esa-sceva/satcom-chunk-collection huggingface dataset:
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 3 EEDOR Void Avoidance with Hop Count Discovery Mechanism
### Network System
* The source node is a node that senses the phenomena and has the collected data to send to the sink(s) on the water's surface. * Next forwarder nodes are the source nodes for the next hops that will continue the forwarding procedure. * Forwarding candidates are other candidate nodes in the forwarding set that may become sources for the next hop if the higher priority candidates fail in forwarding the data packets. 2. Nodes that do not participate in packet forwarding. * Void nodes are nodes that do not have any neighbor nodes with less depth than themselves and are not within range of a sink. They therefore cannot send data to any node that could help in the forwarding process and deliver the packet to its destination. * Trapped nodes are nodes in which the only node in transmission range with less depth than itself is a void node or a node whose only path to nodes of lesser depth leads to a void node. That is, the only nodes of lesser depth that are in range of a trapped node are a void node or other trapped nodes. * The idle nodes are those nodes that are not part of a given source to sink transmission process. In our proposal, we improve the routing performance of EEDOR presented in [15] by using on-demand hop count discovery to prevent the void and trapped nodes from participating in the data packet transmission, to save the network resources. The network scenario shown in Figure 2 illustrates our model and the routing paths. In this model, nodes are homogeneous in terms of energy consumption and transmission range. When the packet holder (P\({}_{\text{holder}}\)) node has a packet to send to the surface sinks, it will use the neighboring relay nodes determined by the hop count discovery process to deliver this packet. The P\({}_{\text{holder}}\) and its neighboring relay nodes use acoustic signals to transmit their packets. We used the Thorp propagation model described in the next section to model the underwater acoustic channel. Figure 2: Network Architecture.
## 1 Introduction
First, this study reveals the reasons by which the performance of RS Codes becomes negatively impacted in the presence of single-frequency EMD. Second, this paper provides an overview of the impact of several setup parameters of RS Codes, including the symbol size and the message length on the overall performance of RS Codes. Finally, this paper introduces a more EM-resilient version of RS Codes against this type of disturbance. The remainder of this paper is structured as follows. Section 2 presents the mathematical definition of RS Codes. The performance metrics are described in Section 3. The experimental setup and the effectiveness of RS Codes under single-frequency EMD are detailed in Section 4. Section 5 proposes a mechanism to increase the EM-resiliency of RS Codes against single-frequency EMD and examines the overall performance of the proposed mechanism. Finally, conclusions and the future works are drawn in Section 6.
# Assessment of Cardiovascular Disease in Autosomal Dominant Polycystic Kidney Disease
## 4 Echocardiography
### Left Ventricular Hypertrophy and Molecular Mechanisms  
Another debated issue is the prevalence of left ventricular hypertrophy (LVH) in ADPKD patients. Some authors consider LVH a consequence of early arterial hypertension onset. In any case, PC1 and PC2 are involved in several intracellular signaling pathways, which can influence cardiac function. PC-1 regulates mammalian target of rapamycin (mTOR) and PC-2 can influence B-cell lymphoma-2 (Bcl-2), leading to a down-regulation of mitophagy and apoptosis; both include pathways which can trigger hypertrophy in cardiac muscle [24; 25; 26; 27; 28; 29; 30]. Based on this hypothesis, PC-1 or PC-2 abnormalities in the heart could lead to an impairment in cardiac structure and function independently of kidney function or blood pressure. Different studies which investigate the prevalence of LVH among ADPKD patients underline that hypertrophy is more prevalent in young, affected patients than in the general population, regardless of the presence of AH [31; 32; 33]. Moreover, [PERSON] et al. [34] found higher left ventricular mass indexes (LVMIs) in male ADPKD patients (13%) than in the healthy group (2%) when comparing ADPKD patients and age- and sex-matched patients with essential hypertension [35]. Otherwise, not all the authors agree with this hypothesis, and this correlation is still uncertain; in fact, some studies do not show a higher incidence of LVH in ADPKD patients when compared with hypertensive patients [35]. A possible source of uncertainty was explained in the review conducted by [PERSON] et al. [36]. In this study, the prevalence of LVH in ADPKD patients was 20-40% when assessed via echocardiography; when they took studies which assess LVH using MRI into consideration, the prevalence fell to 4%. These differences could be related to the imaging modality, variations in the parameters used to define LVH, or demographic differences in the study populations.
# Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
## 2 Theory
### Estimation of Pore Pressure  
For the assessment of the formation of pore pressure, Eaton's model based on MSE is used (Equation (35)). The Eaton exponent (m) is a regional factor, and it can be obtained from any known overpressure intervals in the offset or current wells [32]. The Eaton exponent is estimated by substituting the overburden pressure, pore pressure, regional normal pore pressure, MSE, and the MSE from the corresponding normal trendline into Equation (36) for the target calibration interval, where the overburden pressure is obtained by integrating the formation bulk density logs.  
\[G_{\mathrm{pp}}=G_{\mathrm{ob}}-\left(G_{\mathrm{ob}}-G_{\mathrm{np}}\right) \times\left(\frac{MSE}{MSE_{\mathrm{n}}}\right)^{m} \tag{35}\]  
\[m=\frac{\log\left(\frac{G_{\mathrm{ob}}-G_{\mathrm{pp}}}{C_{\mathrm{ob}}-C_{ \mathrm{np}}}\right)}{\log\left(\frac{MSE}{MSE_{\mathrm{n}}}\right)} \tag{36}\]  
where \(G_{\mathrm{pp}}\) is the formation pore pressure gradient (sg); \(G_{\mathrm{ob}}\) is the overburden formation pressure gradient (sg); \(G_{\mathrm{np}}\) is the normal pore pressure gradient (sg); \(MSE_{\mathrm{n}}\) is the MSE from the normal trendline at a given depth (MPa); and \(m\) is the Eaton exponent.
# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
###### Abstract  
Over the past year, there has been a significant rise in interest in the application of open-source artificial intelligence models (OSAIM) in the field of medicine. An increasing number of studies focus on evaluating the capabilities of these models in image analysis, including magnetic resonance imaging (MRI). This study aimed to investigate whether two of the most popular open-source AI models, namely ChatGPT 4o and Gemini Pro, can analyze MRI video sequences with single-phase contrast in sagittal and frontal projections, depicting a posterior fossa tumor corresponding to a medulloblastoma in a child. The study utilized video files from single-phase contrast-enhanced head MRI in two planes (frontal and sagittal) of a child diagnosed with a posterior fossa tumor, type medulloblastoma, confirmed by histopathological examination. Each model was separately provided with the video file, first in the sagittal plane, analyzing three different sets of commands from the most general to the most specific. The same procedure was applied to the video file in the frontal plane. The Gemini Pro model did not conduct a detailed analysis of the pathological change but correctly identified the content of the video file, indicating it was a brain MRI, and suggested that a specialist in the field should perform the evaluation. Conversely, ChatGPT 4o conducted image analysis but failed to recognize that the content was MRI. The attempts to detect the lesion were random and varied depending on the plane. These models could not accurately identify the video content or indicate the area of the neoplastic change, even after applying detailed queries. The results suggest that despite their widespread use in various fields, these models require further improvements and specialized training to effectively support medical diagnostics.  
artificial intelligence; pediatric brain tumor; ChatGPT; Gemini
# A Bus Signal Priority Control Method Based on Deep Reinforcement Learning
## 1 Introduction
Many researchers have also designed a multi-objective reward function based on weight [16; 31; 32; 33; 34]. However, the objective weight setting is mainly based on artificial experience, which is difficult to effectively calibrate, resulting in a certain degree of influence on the training convergence speed and stability of the algorithm. At the same time, the scale of different objectives is inconsistent, leading to poor interpretability. Finally, how can this model be solved? Traditional reinforcement learning is mainly based on the Q-value table learning algorithm [35] and SARSA algorithm [36], which is not good at dealing with high-dimensional and continuous traffic-state information, restricting further application. Thus, many studies have begun to take advantage of deep learning in data processing to carry out research on deep reinforcement learning in the field of signal control [37; 38]. The current research on bus signal priority control based on reinforcement learning exhibits the above problems to different degrees [39; 40; 41; 42]. To address these issues, a new method for bus signal priority control based on deep reinforcement learning is proposed. The main contributions of this study can be summarized as follows:  
1. We propose a unified framework for bus signal priority real-time control, which is able to migrate to different intersections and different time periods quickly;
2. By using key lanes to characterize traffic states, the dimension of the state space is effectively reduced, and the rapid training ability of the model can be improved;
3. The design of the action space is more flexible, which can adjust phase sequence and phase green time at the same time;
4. Under the multi-objective optimization, we establish a reward function that can be uniformly converted into the number of standard cars, which effectively solves the problem of weight calibration. Accordingly, the rest of this article is structured as follows. Section 2 provides a model description for bus signal priority control based on reinforcement learning. Section 3 introduces the reinforcement learning agent training process to achieve the proposed method. Section 4 presents the simulated experiment results to verify the effectiveness of the proposed method. Lastly, conclusions and recommendations for future research are made in Section 5.
