esa-sceva/satcom-chunk-collection huggingface dataset:
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods
### Spatial Structural Characteristics  
The registration between the on-vehicle images and the HD map involves aligning data from different spaces and modalities. Due to the characteristics of perspective projection, certain structural traits remain unchanged or transform into other characteristics. To capture these spatial structural transformations in the 3D-2D registration, we defined category-based SSC.  
According to [28], guiding principles based on the Law of Parallel Lines in Perspective provide a framework for understanding these spatial transformations. These principles state that parallel lines that are parallel to the picture plane remain parallel in the perspective view, while parallel lines perpendicular to the picture plane converge towards a vanishing point, as displayed in Figure 6a. In the context of the registration process, the shapes of lane markings belong to the category of parallel lines that converge towards a vanishing point. Meanwhile, poles and traffic signs fall under the category of parallel lines that remain parallel, as shown in Figure 6b-d. Assuming that the shapes of the HD map objects in the camera frame are composed of points \((x_{c},y_{c},z_{c})\), we can represent the object category-based factors as follows.
## 3 Open-Circuit Fault Diagnosis Strategy
### Design of IASMO
Based on the Cauchy-Schwarz inequality and norm properties, the following inequality holds:  
\[ \begin{split}\dot{V}&\leq e^{\mathsf{T}}[(A-LC)^{ \mathsf{T}}P+P(A-LC)]e+2\|Pe\|\left(\|E\|\lambda-\frac{k}{\gamma}\right)\\ &\leq e^{\mathsf{T}}[(A-LC)^{\mathsf{T}}P+P(A-LC)]e-2\rho\|Pe\| \\ &\leq e^{\mathsf{T}}[(A-LC)^{\mathsf{T}}P+P(A-LC)]e\\ &\leq e^{\mathsf{T}}[A^{\mathsf{T}}P-C^{\mathsf{T}}(P^{-1}Z)^{ \mathsf{T}}P+PA-PP^{-1}ZC]e\\ &\leq e^{\mathsf{T}}[A^{\mathsf{T}}P+PA-C^{\mathsf{T}}Z^{\mathsf{ T}}-ZC]e\end{split}  \tag{17}\]  
As demonstrated above, if the inequality \(A^{\mathsf{T}}P+PA-C^{\mathsf{T}}Z^{\mathsf{T}}-ZC<0\) holds, then \(\dot{V}<0\). This implies that the observer error is asymptotically stable. Consequently, the designed IASMO is also asymptotically stable. Notably, the positive definite symmetric matrix \(P\), matrix \(Z\), and the condition \(A^{\mathsf{T}}P+PA-C^{\mathsf{T}}Z^{\mathsf{T}}-ZC<0\) can be addressed with the Linear Matrix Inequality (LMI) toolbox.
## 2 Materials and Methods
### Data Collection and Processing
#### 2.1.3 Automated Data Pipeline  
The data extraction pipeline was written in Python using a third-party library, PyGitHub. The pipeline took the chosen search terms as input and received repository data in JSON. The JSON responses were first filtered and then converted to database records and pushed to tables in a MySQL database. Repositories with no description or no programming language specified were excluded from further analysis for the reason that clinical software was the focus of our study. The whole process is reproducible by running the Python scripts at Reference [22]. Moreover, replacing the search term with others scales the pipeline to other domains.
