esa-sceva/satcom-chunk-collection huggingface dataset:
# Immuno-PET for Glioma Imaging: An Update
## 4 Targets for Immuno-PET
### Prostate-Specific Membrane Antigen (PSMA)  
PSMA (prostate-specific membrane antigen) is a type II transmembrane glycoprotein that includes zinc and works as a metal enzyme. It is well-known for use in the molecularly targeted approach to prostate cancer (PC). PSMA has been widely researched throughout the last three decades. Previously, immunoscintigraphy with 111 In-capromab pendetide, an mAb-targeting PSMA, was used to image PC recurrence and metastases. However, its low sensitivity and specificity were attributable to the fact that it targeted the PSMA intracellular domain, which is only present in dying or necrotic cells. The discovery of radiolabeled urea-based PSMA inhibitors targeting PSMA's extracellular enzymatic region was a watershed moment in the field. This innovation cleared the way for the development of a plethora of PSMA inhibitors labeled with radionuclides, appropriate for use in both PET imaging ([68 Ga]-PSMA-11 or [18F]-PSMA-1007) and therapy ([17 Lu]-PSMA-617). PSMA, despite its name, is not just associated with prostate cancer. Antibodies targetingthis biomarker react strongly with neovascularization in a wide range of cancers, including colorectal and breast cancer. Moving on to gliomas, immunohistochemistry has verified PSMA expression in high-grade gliomas. Furthermore, PSMA-PET has shown promise in preliminary investigations on GBM diagnosis and recurrence [49; 50]. From this perspective, it is important to note that PSMA-PET conducted using PSMA inhibitors should not be classified as immuno-PET because it does not utilize radiolabeled antibodies. However, in a different approach, a humanized mAb known as huJ591 (J591), which specifically targets the extracellular domain of PSMA, has been conjugated with various radiometals such as 111 In and \({}^{89}\)Zr. This conjugate has been subject to investigation as a potential imaging agent in a phase I/II clinical study carried out in PC patients [51]. However, scientific data on the potential use of immuno-PET with [\({}^{89}\)Zr]-J591 for glioma imaging remain limited. Recently, [PERSON] et al. published a clinical report detailing the detection of grade II oligodendroglioma (1p/19q co-deleted, IDH mutant) using [\({}^{89}\)Zr]-J591 immuno-PET [52]. The patient, who had undergone multiple treatments (surgery, chemotherapy, radiation therapy, and IDH inhibitor AG-120), showed MRI findings suggestive of disease progression. Serial immuno-PET scans targeting PSMA were conducted on days 1, 2, and 6 post-injection, and these scans were fused with axial post-contrast T1-weighted MRI images. They revealed a nodular enhancing lesion located anterior and inferior to the resection cavity. The authors propose that immuno-PET, owing to the extended physical half-life of \({}^{89}\)Zr, not only holds promise for glioma diagnosis, but also offers the potential for precise dosimetric calculations through serial PET/CT scans, with implications for future radioimmunotherapeutic approaches.  
Table 1 provides a summary of the key conclusions derived from research articles on the use of immuno-PET in gliomas.
## 4 Performance Evaluation
### Simulation Setup  
The simulation scenario encompasses a rectangular street area measuring 3000 \(\times\) 2400 m\({}^{2}\) and features 30 junctions interconnected by 49 multi-lane, two-way roads, as illustrated in Figure 11 below. Each road is populated with randomized vehicles, with only one designated as the source vehicle. The source vehicle traverses the road randomly, dispatching packets to the destination as it moves. The destination is at one of the junctions, while the two rectangles marked with a diagonal pattern represent closed roads inaccessible to vehicles.  
The simulation parameters used for performance evaluation are outlined in Table 6.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 2 Methodology  
The control algorithms of lane change maneuvers generally contain three consecutive parts: decision-making system for the start of lane change, generating a reference path for the vehicle to follow, and real time path re-planning during the overall process. To take the analysis of algorithm, the space between the two vehicles on the road is defined as a traffic gap. The host vehicle \(V_{H}\) is controlled to perform a lane change maneuver, and it will move from one traffic gap in the current lane to another traffic gap in a neighboring lane. To simplify the algorithm design, the lane change maneuver is taking place in adjacent lanes, without the consideration of other specific maneuvers, such as crossing multiple lanes at once.  
