esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
One of the pivotal components of the EU DEMO fusion reactor is the Breeding Blanket (BB), and its development plays an essential role in the DEMO fusion reactor research activities carried out by the EUROfusion consortium [1, 2]. In this context, the Water-Cooled Lead Lithium Breeding Blanket (WCLL BB) is one of the two main candidates as a driver blanket for the EU DEMO fusion reactor.  
As already seen in previous works [3, 4], the Cap regions of a Blanket Segment are the most critical in terms of loads and requirements they are subjected to, and their design needs dedicated study. In this work, an exploratory thermo-mechanical assessment of the Bottom Cap (BC) region of the EU DEMO WCLL Central Outboard Blanket (COB) segment is reported. In particular, a similar approach to that already followed for the Top Cap (TC) region has been adopted [4].  
Starting from the reference geometric layout [5; 6], a simplified structural analysis of the single elementary cell housing the Cap, but not including the manifolds region, has been conducted in order to provide a preliminary investigation of its structural performances under an externally calculated thermal field. On the basis of this preliminary assessment, a set of thermo-mechanical analyses has been launched, updating each time the layout of the cell housing the BC, adopting a more detailed geometric layout encompassing the manifolds regions and two cells adjacent to that housing the BC plate. Following a series of progressive modifications, the objective of the activity has been to pave the way for future design works, defining the space of the possible configurations in view of the design requirements. In particular, from the thermal standpoint, the temperature of the structural components, composed of Eurofer, must not exceed the suggested limit value of 550 \({}^{\circ}\)C [5]. Instead, the structural performances have been evaluated in light of the RCC-MRx structural design code [7]. The loading conditions taken into account for the evaluation are related to the steady state Normal Operation (NO) and the Over-Pressurization (OP) scenarios, the first taking into account the nominal operative condition, the latter, instead, relating to an off-normal condition resulting from an in-box LOCA event, causing the pressurisation of the whole component, due to a leak of coolant.  
To this purpose, a theoretical-numerical approach based on the Finite Element Method (FEM) has been followed, and the quoted Ansys commercial code has been used. The obtained results are herewith presented and critically discussed.
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 4 Methodology
### Minimum Distance between C-UE and V-UE Transmitter
\tag{13}\]  
Owing to fading affects randomly influencing the LTE-A signal quality with time, geographical position or radio frequency, we take into account this random property in the parametrization of the channel link. As we consider both the fast fading due to multi-path propagation and slow fading due to shadowing, the channel gain between C-UE \(n\) and the eNodeB \(B\) can be expressed as  
\[h_{nB}=K\cdot\beta_{nB}\cdot\zeta_{nB}\cdot d_{nB}^{-\alpha} \tag{14}\]  
where \(K\) is a constant determined by system parameters, \(\beta_{i,B}\) is fast fading gain with exponential distribution, \(zeta_{i,B}\) is the slow fading gain with log-normal distribution, \(\alpha\) is the path-loss exponent, and \(d_{(i,B)}\) is the distance between C-UE \(n\) and the eNodeB. In the same way, we can define the channel gain of V2V communication as \(g_{m}\), the channel gain between the transmitter \(m\) of the V2V pair and the eNodeB as \(h_{mB}\), and the channel gain between C-UE \(n\) and V-UE transmitter \(m\) as \(h_{mn}\). Thus, having the minimum transmission powers for C-UE and V-UE, the minimum distance between C-UE and V-UE transmitter that can satisfy all the constraints can be calculated getting the  
Figure 3: Representation of pairing constraints. (**a**) possible pair; (**b**) impossible pair. value of \(h_{nm}\) from \(P_{A}^{c}\) and \(P_{A}^{d}\), and next, finding the values of \(d_{nm}\). Knowing the minimum distance it is easier for the eNodeB to pairing C-UEs and V-UE. \[L_{nm}=\left\{ \begin{array}{ll}\left(\frac{k_{Bnm}c_{nm}d_{nm}^{d}c_{min}^{d}P _{max}^{c}h_{mB}}{P_{max}s_{nB}s_{n}-\sigma^{c}c_{min}^{2}\left[s_{n}-c_{min}^{ 2}h_{mB}\right]}\right)^{\frac{1}{4}}&\text{if }\frac{P_{max}^{c}s_{nB}}{\sigma^{c}+P_{max}^{c}s_{nB}} \geq\varepsilon_{min}^{c}\\ \left(\frac{k_{Bnm}c_{nm}c_{nm}^{d}c_{min}^{c}\left[P_{max}^{c}h_{mB}+\sigma^{ 2}\right]}{s_{nB}\left[P_{max}^{d}s_{nB}-\sigma^{2}\right]}\right)^{\frac{1}{2} }&\text{if }\frac{P_{max}^{c}s_{nB}}{\sigma^{c}+P_{max}^{d}s_{nB}} \geq\varepsilon_{min}^{c}\end{array} \right.\]
# Single-Switch Non-Isolated Resonant DC-DC Converter for Single-Input Dual-Output Applications
## 2 Proposed Single-Switch SIDO ZVS-QRC Configuration Description  
From conventional ZVS-QR \(M\)-type half-wave Cuk and SEPIC structures, shown, respectively, in Figures 1 and 2, it can be observed that, from a conceptual and analytical point of view, Cuk and SEPIC configurations are very similar and also present some of the advantages and limitations of resonant configurations described above. Both provide zero-voltage switching on the power switch (\(Q\)), use the same number of components, namely two inductors (\(L_{1}\) and \(L_{2}\)), a freewheel diode (\(D\)) and a link capacitor (\(C_{1}\)) with the same resonant tank (\(L_{R}\) and \(C_{R}\)); both also provide the same conversion ratio, but SEPIC is a positive output converter, while Cuk is a negative output converter; and both also have a single power switch connected to the negative terminal of the DC source (\(V_{g}\)).  