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 2 Methodology
### Support Vector Regression  
SVR is a type of SVM used for regression modeling, adopting SVM principles to predict a continuous output variable via kernel functions [30]. SVR aims to find a function \(f(x)\) that deviates from the actual target values \(y\) by a value no greater than \(\varepsilon\) (epsilon), while being as flat as possible. The function \(f(x)\) is expressed as follows:  
\[f(x)=wx+b \tag{1}\]  
where \(w\) = weight vector, \(x\) = input vector, and \(b\) = bias term. To achieve that, SVR solves the following optimization problem:  
\[\min_{w,b}\frac{1}{2}\|w\|^{2} \tag{2}\]  
For each training data point (\(x_{i}\), \(y_{i}\)), Equation (2) is subject to  
\[\left\{\begin{array}{l}y_{i}-(wx_{i}+b)\leq\varepsilon\\ (wx_{i}+b)-y_{i}\leq\varepsilon\end{array}\right. \tag{3}\]  
This ensures that the predicted values \(f(x_{i})\) lie within an \(\varepsilon\)-tube around the actual values \(y_{i}\). In practice, fitting all data points within the \(\varepsilon\)-tube is often challenging. To this end, slack variables \(\xi_{i}\) and \(\xi_{i}^{*}\) are introduced to handle points outside the tube. Consequently, the optimization problem becomes  
\[\min_{w,b,\xi,\xi^{*}}\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{n}(\xi+\xi_{i}^{*}) \tag{4}\]subject to  
\[\left\{\begin{array}{l}y_{i}-\left(w\phi(x_{i})+b\right)\leq\varepsilon_{0}+ \zeta_{i}\\ \left(w\phi(x_{i})+b-y_{i}\right)\leq\varepsilon_{0}+\zeta_{i}^{*}\\ \zeta_{i}\zeta_{i}^{*}\geq 0\end{array}\right. \tag{5}\]  
where \(C=\) regularization parameter. It controls the trade-off between the flatness of the function and the amount up to which deviations larger than \(\varepsilon\) are tolerated.  
SVR is typically solved using its dual formulation, which involves Lagrange multipliers. The dual problem is expressed as follows:  
\[\max_{\alpha,\alpha*}\left(-\frac{1}{2}\sum\limits_{i,j=1}^{n}\left(\alpha_{i }-\alpha_{i}^{*}\right)\left(\alpha_{j}-\alpha_{j}^{*}\right)K\left(x_{i},x_{ j}\right)-\varepsilon\sum\limits_{i=1}^{n}\left(\alpha_{i}-\alpha_{i}^{*} \right)+\sum\limits_{i=1}^{n}y_{i}\left(\alpha_{i}-\alpha_{i}^{*}\right)\right) \tag{6}\]  
subject to  
\[\left\{\begin{array}{l}\sum\limits_{i=1}^{n}\left(\alpha_{i}-\alpha_{i}^{*} \right)=0\\ 0\leq\alpha_{i},\alpha_{i}^{*}\leq C\end{array}\right. \tag{7}\]  
where \(K(x_{i},x_{j})=\) Kernel function, and \(\alpha_{i}\), \(\alpha_{i}^{*}=\) Lagrange multipliers. Kernel functions map the input data into a higher-dimensional feature space where a linear hyperplane can be used to perform the regression. Lastly, once the SVR model is trained, predictions for new data points \(x\) are made using the following expression:  
\[f(x)=\sum\limits_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(x_{i},x)+b \tag{8}\]
###### Abstract  
The bi-directional information transfer in optical body area networks (OBANs) is crucial at all the three tiers of communication, i.e., intra-, inter-, and beyond-BAN communication, which correspond to tier-I, tier-II, and tier-III, respectively. However, the provision of uninterrupted uplink (UL) and downlink (DL) connections at tier II (inter-BAN) are extremely critical, since these links serve as a bridge between tier-I (intra-BAN) and tier-III (beyond-BAN) communication. Any negligence at this level could be life-threatening; therefore, enabling quality-of-service (QoS) remains a fundamental design issue at tier-II. Consequently, to provide QoS, a key parameter is to ensure link reliability and communication quality by maintaining a nearly uniform signal-to-noise ratio (\(SNR\)) within the coverage area. Several studies have reported the effects of transceiver related parameters on OBAN link performance, nevertheless the implications of changing transmitter locations on the \(SNR\) uniformity and communication quality have not been addressed. In this work, we undertake a DL scenario and analyze how the placement of light-emitting diode (LED) lamps can improve the \(SNR\) uniformity, regardless of the receiver position. Subsequently, we show that using the principle of reciprocity (POR) and with transmitter-receiver positions switched, the analysis is also applicable to UL, provided that the optical channel remains linear. Moreover, we propose a generalized optimal placement scheme along with a heuristic design formula to achieve uniform \(SNR\) and illuminance for DL using a fixed number of transmitters and compare it with an existing technique. The study reveals that the proposed placement technique reduces the fluctuations in \(SNR\) by 54% and improves the illuminance uniformity up to 102% as compared to the traditional approach. Finally, we show that, for very low luminous intensity, the \(SNR\) values remain sufficient to maintain a minimum bit error rate (BER) of \(10^{-9}\) with on-off keying non-return-to-zero (OOK-NRZ) modulation format.  