## 3 Simulation Setup and Results under Ideal Assumptions  
### Channel Modeling and Deployment Considerations  
We consider the 3D mmW channel model proposed in [24], characterizing an urban micro-cellular scenario. This model is based on real measurements taken in New York City. It is consistent with the 3 GPP ray-based modeling methodology, and includes characterization of the channel in azimuth, elevation and polarization. This model takes into account channel variability in the frequency and time domains, considering also the actual correlation between antennas depending on the geometry of the antenna array deployment. The propagation condition has been taken from the urban micro 3 GPP channel model [25], which classifies users in LoS or NLoS condition by means of a probability distribution function of the distance between BS and UE. Simulations are performed in the lower edge of the mmW band, using a carrier frequency of 28 GHz and a system bandwidth of 1 GHz. Unless otherwise stated, perfect channel knowledge is assumed at the transmitter and receiver side.  
The simulation setup considers a wrap-around configuration of the seven-site layout depicted in Figure 2. The sites are deployed in an urban scenario with inter site distance of 200 m, which entails having approximately 70% of UEs in LoS condition. Each site includes three 120\({}^{\circ}\) sectors (cells), each one served by a BS equipped with a horizontal uniform linear array of 64 antenna elements. The antenna pattern of each element is parabolic as in [25], and then defined by a maximum gain (8 dBi), front-to-back ratio (20 dB), and half-power beamwidth (70\({}^{\circ}\)). Fifty UEs are randomly deployed per site, leading to an average of 16.7 UEs per BS. As already said, the modeling of beamforming at the UE side is simplified. Specifically, we assume that the UE is equipped with an array of antenna elements whose resulting pattern is parabolic with a maximum gain of 12 dBi, a front-to-back ratio  
Figure 1: Transceiver considered at the Base Station (BS), which is equipped with \(N_{t}\) antenna elements and \(P\) radio-frequency (RF) chains.  
of 20 dB, and a half-power beamwidth of 45\({}^{\circ}\) (the latter calculated according to [21]). In addition, we consider that the UE beamformer is always pointing to the direction that maximizes the SNR at the receiver. For all the simulated schemes, we assume a per-site power restriction of 40 dBm, with equal power allocated to each sector. In addition, equal power allocation among the active RF chains is considered at each BS for the analog implementations. Other important simulation parameters are listed in Table 1.
# An Improved FFIP Method Based on Mathematical Logic and SysML
## 4 Improvement of SysML Based on Failure Logic
### Improvement of SysML State Machine Diagram  
In the previous section, we use the BDD and SD in SysML to make a standardized modeling of the structure model, function model and part of the behavior model in FFIP. What has not been built in the behavior model is failure logic. Failure logic is the core of the extended FFIP method. However, none of the nine diagrams in SysML can describe failure logic. To combine SysML with FFIP, this paper extends the elements of SysML to describe failure logic.  
State machine diagrams (SMD) in SysML are mainly used to describe the life cycles of objects, subsystems and systems [48]. Through the SMD, we can know all the states that an object can reach and the impact of events on the state. Therefore, the SMD can represent the state transition of components and the transformation relationship between different states.  
In this paper, we divided the functional states of components into three categories: nominal state, failure state and degradation state. The nominal state is the normal operation state of the system. Failure state refers to the state in which the system produces some failure when the deviation exceeds a certain value. Degradation state refers to a state between nominal and failure state, which has not had a significant impact but deviates from normal performance. Failure logic can be represented using key process variables. The logical relationship between the input, output, and deviation of variables can explain the current state of the component, and the deviation can express the internal failure of component behavior and the effect of other components.  
However, we need more information in the state diagrams to describe the failure logic:  
* the representation of deviation;
* the representation of different state types of components.  
Table 4 shows the extension of SysML elements to meet the above two requirements.  
When we use the SMD to represent the FFIP behavior model, the state blocks are used to represent different states of the components. The arrows are used to show the factors that cause the state transition. The text on the arrow shows the mathematical logic to describe  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SysML Elements** & **SysML Symbol** & **Explanation of Meaning** \\ \hline Numerical deviation & Deviation & The deviation from the current value \\  & State & \\ Normal state & The state of the component when it is running normally. \\  & State & \\ Abnormal state & A block turns red after a component that has deviated indicates an exception. \\ \hline \hline \end{tabular}
\end{table}
Table 4: The extended elements of SysML.  