Figure 1: ZVS-QR \(M\)-type half-wave Cuk converter.  
If the configurations shown in Figures 1 and 2 are compared, it can be seen that both converters have an identical front part that can be combined as a common node and that it is possible to combine the two configurations to implement a SIDO converter, as shown in Figure 3. The combined structure shows a common \(L_{R}\)-\(C_{R}\) tank, a common input inductor (\(L_{1}\)) and a single power switch (\(Q\)) connected to the negative terminal of the DC generator (\(V_{g}\)), and the resulting converter is able to provide a bipolar output voltage. This can be an interesting solution for many applications requiring small size and high power density. The main advantage of the proposed configuration is that it allows the implementation of bipolar symmetric outputs with only one controllable power switch and soft-switching techniques.
## 4 Experimental Validation  
To demonstrate the applicability and accuracy of the proposed ISCV-ViT method, two case studies are performed for experimental verification. One is on a public dataset, which is from the bearing dataset of Paderborn University (PU) [50], and the other is on the laboratory bearing dataset of Shenzhen Technology University (SZTU). Furthermore, the diagnostic model is evaluated according to the experimental results. To perform signal processing, the single-phase current images of the current signal are extracted to compare with the proposed ISCV time-domain images. In addition, to illustrate the superiority of the ISCV-ViT model at the level of algorithm diagnosis, comparative experiments are conducted with the standard ViT and ResNet50 with residual connections. The specific hyperparameter settings of the above three benchmark models are shown in Table 2.
# Utilization of Polymeric Materials toward Sustainable Biodiesel Industry: A Recent Review
## 3 Polymeric Catalysts
### Chemically Synthesized Polymeric Catalysts
\begin{table}
\begin{tabular}{l l c c c c} \hline \multirow{2}{*}{**Polymeric Catalyst**} & **Biodiesel** & \multirow{2}{*}{**Yield (\%)**} & \multicolumn{3}{c}{**Reusability**} & \multirow{2}{*}{**Reference**} \\ \cline{3-4}  & **Feedstock** & & & **Number of Cycles** & & **Yield (\%)** \\ \hline FDU-15-SO\({}_{3}\)H & Soybean oil & 99.0 & 4 & 90 & [63] \\ PDVB-IL & Waste oils & 99.0 & 6 & 89 & [64] \\ Resin-N\({}_{3}\)=P(MeNCH\({}_{2}\)-CH\({}_{2}\))\({}_{3}\)N & Soybean oil & 100 & 5 & 90 & [65] \\ Poly (butanesulfonic acid pyrrole) & Waste oils & 98.1 & 10 & 97.5 & [66] \\ OALL-100(Fe)@DAILs & Oleic acid & 93.5 & 6 & 86 & [67] \\ HPW@MIL-100(Fe) & Acetic acid & 96.3 & 5 & 95.5 & [68] \\ Fe\({}_{3}\)O\({}_{4}\)@HKUST-1-ABILs & Soybean oil & 92.3 & 5 & 85 & [69] \\ HZnPS-1 & Oleic acid & 90 & 5 & 80 & [70] \\ Oligocat & Swine tallow & 96.5 & 3 & 96.5 & [9; 10] \\ Pd/HPS * & stearic acid & 97 & - & - & [71] \\ \hline \end{tabular}  
* This polymer-metal catalyst was used to catalyze hydrodeoxygation reaction in purpose of synthesizing hydrocarbon-based biodiesel n-heptadecane.  