Keywords:illuminance; optical body area network (OBAN); optimal lamp arrangement; reciprocity; signal-to-noise ratio (SNR) uniformity; visible light communication (VLC) +
Footnote †: journal: _Sensors_
## 2 Perforation Experiment
### Experiment of Simulated Reservoir Penetrating Hard Rock Target  
According to the Evaluation of Well Perforators issued by the American Petroleum Institute, a simulated reservoir perforation test device was designed and established according to the mechanical environment, rock properties, perforating gun parameters, and actual underground working conditions of the target reservoir. The device and test flow are illustrated in Figure 3. The device is mainly composed of a unit gun and hard rock in two parts and a shaped charge (the same model as above) assembled in the gun, and it is connected to a detonator, placed in the upper part of the device. The hard rock (a cylinder 20 cm in diameter and 45 cm in height) is vacuum-saturated and placed under the unit gun.  
Figure 2: Experimental results of penetrating steel target.  
At the onset of the experiment, the shaped charge is fired through the detonator. The resultant metal jet sequentially penetrates the gun holster (containing blind holes), wellbore fluid (water), casing, and cement ring, and ultimately forms a perforation channel within the hard rock. Considering the actual reservoir conditions, wellbore pressure is regulated at 25 MPa, while confining pressure is maintained at 30 MPa.  
Seven groups of experiments were repeated with the above device. After the experiment was completed, the experimental target was broken, the maximum depth of the hole was recorded as the perforation depth, and the diameter of the opening of the experimental target was measured as the perforation aperture. The experimental results are illustrated in Figure 4. The perforation depth and perforation aperture of the seven groups were relatively stable. The perforation depth fluctuated between 30.52 cm and 33.29 cm, the average depth was 32.00 cm, and the standard deviation was 1.05 cm. The perforation aperture fluctuated between 1.00 cm and 1.17 cm, with an average aperture of 1.08 cm and a standard deviation of 0.07 cm. The statistical results indicate a low degree of dispersion for each data point, thereby providing reliable support for subsequent numerical simulations of hard rock perforation and the inversion of constitutive parameters.  
Figure 4: Experimental results of simulated reservoir penetrating hard rock target.  
Figure 3: Experimental device and process.
## 1 Introduction
### Previous Peak to Average Power Ratio or Crest Factor Reduction Works
The block diagram of DSI-SLM technique is presented in Figure 4.  
DSI-SLM approach can reduce the PAPR by more than 4 dB; however, in order to achieve that performance the inverse discrete fast Fourier transform (IDFT) can require being implemented approximately 16 times, which in turn is computationally challenging. Moreover, due to the selection of the sequence with minimum PAPR at the right side of the above block diagram, the information about this selection also has to be sent to the receiver. For example, in order to have 16 selections, 4 extra bits have to be sent. Another challenge for implementing DSI-SLM compared to clipping based techniques is the necessity of implementing compensation algorithm in the receiver side. As seen in block diagram of Figure 4, \(M\) different phases (\(Ph.1\), \(Ph.2\), \(\ldots\), \(Ph.M\)) are multiplied into the signal. The received signal at the receiver has to be divided by the same phase in order to extract the original signal, this means implementing a complex division block at the receiver, which has its own complications, as explained  
Figure 3: Example of conventional clipping and filtering technique. Figure 2: LTE signal spectrum with/without clipping technique (**a**); time domain LTE Signal with/without clipping technique (**b**). in [21]. There are some works aimed at improving these complications [22; 23] and the authors have designed algorithms to avoid transferring extra bits; however, the complexity at the transmitter, and the channel security, remain challenging.