Figure 1 shows the flowchart of the lane change algorithm, which features three major parts: evaluation of available traffic gaps, trajectory planning and trajectory correction. The lane change algorithm is carried out in a loop cycle for the real-time control, and one cycle is executed per time step \(t_{s}\). For each cycle, monitored traffic information is updated, and input into the lane change algorithm for calculation.  
When the host vehicle \(V_{H}\) is controlled for a lane change maneuver, the algorithm cycle will start as the control diagram in Figure 1. Firstly, by using the monitored and predicted traffic information, the algorithm will evaluate available traffic gap for lane change (Part A). When the target traffic gaps better than the current driving gap are determined, the algorithm attempts to generate LC trajectory for these gaps with relative constraints. The traffic gap with successful trajectory planning and highest evaluation index will be selected for lane change. If none gap is successful, \(V_{H}\) stays in its original gap, otherwise, an optimal trajectory for lane change is determined for \(V_{H}\) to follow (Part B). During the lane changing, the viability of current LC trajectory is constantly monitored, and if it is no longer feasible, a trajectory correction is required. Thus a trajectory re-planning control is conducted to satisfy the changed constraints (Part C). The time cost for one calculation cycle is related to the current stage of lane change maneuver. Based on the hardware environment, three stages shown in Figure 1 have different calculation time. To satisfy the real-time requirement, the time cost for one cycle should be controlled below 50 ms. All procedures above will be explained in the following Sections.
## 3 Results
### Biofilm Production  
Of the 56 strains tested on CRA medium, a total of 12 tested strains (6 CNS strains and 6 CPS strains) showed changes in the color of the medium indicating the ability to produce biofilm. The number of isolates with mucus production properties was in the CNS group (6 strains) and in the CPS group (6 strains).  
By assessing biofilm production using the spectrophotometric method by measuring absorbance according to [PERSON] et al. (2018), one strain (1.77%) showed the ability to intensively produce biofilm (OD \(>\) 0.8); 30 strains (53.57%) were moderate (0.4 \(<\) OD \(<\) 0.8), while 10 (17.87%) were weak (0.2 \(<\) OD \(<\) 0.4). The remaining 15 strains (26.80%) did not show the ability to produce biofilm (OD \(\leq\) 0.2). Among the CNS strains tested using the spectrophotometric method, 19/28 strains showed the ability to produce biofilm--one strain showed an intense ability (3.57%), 14 (50%) a moderate ability, and 4 (14.28%) a weak ability. The inability to produce biofilm was found in 9 cases (32.15%). Analyzing the species affiliation of CNS strains, in the group of strains belonging to _S. aureus_, 13 strains with the ability to produce biofilm were found (1 strain intense, 10 strains moderate, and 2 strains weak). In the group of CNS strains belonging to the _Staphylococcus_ genus, the ability to produce biofilm was confirmed in 6 cases (4 moderate and 2 weak). Among the CPS strains tested using the spectrophotometric method, the ability to produce biofilm was demonstrated by 22/28 strains, including 16 strains with a moderate ability to produce biofilm and 6 with a weak ability. In the case of 6 strains, the ability to produce biofilm was not demonstrated in the test performed using the spectrophotometric method.  
The tests carried out for the presence of three genes responsible for biofilm production (_ica_, _agr_, _sacs_) did not reveal the _agr_ gene in any of the 56 CNS and CPS isolates tested. The _ica_ gene was detected in 39 tested isolates (69.64%) and the _sacs_ gene in 38 (67.86%). It was determined that 15 isolates did not contain any of the tested genes, which constituted 26.79% of all tested strains.  
Of the 28 CPS isolates tested, all of them contained the _ica_ and _sacs_ genes. The _agr_ gene was not found in the tested group of strains. Of the 28 CNS isolates tested, 11 strains contained the _ica_ gene (39.29%) and 10 strains contained the _sacs_ gene (35.71%). CNS isolates had significantly lower numbers of both the _ica_ 11 gene (39.29%) and the _sacs_ 10 gene (35.71%). Detailed results are summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline
**Strain Type** & **Gene Pattern** & **Strains** \\ \hline \multirow{5}{*}{CPS} & _tetK_ & 75/2, 41/1, 73/1, 97/3, 87/3, 15/3, 87/1, 74/3, 15/2, 41/3, 51/2, 97/1, 75/3 \\  & _vanB_ & 13/3, 75/2, 72/1, 51/3, 87/2, 2/1, 97/3, 41/2, 87/1, 51/1, 74/3, 51/2, 97/1, \\  & _tetK_, _vanB_ & 75/2, 97/3, 87/1, 74/3, 51/2, 97/1, 75/3 \\  & _blaZ_, _vanB_ & 72/1, 74/3 \\  & _tetK_, _vanA_ & 73/1 \\  & _tetK_, _blaZ_, _vanA_ & 73/1 \\  & _tetK_, _blaZ_, _vanB_ & 74/3 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._
# Application of Graph Theory for Blockchain Technologies
## 4 Relevant Concepts from Graph Theory  
We briefly recap some common graph metrics and analytical methods that would be relevant for blockchain networks [3]:  