\end{table} 
Table 4: Some of the chemically synthesized polymeric catalysts.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## 1 Introduction
### Control of Semi-Active Suspension  
Control algorithms that are dedicated to MR dampers are usually decomposed into the lower force control layer and the upper vibration control layer. The force control can operate in the open or closed loop. The open-loop takes advantage of the invertible MR damper model, while the closed-loop approach requires a force feedback signal. Force measurements are widely used in mechanical system starting from small-scale elements of vehicle suspension [35] to strain gauge that can be applied in mechanical constructions from suspension to a large-scale bridge conveyor of bucket wheel excavator [36].  
The application of MR dampers in vehicle suspension allows for changing the suspension settings from soft to hard during the drive. However, the Skyhook algorithm that is presented in [17] is considered as one of the first semi-active control algorithms, simultaneously robust and widely tested, e.g., in [8] for an all-terrain vehicle (ATV). It also serves as a reference for other algorithms. On the other hand, the Groundhook algorithm described, e.g., in [37] generates a desired force that is inversely proportional to the wheel vertical velocity, whereas Skyhook generates force that is related to the vertical velocity of the body.  
Applications of MR dampers can be also limited to the suppression of a seat vibration only. The real-time control of the MR damper applied for a driver seat with on-off and continuously variable control was presented in [13]. Further, the results of vibration suppression for an operator cabin in bucket wheel excavators were presented in [38], where the authors proposed an iterative learning method for optimal control.  
Fuzzy control serves as an alternative control approach that can be applied for MR dampers [39], where an inverse MR damper is not required. Other authors presented, in [40], a control system of a vehicle model with MR dampers that was integrated with braking and front steering subsystems. Here, the study is more complex and takes into account both suspension dynamics and tire-road interaction in the form of a Magic Formula tire model. However, the authors focused their study on vehicle vibration that was induced by road manoeuvres, while road irregularities as well as the vertical dynamics of the wheel were not taken into account.
## 5 Simulation Results and Discussion
### Case 4: Minimization of Total Generation Cost with Valve Point Effect
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Parameters** & \begin{tabular}{c} **Min.** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Max.** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **PSO** \\ \end{tabular} & \begin{tabular}{c} **GWO** \\ \end{tabular} & \begin{tabular}{c} **HPSO-GWO** \\ \end{tabular} & \begin{tabular}{c} **SHADE-SF** \\ \end{tabular} & \
\begin{tabular}{c} **BMO** [46] \\ \end{tabular} &
\begin{tabular}{c} **GOA** [46] \\ \end{tabular} \\ \hline \multirow{4}{*}{Generator} & QTG1(MVAv) & \(-\)20 & 150 & \(-\)10.613 & \(-\)10.334 & \(-\)13.645 & \(-\)5.100 & 18.050 & \(-\)20.000 \\  & QTG2(MVAv) & \(-\)20 & 60 & 18.262 & 14.144 & 22.185 & 13.073 & \(-\)20.000 & 60.000 \\  & QWT5(MVAv) & \(-\)30 & 35 & 25.050 & 27.894 & 25.071 & 22.552 & 30.344 & \(-\)5.628 \\ \multirow{4}{*}{powers} & QTG8(MVAv) & \(-\)15 & 40 & 40.000 & 40.000 & 40.000 & 40.000 & 40.000 & 40.000 & 40.000 \\  & QWT11(MVAv) & \(-\)25 & 30 & 19.496 & 17.909 & 20.023 & 30.000 & 27.869 & 19.217 \\  & QSPV13(MVAv) & \(-\)20 & 25 & 20.487 & 23.054 & 19.259 & 16.498 & 19.998 & 25.000 \\ \hline \multirow{4}{*}{Objective} & Gen. cost & \multirow{4}{*}{-} & \multirow{4}{*}{781.321} & \multirow{4}{*}{780.924} & \multirow{4}{*}{780.587} & \multirow{4}{*}{781.293} & \multirow{4}{*}{781.652} & \multirow{4}{*}{785.711} \\  & (\$/h) & & & & & & & & \\ \cline{1-1}  & \(\$/h\) & & & & & & & & \\ \cline{1-1}  & \(\$/h\) & & & & & & & & \\ \cline{1-1}  & Carbon tax & - & - & - & - & - & - & - & - \\ \cline{1-1}  & V.D (p.u.) & - & - & 1.021 & 1.066 & 0.978 & **0.466** & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 20: Generator bus voltages comparison (Case 4). Figure 21: Generators real power comparison (Case 4). Figure 22: Generators reactive powers comparison (Case 4).