## 5 Conclusions  
In this paper, the optimization model with mobile edges for multimedia sensors using artificial intelligence of things is presented, which aims to increase the management of network resources in multimedia traffic with securing transmission. It provides the real-time paradigm for critical MIoT-based applications and facilitates production with high reliability. Moreover, communication is secured under the occurrence of malicious nodes with the lightweight nodes' power of the MIoT network using intelligent SDN technology. It gives intelligent decisions among mobile edges by evaluating the QoS features and strengthens the network performance. Moreover, nodes are authenticated with each other and secret shares by using the Shamir secret sharing scheme. The set of experiments were performed in the OMNET++ simulator, and based on the results analysis, it is proven that the proposed model remarkably increases the performance for the delivery rate, time delay, routing overheads, packet drop ratio, and reliability, than benchmark solutions. The proposed model gives some intelligence using edge computing; however, it faces some communication expenses in determining the optimal forwarders. Thus, in the future, we aim to utilize the transfer learning technique and train the IoT network with a real-time data set.  
Conceptualization, A.R., K.H. and T.S.; Methodology, A.R., T.S., K.H.; Software, A.R., S.S., J.L.; Validation, J.L., S.S.; Formal Analysis, A.R., J.L.; Investigation, A.R., K.H., T.S.; Resources, A.R., J.L.; Data Curation, S.S., J.L.; Writing--Original Draft Preparation, A.R., K.H.; Writing--Review & Editing, T.S., J.L.; Visualization, S.S.; Supervision, J.L.; Project Administration, A.R.; Funding Acquisition, A.R., J.L. All authors have read and agreed to the published version of the manuscript.  
There is no funding for this research work.  
Institutional Review Board StatementNone.  
Informed Consent StatementNone.  
Data Availability StatementAll Data is available in the manuscript.  
This research was technically supported by the Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia. The authors are thankful for the technical support.  
Figure 7: Data retransmission with the number of nodes.
## 1 Introduction
To reveal the strengths of different signal domains, the Fast Fourier Transform can be used to switch from time to frequency domain, and it has been noticed changes in the vibration signature of railway elements such as axle-boxes [13]. Moreover, if we want to understand how the strength of a signal is distributed in the frequency domain, we can use the power spectral density (PSD), which describes the power of the signal as a function per frequency unit. Therefore, the PSD can be used to infer normal operation or fault conditions of railway vehicle [14; 15]. Through an analysis of CIs, it is possible to obtain adequate information for the understanding and interpretation of the machinery condition, such as the definition of limit values of certain indicators to establish that the machinery is in normal or abnormal conditions. This would support the diagnosis process and the maintenance decision-making [16; 17]. On the other hand, the result of the diagnosis from the analysis of CIs can be improved by having several sensors to monitor the machinery because it would allow performing Data -Fusion [18; 19]. The extraction of indicators in time and frequency domains requires a lower computational cost than the required one for calculating indicators in the time-frequency domain [20]. Data Fusion refers to the combination of data from multiple sensors of either the same or different types, and can be defined as the use of techniques that combine data from multiple sources. Thus, by using data fusion, a more reliable and realistic inference, deduction or discrimination can be made by using data from different isolated sources in data-driven approaches [21]. The goal of this work is the evaluation of the performance of condition indicators in time and frequency domains for crack detection in railway axles through the use of vibration signals. Two approaches are proposed: (1) the first one evaluates the indicators extracted from vibration signal of two accelerometers, in time domain, frequency domain, and their combination, and (2) the second approach evaluates the data fusion of the indicators of the six accelerometers. The rest of this paper is organized as follows. Section 2 presents the condition indicators used in this work, the selection, and classification methods. Section 3 contains the experimental set-up and data acquisition. In Section 4, the proposed methodology for evaluating the condition indicators performance is detailed. Then, Section 5 shows the results and discussion, and finally in Section 6, the conclusions are addressed.