1. Degree centrality identifies highly connected nodes that interact with many others in the network [45].
2. Betweenness centrality identifies nodes that bridge different communities, enabling the flow of information [46].
3. Closeness centrality measures how fast information from one vertex can reach others via minimum links [47; 48].
4. Clustering analyzes whether a network partitions into densely interconnected communities with sparse connections among them [49; 50].  
* Bridges, cut vertices, and articulation points are crucial connectors whose removal can fragment graphs [51; 52].
* Shortest paths, network diameter, and average path lengths indicate how easily nodes can reach each other [53; 54].
* Resilience evaluates a network's vulnerability to random failures or targeted attacks on nodes or edges [55].  
These graph properties are mirrored in blockchain platforms through influential miners, network partitions caused by regional regulations, bottleneck links that affect transaction speeds, and systemic risks from the failure of critical mining pools. Applying graph theory can, therefore, offer actionable insights to enhance the system's design, efficiency, and security [36].
### Motivation and Contribution  
Work from home has been promoted during the COVID-19 pandemic. The IoT can help healthcare providers monitor patients remotely, which can enhance patient care and decrease healthcare costs [23; 24]. Patients can use IoT devices to track their vital signs, including coronary heart rate, blood stress, and blood glucose range, and transmit these data to healthcare providers in real time. This can help healthcare providers detect health problems early and provide timely intervention. However, the sensitive clinical statistics of the patient are at high risk if the data are accessed remotely, due to the fact that the communication channels are insecure [25]. In this paper, the authentication issue is highlighted so that an authentic user can only monitor the health condition of the patient remotely over an insecure network. Authentication schemes stop unauthorized users from accessing network resources over vulnerable networks [26]. The available authentication schemes for IoT-based healthcare services cannot resist major network attacks. Moreover, existing schemes adopt complicated procedures that consume high computation and communication costs. So, such authentication schemes are not recommended for the resource-constrained environment that demands a lightweight scheme in terms of low computation and communication costs. Therefore, this paper introduces a lightweight remote user authentication scheme for IoT-based healthcare services by using a strong and simple symmetric session key exchange algorithm. The essential contributions of this research study are as follows:An efficient, cost-effective, and simple IoT-based secure platform is proposed in this research.
* The security model adopts a strong and simple symmetric session key exchange algorithm.
* The effectiveness of the plan against several types of known attacks is demonstrated.
* The proposed system model only allows the registered and verified users to be granted entry into the healthcare network.
* A detailed comparison analysis of the proposed model is conducted with the existing models to compute the cost of the proposed model with respect to communication and computation costs.
## 2 Materials and Methods
### Aging of Materials: The Long-Term Performance  
The aging of materials is a process in which their physical, mechanical and thermal properties change over time. Aging of the material can be artificially induced by irradiation (UV or infrared) or heat treatments. The heat treatment of materials can also increase the kinetic processes that take place in them, as well as the speed of chemical and physical degradation processes within the materials. In the case of thermal insulation materials, artificial aging is usually carried out by heat treatment at 343 K for a longer period [15; 16; 23]. The durability of the thermal conductivity factor plays an important role in the design, both from an operational and economic point of view. Aging at higher temperatures (up to 523 K) is important for materials that can also be used to insulate pipes transporting hot media (e.g., fibrous aerogel) like those in industrial settings or power plants [10].
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites  
A review of various DC-DC converters for satellite applications is provided in this section. For this review, the topologies are classified into non-isolated and isolated topologies. Various integrated converters (isolated and non-isolated) and topologies for high-power EP are also presented.
# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 3 Feature Selection Based on Ensemble Learning  
The incorporation of ensemble learning in feature selection endeavors to improve the robustness, comprehensiveness, and stability of models, thereby mitigating the risk of overfitting and enhancing predictive accuracy. These advantages position ensemble learning as a potent tool in time-series prediction tasks, thereby contributing significantly to the enhancement of model performance and reliability.  