# Evaluating the Efficacy of Segment Anything Model for
## 4 Result
### Same Study Area Size at Different Resolutions
Turning to the results of unsupervised segmentation, except for Graph Set 2 in Figure 14, the other three graph sets show great instability. Segmentation accuracy performs poorly as resolution decreases. Without training points, unsupervised segmentation results are relatively unable to achieve the segmentation accuracy of conditional segmentation. Errors manifest in the fineness of segmentation, especially noticeable as the resolution decreases in Graph Sets 3 and 4 in Figure 14, where fewer and fewer regions can be segmented, even with clearer boundaries. Simultaneously, there is progressively less segmentation for finer parts, highlighting a significant limitation of unsupervised segmentation.
# Cooperative Terrestrial-Underwater FSO System: Design and Performance Analysis
## 3 Channel Model
### Composite FSO-TU Channel  
This section describes how the CDF of the FSO-TU system is obtained to address the performance evaluation of the end-to-end hybrid network. Accordingly, once the statistical PDFs of the terrestrial and underwater channels are obtained, this information can now be used to develop the expressions for the CDF of the hybrid AF-based FSO-TU system. Thus, we have  
\[F_{\gamma}(\gamma)\approx\text{Pr}[\gamma_{1}<\gamma\bigcup\gamma_{2}<\gamma] =F_{\gamma_{1}}(\gamma)+F_{\gamma_{2}}(\gamma)-F_{\gamma_{1}}(\gamma)F_{ \gamma_{2}}(\gamma), \tag{21}\]  
where \(\gamma_{1}\), and \(\gamma_{2}\) are the signal-to-noise ratios of the terrestrial and the underwater links, which are defined in Section 2, and \(F_{\gamma_{1}}(\gamma_{1})\) and \(F_{\gamma_{2}}(\gamma_{2})\) are their corresponding CDFs. These two functions are directly obtained by integrating their PDFs which can be calculated from the Equations (15) and (18), taking into account that \(f_{\gamma_{1}}(\gamma_{1})=f_{h_{1}}(\gamma_{1}/\gamma_{a})\) and \(f_{\gamma_{2}}(\gamma_{2})=f_{h_{2}}(\gamma_{2}/\gamma_{u})\), respectively. Thus,  
\[F_{\gamma_{1}}(\gamma_{1})=\frac{\xi_{\rho}^{2}}{\Gamma(\alpha)\Gamma(\beta)} G_{2,4}^{3,1}\Bigg{(}\frac{\alpha\beta\gamma_{1}}{A_{0}h_{l}\gamma_{a}} \bigg{|}\begin{array}{c}1,1+\xi_{\rho}^{2}\\ \xi_{\rho}^{2},\alpha,\beta,0\end{array}\Bigg{)}, \tag{22}\]  
\[F_{\gamma_{2}}(\gamma_{2})=1-\exp\left[-\Bigg{(}\frac{\gamma_{2}}{\Lambda \gamma_{u}h_{lg}}\Bigg{)}^{K}\right], \tag{23}\]  
where the cumulative probability function in Equation (22) is obtained by using Equations (07.34.21.0085.01) and (07.34.21.0084.01) [36]. In a similar fashion, Equation (07.34.16.0001.01) [36] is used in order to develop the equation one step further, allowing simplification of the result by adding \((\alpha+\beta)/2\) to the obtained \(a_{i}\) and \(b_{i}\). Hence, by inserting Equations (22) and (23) into (21), the resulting overall CDF is obtained as  
\[F_{\gamma}(\gamma)=1-\exp\left[-\Bigg{(}\frac{\gamma}{\Lambda\gamma_{u}h_{lg }}\Bigg{)}^{K}\right]\times\Bigg{\{}1-\frac{\xi_{\rho}^{2}}{\Gamma(\alpha) \Gamma(\beta)}G_{2,4}^{3,1}\Bigg{(}\frac{\alpha\beta\gamma}{A_{0}h_{l}\gamma_ {a}}\bigg{|}\begin{array}{c}1,1+\xi_{\rho}^{2}\\ \xi_{\rho}^{2},\alpha,\beta,0\end{array}\Bigg{)}\Bigg{\}}. \tag{24}\]
## 4 Simulation Results  
The simulation results from different contingency analyses are presented investigating different parts of the designed system and verifying the proposed topology in a MATLAB simulation environment. In the first stage, the PV module is simulated at STC and three PSCs with multiple peaks (LMMPs and GMPP). In the second stage, the proposed system, which consists of the converter, controllers, and MPPTs, is verified using the simulation results. The INC-MPPT, P&O-MPPT, and proposed MPPT are compared to investigate their performance and robustness to interference. Results show that the proposed MPPT system is a more robust and highly efficient technique for the PV system than the two other MPPT techniques. The solar irradiation patterns are kept similar for the three algorithms. First, simulation results are obtained using the algorithms in the STC at 1000 W/m\({}^{2}\) and 25 \({}^{\circ}\)C. The sampling time and simulation time for the MPPT controller are selected as 0.02 s and 1 s, respectively. The switching frequency of the converters is chosen as 10 kHz. The step size of the duty cycle in both the conventional algorithms and the proposed algorithm is 0.0001.