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 2 Background Review
### Data Rate-Based Approaches  
The eight possible data rates that the DSRC standard has specified can be used for BSM transmissions are 3, 4.5, 6, 9, 12, 18, 24, and 27 Mbps; the most commonly used of these is 6 Mbps [15]. Data rate-based approaches alter the bitrate used when transmitting BSMs, and they have recently been garnering more focus [15]. The work in Ref. [56] compares power control- and data rate control-based approaches, and shows that the latter can outperform the former in a variety of different scenarios, such as when congestion occurs in a localized cluster of vehicles.  
In Ref. [57], only the 3, 6, 9, and 12 Mbps data rates were considered to avoid flooding. There were four possible states for the vehicles, one for each data rate, and the state of the vehicles was determined by the channel load. The vehicles would use the data rate that corresponded to the state they were currently in. In Ref. [58], the CBR was measured at regular intervals and vehicles transitioned to different states depending on the CBR, where the state of a vehicle determined the data rate that it used. If a vehicle measured the current CBR to be greater than a predefined threshold, it would move to a state that used a higher data rate, and if it calculated the CBR to be less than a given threshold, it would move to a state that used a lower data rate. In Ref. [59], the authors defined a combined message and data rate-based algorithm (MD-DCC) that can lead to increased supported vehicle density, and in Ref. [60], they explored how MD-DCC can coexist with LIMERIC, allowing vehicles to choose either one at different vehicle densities. In Ref. [61], the authors proposed a hybrid method that involved managing a combination of the data rate and the message rate. The message rate was reduced in order to keep the beacon frequency above the required minimum value, while the data rate was increased during periods of high vehicle density to provide a greater channel capacity.  
This paper also focuses on data rate-based distributed congestion control, and a preliminary version of this work with initial results was presented in Ref. [62]. In this paper, we present our updated literature review, provide a more detailed description of our algorithm, and include a study of how the selection of the congestion threshold values affects its performance. This paper also provides new simulation results with different packet sizes and beacon intervals, along with an analysis of the distribution of received packets based on distance from the sender.
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
## 3 Design
### Design Requirements and Antenna Structure  
As outlined in Section 2, the drain and gate connections must form an RF short circuit, while radiation between the gate and source connections should be introduced with high impedance. The antenna structure considered here is a planar bow-tie design with a 60-degree opening angle. Figure 4 illustrates the metal surface designs used to meet these boundary conditions and the achievable impedances. The antenna feed point between the gate and source terminals is marked in the first column of Figure 4.  
* Top Row: Overview of the entire structure.
* Bottom Row: Detailed metallic structures of the HEMT.  
The drain and gate terminals form an antenna wing, creating an RF short circuit over a wide bandwidth. The gate terminal acts as the lower electrode, and the drain terminal serves as the upper electrode of the MIM capacitor.  
Figure 4: (**a**) Scanning electron images of a bow-tie detector structure and, on the bottom part, an enlarged image of the central HEMT. (**b–d**) Schematic representation of the galvanic connection of the three HEMT terminals.  
Column (a): a scanning electron micrograph shows the partially fabricated HEMT, where the gate terminal completely encloses the drain terminal.
* Column (b): the continuation of the metal surface, directly connected to the gate terminal.
* Column (c): the metallic connection of the source terminal which is in the same plane as the gate connection.
* Column (d): the metallic connection structure of the drain terminal  
Between columns (c) and (d), a 200 nm silicon nitride layer is applied over the surface, with openings at the drain terminal. Subsequently, the second metallization is vapor-deposited, contacting the drain terminal. MIM capacitances form where the metal layers overlap, as shown in the top row of Figure 4.