Given a dataset \(D\), consisting of \(m\) samples and \(p\) features, we propose a set of feature selection methods \(M_{1},M_{2},\ldots,M_{n}\). Each method \(M_{
## 4 Intelligent Controller Design  
### Neural Network Structure  
Figure 3 depicts the architecture of the neural network used in this research. The neural network structure is a perceptron with three layers, which are an input layer, a hidden layer, and an output layer. The input layer has four inputs--stroke, aircraft acceleration, stroke velocity, and sink speed--where the former two come from the position sensor and accelerometer (Figure 1), and the latter two are derived from the sensor data. The hidden layer has n neurons, each of which constitutes an output vector (\(4\times 1\)). Thus, the hidden layer can be characterized by a weight matrix \(W_{1}\) (\(4\times 1\)) and a bias vector b\({}_{1}\) (\(1\times n\)). The output layer only involves the electric current that will be applied to the MR damper. Thus, the output layer can be represented by a weight vector \(W_{2}\) (\(n\times 1\)) and a scalar number b\({}_{2}\). The relationship between the output and input is given by:  
\[Output=f_{2}(f_{1}(W_{1}\times input+b_{1})W_{2}+b_{2}) \tag{13}\]  
where \(f_{1}\) and \(f_{2}\) are activation functions [13], which can be described by:  
\[\begin{array}{c}f_{1}(x)=\frac{e^{-x}-e^{-x}}{e^{-x}+e^{-x}}\\ f_{2}(x)=\left\{\begin{array}{cc}0&\text{if }x<0\\ x&\text{if }0\leq x\leq 1\\ 1&\text{if }x>1\end{array}\right.\end{array} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Aircraft Mass(kg)**} \\ \cline{3-6}  & & **560** & **600** & **640** & **680** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Sink speed \\ (m/s) \\ \end{tabular} } & 2 & \(\eta_{1}\) & \(\eta_{2}\) & \(\eta_{3}\) & \(\eta_{4}\) \\  & 2.5 & \(\eta_{5}\) & \(\eta_{6}\) & \(\eta_{7}\) & \(\eta_{8}\) \\  & 3 & \(\eta_{9}\) & \(\eta_{10}\) & \(\eta_{11}\) & \(\eta_{12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Efficiency symbols in the differing landing scenarios.  
The training problem of the neural network is to determine the unknown parameters, which are \(W_{1}\), \(W_{2}\), \(b_{1}\), \(b_{2}\), and \(n\), so that the cost function in Equation (11) is maximized. If an optimal electric current for this control target is known, supervised learning using the backpropagation algorithm [24] can be applied to solve this problem. However, the optimal electric current is also an unknown target. Thus, the training problem is solved by search methods using a genetic algorithm [13] or policy gradient estimation [25]. Figure 4 describes the learning process of landing gear equipped with an MR damper. It can be seen that the landing gear equipped with an MR damper in the touchdown phase is assumed to be an environment; the neural network controller is an agent that perceives its environment through sensors and acts upon that environment by applying the electric current on the MR damper. This agent uses a search method to choose the right actions to achieve the goal, i.e., maximizing the control target, and is accordingly called a goal-based agent [26].
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 3 Methodology
### Development of the Model
#### 3.2.2 Performance Metrics  
Assessing the model's performance is critical to data analysis and predictive modeling. Since each metric provides a unique perspective on different aspects of the model, it is crucial to evaluate more than one metric to achieve a comprehensive and well-rounded assessment of the model's performance, which will give a complete picture of the problem and allow us to gain insights into the strengths and weaknesses of the model.  
To quantify the predictive capabilities of the proposed model and enable meaningful comparisons among the different architectures and previous models published in the literature, it is advisable to use more than one performance metric. Researchers commonly employ complementary performance metrics, such as the coefficient of determination (R2) and root mean squared error (RMSE). The former measures the proportion of the variance in the output that is predictable from the input variables. Its value varies from 0 to 1, and the closer the value is to 1, the better the model's capability to explain the variability of the data. However, the latter measures the average error between the predicted and actual output values. Due to its squared term, it is more sensitive to outliers and moresignificant errors. The lower this metric is, the more accurate the predictions are. These metrics are valuable indicators of how well the models capture the data's variance and their predictions' accuracy. The mathematical formulae for R\({}^{2}\) and RMSE are provided in Equations (2) and (3).  
\[\text{R}^{2}=\frac{\sum_{\text{k}=1}^{n}(y-\text{y}^{\prime})^{2}}{\sum_{\text{ k}=1}^{n}(y-\text{y}_{\text{m}})^{2}}, \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y-y^{\prime})^{2}}{n}}, \tag{3}\]  
where n is the number of data points, y is the experimental data found in the literature, y\({}^{\prime}\) is the predicted data, and y\({}_{\text{m}}\) is the mean of the experimental data.