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 2 Methodology
### Research Objective  
In disciplines connected to artificial intelligence, such as visual multi-object tracking, deep learning, and autonomous driving, significant progress has been made in recent years. It becomes more challenging to keep up with developments or enter the area as a newcomer, as is the case with any rapidly expanding field. A thorough analysis of the difficulties, data sources, and algorithms for visual multi-object tracking in autonomous driving scenarios has not yet been published, despite multiple reviews on feature sub-problems. By reviewing the most cutting-edge data sets and algorithms, this review aims to close the knowledge gap between learners and the field of automatic driving visual multi-object tracking. Additionally, the approaches included in this evaluation only require monocular cameras as opposed to complicated sensor fusion, which is likely to pave the way for the quick development of more affordable and secure ways to deploy automatic driving systems.
# Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation
## 6 Conclusions  
We propose a novel deep learning framework, Spider U-Net, for 3D BVS that incorporates an RNN component into the 2D U-Net baseline. The core element is the weft path that unites the broken context between consecutive images by applying convolutional LSTM in a bidirectional way. This helps the entire model infer accurate segmentation masks while taking into consideration adjacent image features. Moreover, a training strategy named striding stencil is proposed to train Spider U-Net. Our proposed model was evaluated on three different datasets and outperformed all comparative models on DSC. This shows that utilizing the inter-slice connectivity with LSTM improves the performance for 3D BVS tasks. In addition, we experimentally validated that our network component placement is more effective than previous models.  
A future study to validate Spider U-Net in a real clinical scenario is planned. In order to segment the intracranial artery and diagnose stenosis and occlusion simultaneously, we will implement a multi-task model by attaching an auxiliary classifier to Spider U-Net. We will then compare the diagnostic results with invited radiologists.  
Conceptualization, K.L., L.S. and K.J.L.; methodology, K.L.; software, K.L.; validation, K.L.; formal analysis, L.S. and T.K.; investigation, K.L. and L.S.; resources, L.S., T.K. and K.J.L.; data curation, L.S. and T.K.; writing--original draft preparation, K.L.; writing--review and editing, K.L. and K.J.L.; visualization, K.L.; supervision, T.K. and K.J.L.; project administration, L.S. and K.J.L.; funding acquisition, L.S. and K.J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (No. 2017R1D1A1B03033610) and by grant (no. 18-2018-016) from the SNUBH Research Fund.  
Data Availability Statement: Not applicable.  
Acknowledgments: We thank [PERSON] and [PERSON] for providing technical assistance to data acquisition and annotation.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Findings and Discussion
### Explainable Frameworks in XAI
#### 4.3.4 What Is GRAD-CAM and What Is Its Applicability to IDSs for ICVs?  
A method called Gradient-weighted Class Activation Mapping (GRAD-CAM) [110] creates a heat map for each class from a single image. Grad-CAM then generates a localization map that discriminates classes. The framework uses the feature maps produced by the final convolutional layer of a CNN. This is model-specific, local-based, and post hoc [128]. GRAD-CAM finds usage in the visualization of deep learning (such as CNN) outputs. GRAD-CAM achieves this by using the features obtained from the last convolutional layer of a CNN, for example. The weighted combination of feature maps followed by the ReLU activation function is used to obtain a precise heat map. Although GRAD-CAM is one of the most popular methods for explaining deep neural network decisions, it violates key axiomatic properties, such as sensitivity and completeness. Integrated gradients are an axiomatic attribution method that aims to cover this gap [130]. In 2020, GRAD-CAM was reported to have a challenge in its ability to reflect the model's computation because of the gradient average steps. These steps are thus unreliable in prediction. To solve this, a modified CAM was proposed, known as High-Resolution Class Activation Mapping (HiResCAM), for faithful explanations of CNN [131].