Transfer Model
### Surface-Bonding Stain Transfer Model
Assuming the vertex of the triangle was (\(0,H\)), and other two vertices were (\(-D/2\), \(0\)) and (\(D/2\), \(0\)), then the right boundary equation was as follows:  
\[h(x)=-\frac{2H}{D}x+H \tag{3}\]  
Through stress analysis on the micro-elements of the bonding layer, the equilibrium equation in \(x\) direction can be found:  
\[\pi r_{g}\tau_{gc}\big{(}x,r_{g}\big{)}+\left[\int_{0}^{\frac{D(H-h)}{2D}} \bigg{(}H-\frac{2H}{D}x\bigg{)}dx\right]d\sigma_{c}=\frac{D(H-h)}{2H}dx\cdot \tau_{c}(x,h) \tag{4}\]  
Substituting Equation (2) into Equation (4) and simplifying produces the following:  
\[\tau_{c}(x,h)=\frac{\pi r_{g}{}^{2}Hd\sigma_{g}}{D(H-h)dx}+\frac{(H+h)d\sigma_ {c}}{2 dx} \tag{5}\]  
Because the fiber grating and bonding layer deformed simultaneously, their strain variation rate was similar, and their elastic moduli are significantly different, so the following conditions can be justified as \(\frac{d\varepsilon_{g}}{dx}=\frac{d\varepsilon_{c}}{dx}\) and \(\frac{E_{c}}{E_{g}}=0\), \(E_{g}\) is the fiber modulus, producing the following:  
Figure 3:0  
Using the essential of shear-lag model, the following equation can be derived:  
Figure 3:1  
According to Equation (10), the average strain transfer rate measured by the fiber grating sensor can be expressed as follows:  
Figure 3:2  
The strain transfer model proposed in this paper is suited to uncoated FBG and polyimide FBG. The result from this model can be compared with [PERSON] [6] and [PERSON] [7]'s Model as Figures 7 and 8:
## 2 Operating Principle
When the output signal from the SL was sent to a photodetector through the CIR again, the two dominant wavelengths with the frequencies of \(f_{m}\) and \(f_{avv}\) would generate a beating signal, which had a frequency of \(f_{c}\), as shown in Figure 2. \(f_{c}\) could be tuned from a few GHz to tens of GHz by changing the detuning frequency \(\Delta f\), and the injection ratio (\(R\)), respectively or together. Here, the parameter \(R\) is expressed as the square root of the power ratio between the injected light and the optical signal generated by the free-running SL. Furthermore, the injected light was modulated in a PM by the subharmonic microwave signal with frequency \(f_{0}/N\), where the frequency \(f_{0}\) was approximately equal to the frequency \(f_{c}\). One of the \(N\)th-order sidebands of the modulated signal was located near the locking area of the SL and locked it. Therefore, the P1 oscillation frequency was locked. At the same time, only the OEO mode that was close  
Figure 1: Schematic diagram of the dual-frequency dual-loop OEO. ML: master laser; SL: slave laser; OC: optical coupler; PM: phase modulator; CIR: optical circulator; PC: polarization controller; DOMZM: dual-output Mach-Zehnder modulator; SMF: single-mode fiber; VODL: variable optical delay line; BPD: balanced photodetector; EA: electrical amplifier, EBPF: electrical band-pass filter; ESA: electrical spectrum analyzer; SSA: signal source analyzer. to the \(N\)th-order sideband would oscillate steadily. Finally, the OEO could oscillate stably to generate a signal with high quality. Furthermore, the free-running frequencies of ML1 and ML2 were \(f_{m1}\) and \(f_{m2}\) in the proposed OEO system, respectively. However, \(f_{m1}\) was different from \(f_{m2}\) in our structure. The microwave signal with a frequency of \(f_{0}/N\) modulated the PM, which was used to implement subharmonic microwave modulation. Then, the modulated optical signals were injected into the FP-LD. As far as we know, FP-LD is a multilongitudinal mode semiconductor laser. The different longitudinal mode of the FP-LD would be red-shifted and work at the P1 oscillation state via dual-beam optical injection, so that two microwave signals with different frequencies could be obtained when the optical signals were beaten in the BPD.
# Ultrasound-Guided Detection and Segmentation of Photoacoustic Signals from Bone Tissue In Vivo
## 2 Materials and Methods
### 2D Simulations of US Signal Propagation  
A 2D simulation model of an elastic wave was developed based on the finite-difference time-domain (FDTD) method [33; 34]. For the excitation condition, the Gaussian-modulated sine pulse defined in Equation (1) was used as the ultrasound pressure source.  
\[p(t)=A\cdot exp(\frac{-\left(t-\frac{d}{2}\right)^{2}}{s^{2}})\cdot sin(2\pi ft) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Bone Mineral** & **Bone Marrow** \\ \hline Density (kg/m\({}^{3}\)) & 1960 & 1000 \\ \hline First Lamé coefficient (GPa) & 14.8 & 2 \\ \hline Second Lamé coefficient (GPa) & 8.3 & 0 \\ \hline Longitudinal wave SOS (m/s) & 4002.6 & 1500 \\ \hline Shear wave SOS (m/s) & 2057.8 & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the related materials.  
Figure 1: The ultrasound (US) and photoacoustic (PA) 2D simulation system. (**a**) Flow chart of the US and PA 2D simulation process. (**b**) Schematic diagram of the US mode. (**c**) Schematic diagram of the PA mode.  
where \(A\) is the amplitude, \(d\) is the delay of the pulse, \(s\) is the steepness of the function, and \(f\) is the central frequency. In our simulation, the parameters were defined as follows: \(A=1\), \(d=1\) us, and \(s=1\) us, and the center frequency \(f\) was set at 0.5 MHz. Figure 1b shows the geometry of the FDTD simulation model for ultrasound transmission measurement. The total area of the simulation region was approximately \(50\times 100\) mm\({}^{2}\), with the bone model placed at x = 25 mm. Both the transmitting and receiving transducers were flat lines with a length of 25.4 mm. The transmitting transducer was placed at x = 17 mm, which is near the surface of bone tissue, and the ultrasonic pressure source was given at the corresponding points on the transmitting surface. The simulated ultrasonic waves propagate through the whole bone in the x direction. The transmission wave propagation through the whole bone was received by another ultrasonic transducer at another side of the bone tissue. The transmission signal that propagated through the entire bone was obtained by averaging the pressures at the corresponding points on the entire surface of the receiver transducer placed at x = 75 mm. The signal propagation lasted for 100 us in the simulation, which allowed it to travel through and beyond the specified domain.