## 3 The Radiative Transfer Equation
### RTE Solution Considerations
#### 3.2.3 Analytical and Numerical Methods  
An Outline  
On the hard side of the RTE solution challenge, the RTE needs to account for inelastic scattering, scintillation, turbulence, and elastic scattering. Such a scenario requires that the RHS's third term be modified and represented using Equation (4).  
To solve the RTE numerically, we need to calculate the intensity and distribution of light at different points in the underwater medium. Based on the specific requirements of the communication scenario, appropriate numerical methods, such as a finite difference, a finite element, or MCSs, should be used to solve the RTE.
## 3 Reliability Modeling of PV Systems
### Reliability Modeling of a Central PV System
A three-phase two-level voltage-source inverter is used in this work. This inverter has six switches and diodes. This work does not treat the inverter as one black-box. Each component inside the inverter is considered a major factor in failure. The Reliability Block Diagram (RBD) [28; 29] is used in this project. This technique involves the use of switches and diodes connected in series, and this is known as series RBD as shown in Figure 4. The failure rate of the inverter (\(\lambda_{inv}\)) can be evaluated using Equation (17). Equations (18)-(21) are used to evaluate the failure rates of diodes (\(\lambda_{diade}\)) [25; 26; 27]. \[\lambda_{inverter}=6*\lambda_{diade}+6*\lambda_{switch} \tag{17}\]  
\[\lambda_{diode}=n*\lambda_{base}*\pi_{S}*\pi_{Q}*\pi_{E}*\pi_{T}*\pi_{C} \tag{18}\]  
Figure 5:0  
\[\pi_{S}=\left\{ \begin{array}{l}0.054----V_{S}\leq 0.3\\ V_{s}\!^{2.43}----0.3\leq V_{s}\leq 1\end{array} \right\} \tag{20}\]  
\[V_{s}=\frac{operated\ voltage}{rated\ voltage} \tag{21}\]  
where \(n\) is the total number of components in the system; \(\lambda_{base}\) is the base failure rate of diodes and is equal to 0.025 occur/year; \(\pi_{E}\) is equal to 6; \(\pi_{S}\) is the electric stress factor; the operating voltage and rated voltages are 607 V and 690 V, respectively; \(\pi_{Q}\) is equal to 5.5; \(\pi_{j}\) is the temperature stress factor; \(T_{j}\) is equal to 50 \({}^{\circ}\)C; and \(\pi_{C}\) is the contact construction factor, which is 1. Equations (22) and (21) are used to evaluate the failure rates of the switches (\(\lambda_{switch}\)) [25; 26; 27]. \[\lambda_{switch}=n*\lambda_{base}*\pi_{Q}*\pi_{E}*\pi_{T} \tag{22}\]  
\[\pi_{T}=\exp^{-1925*(\frac{1}{T_{j}+2\gamma}-\frac{1}{198})} \tag{23}\]  
where \(\lambda_{base}\) is the base failure rate of the switches, which is 0.012 occur/year; \(\pi_{E}\) is equal to 1; \(\pi_{Q}\) is equal to 5.5; and \(T_{j}\) is equal to 50 \({}^{\circ}\)C. The total failure rate of inverter is 0.095 occur/year. AC Circuit Breaker and Transformer  
The reliability database provided by [30] is used to calculate the probability of success and failure of these components. The reliability data of common PV system components are shown in Table 2. The probability of up and Down states of a central PV system is shown in Table 3. Then, Table 3 is combined with Table 1 to build the overall central PV system model. This model represents the multi-state model of the power output of a central PV system including the component failure factors. \begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Failure Rate (occur/year)** & **Repair Time (hour)** \\ \hline Capacitance & 0.0314 & 100 \\ Diode & 0.025 & 96 \\ LGBT & 0.012 & 513 \\ Circuit Breaker & 0.003 & 54 \\ Transformer & 0.006 & 168 \\ \hline \end{tabular}
\end{table} 
Table 2: Failure and Repair Data.