the conditions for triggering failure. The SMD of SysML can better show the transformation relationship of failure logic, making the models more coherent and readable.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 1 Introduction
A gradual drift is connected with a slower rate of change and can be found, for example, in a solar power system where the weather patterns change gradually, or the dust accumulation on solar panels is reducing their efficiency with time [19]. Incremental changes occur in small increments over time. For example, this type of drift happens when stock prices change incrementally due to a shift in market conditions or investors' behavior. This study investigated the performance of four commonly used methods (EDDM, DDM, HDDMW, and HDDMA) across hundreds of synthetic datasets to tackle the challenge of selecting a suitable change detection algorithm in a wide range of possible scenarios. These datasets were designed using a tool developed in this work to simulate various concept drift types, with varying amplitude and duration. The number of drifts and the stream size were also manipulated to increase the scenario's diversity. We evaluated the algorithms based on several criteria, including the detection accuracy, detection time, and detection delay. By exploring these perspectives, we aimed to provide insights into the strengths and limitations of each method, as well as their suitability for different types of concept drift scenarios. The main contributions of this paper are as follows:  
* Systematic comparison among EDDM, DDM, HDDMW, and HDDMA change detectors when processing streams with abrupt, gradual, and incremental drifts. Three different performance perspectives (accuracy, time, and delay) were analyzed;
* A free online tool to generate synthetic streams with different concept drift setups;  
The remainder of this paper is organized as follows: Section 2 provides further details about stream mining and concept drift, while Section 3 presents the related work. In Section 4, we describe the steps to implement the experiment alongside the used algorithms. Section 5 presents the results, while the discussion and open issues are addressed in Section 6. Section 7 presents the main conclusions and directions for future work.
## 3 Signal Level Compatibility Analysis
### Analysis of the Effect of Interference on the Tracking Performance of BeiDou  
#### 3.2.1 Carrier Tracking Performance  
The phase measurement error sources of Phase-Locked Loop (PLL) include phase jitter and dynamic stress error. The dynamic stress error is very small compared to the phase jitter error when the equivalent carrier-to-noise ratio satisfies the signal capture, so it is negligible.  
The error sources that cause phase jitter are mainly categorized into thermal noise, jitter in the oscillation frequency caused by mechanical chatter, and Allan squared error. In practice, since the source of PLL phase-locked loop chattering may be instantaneous, only the carrier tracking error caused by thermal noise is generally considered. The carrier tracking error due to thermal noise and interference can be expressed as:  
\[\sigma_{PLL}=\frac{180^{\circ}}{\pi}\sqrt{\frac{B_{L}}{C/N_{0}}\bigg{(}1+\frac{ 1}{2 TC/N_{0}}\bigg{)}} \tag{5}\]  
\(B_{L}\) represents the phase-locked loop noise bandwidth; \(T\) indicates the pre-detected integration time; \(C/N_{0}\) is the carrier-to-noise ratio.  
Figure 6 shows the carrier tracking error plot versus the equivalent carrier-to-noise ratio for different pre-detection integration times where the receiver carrier loop noise bandwidth BL is constant and equal to 20 Hz. From the above simulation, it can be seen that the carrier tracking error decreases with the increase in the equivalent carrier-to-noise ratio. When the tracking error threshold is \(15^{\circ}\), under the simulation conditions with different pre-detection integration times, the longer the pre-detection integration time, the smaller the carrier tracking error, and the smaller the interference on the carrier tracking performance.
## 1 Introduction
Hence, this method would require the presence of a WIM system onsite for classifying trucks according to their static weights and axle spacing, as well as locating them on the bridge based on their speed. An eigenvalue analysis conducted on a finite element model of the bridge is computationally very fast, and allows a quick picture of the expected impact of damage on the forced frequency pattern of the fleet to be gathered. When carrying out the eigenvalue analysis on a vehicle fleet, it is possible to define probability distributions for the values of the forced frequencies. Moreover, these distributions will change depending on the position of the vehicle and on the location and severity of the damage, hence allowing for the creation of a database. In the absence of field measurements, transient analysis was employed to simulate the acceleration of data collected on the bridge. The transient analysis was performed for a traffic fleet independent from the calibration fleet used in the eigenvalue analysis. The Short-Time Fourier Transform (STFT) was applied to the time-history of accelerations to extract the time-varying frequencies associated with each vehicle crossing. Finally, specific values of the transient forced frequencies were compared to the distributions from eigenvalue analysis to establish whether damage has occurred or not, where, and to what extent. The Mahalanobis distance was used to infer the probability distribution of the database to which a fleet is more likely to belong. Section 2 describes the research methodology, including the fundamental equations for the VBI model, as well as the main tools used, i.e., STFT and Mahalanobis distance. Section 3 provides details on the specific bridge and traffic fleet models used for testing the methodology. Section 4 illustrates the variation of the forced frequency of the 1 st mode of the bridge, and how the probability distribution of forced eigenfrequencies can be obtained from a traffic fleet. Section 5 shows the challenges associated with obtaining the forced frequencies from transient analysis due to the effect of the road profile, vehicle speed, and damping, and how local and global damage affects the forced frequencies. Section 6 presents results for two damage scenarios in two bridge spans traversed by a traffic fleet at two different speeds. Finally, Section 7 draws conclusions and makes suggestions for future research.
# Balanced Team Formation Using Hybrid Graph Convolution Networks and MILP
## 4 Proposed GCN-MILP Model
### MILP Model
#### 4.2.1 Decision Variables  
* \(x_{ij}\): binary variable, 1 if player \(i\) is assigned to team \(j\), 0 otherwise.