esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 4 Including NDN in the 3 GPP V2X Architecture
### Protocol Stack  
Figure 2 shows the proposed reference protocol stack for the V2X entities (i.e., vehicles, pedestrians, etc.) and the eNodeB.  
**V2X entities.** To support applications running on mature IP-based solutions and other V2X applications running on top of non-IP protocols, we envision a general protocol stack for UEs, which are provided with multiple networking solutions (e.g., IPv6, NDN, and WSMP) on top of the 3 GPP radio interfaces. Of course, some UEs in the V2X ecosystem (e.g., VRUs) may run just a subset of the aforementioned network layers, according to their capabilities.  
**eNodeB.** The NDN layer is deployed also at the eNodeB. Several works claimed the importance of augmenting the eNodeB with caching capabilities (not necessarily related to an ICN approach) to speed up data retrieval (e.g., [33; 35]). NDN offers caching natively and additional features that can help in optimizing the network resource allocation and limit the traffic volume, as detailed in the next Section. In [36], the eNodeB is enhanced with a forwarding table to take local forwarding decisions. The beauty of NDN is that a forwarding table can be natively included, and the relevant forwarding fabric and routines can be inherited with no need to rethink them from scratch.  
A further unique strength of our proposal and a clear departure from the existing literature is that the NDN forwarding fabric at the eNodeB is coupled with radio resource allocation procedures, which become _name-aware_, thus saving precious resources, as clarified in the following.
# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
## 4 Results and Discussion
### Detector Fusion  
Detector fusion refers to the combination of different detectors, all of them deciding about the same two hypotheses H1 and H0 with the aim of improving the individual  
Figure 27: SVM Precision–Recall curve.  
Figure 25: Fine-tuned VGG-16 Precision–Recall curveperformance. Detector fusion can be broadly classified into hard and soft fusion. In hard fusion, the individual binary decisions are combined to obtain a final decision. In soft fusion, some continuous statistics generated by the detectors are combined to obtain one fused statistics, which are considered to obtain the final decision [36]. In [37], authors proposed separated score integration (SSI), a new method based on alpha integration to perform soft fusion of scores in multiclass classification problems, which inspired the present study. We first convert the probability outputs of each classifier--VGG, ResNet, DenseNet, InceptionNet, EfficientNet, CNN, Random Forest, and SVM--into binary predictions by applying a threshold of 0.5. This converts each probability score into a definitive class label of 0 or 1. Next, we aggregate the binary predictions from all models using a majority voting scheme. Specifically, we average the binary predictions across all models and apply a rounding function to determine the final class label. If more than half of the models predict a class as positive, that class is chosen for the final combined prediction. Finally, we evaluate the accuracy of the combined classifier by comparing its predictions to the true labels, providing a measure of how well the integrated approach performs relative to the individual models. This method aims to leverage the strengths of each model to enhance overall classification accuracy. The fused classifier achieved an accuracy of 0.86, indicating that it correctly classified 86% of the test samples.
## 1 Introduction  
Monocytes are a type of mononuclear phagocyte that, as cells of the innate immune system, are part of the initial immune response to invading pathogens [1]. Under homeostatic conditions, monocytes patrol the blood stream, replenish macrophage pools in tissues, and are recruited rapidly to sites of infection and inflammation [2, 3]. Egress from the bone marrow is mediated mainly by C-C chemokine receptor 2 (CCR2), which binds to CCL2 secreted by cells in injured or infected tissue [4, 5].  
Once in the tissues, these cells shape the inflammatory milieu via expression of pro- or anti-inflammatory cytokines, phagocytic activity, and antigen presentation; they can also differentiate into macrophages [3, 6]. However, monocytes can trigger immunopathology when inadequately controlled [7, 8]. Murine monocytes are mostly identified as CD11b+ Ly6C+ Ly6C- cells and are commonly divided into two major subsets: proinflammatory CD11b+Ly6 Chi and anti-inflammatory CD11b+Ly6 Chi monocytes [9, 10]. They can be further subdivided according to the expression of CCR2 and CX3 CR1: Ly6 Chi CCR2 hi CX3 CR1i(proinflammatory) and Ly6C\({}^{\rm{lo}}\) CX3 CR1\({}^{\rm{hi}}\) CCR2- (anti-inflammatory), although the usage of CX3 CR1 is presently questioned [11]. Fate mapping and single cell approaches revealed the priming of Ly6C\({}^{\rm{hi}}\) monocytes towards a neutrophil-like monocyte (nMO) or dendritic cell-like monocyte (dcMO) phenotype under steady state conditions, and to _Cxcl10\({}^{+}\)_ and _Saa3\({}^{+}\)_ monocytes in pathogenic conditions [12; 13; 14]. In two murine models of liver infection, the absence of Ly6C\({}^{\rm{hi}}\) monocytes results in opposite disease outcomes. In the murine model of hepatic amebiasis, intrahepatic infection with the protozoan parasite _E. histolytica_ results (as in humans) in focal liver destruction (18). This type of liver damage, also termed amebic liver abscess (ALA), is almost abolished in mice with a _Ccr2_ knockout (_Ccr2-/_) or in mice in which monocyte were immunodepleted, suggesting an immunopathologic role for inflammatory monocytes (14). This liver damage also depends on inflammatory factors and can be inhibited by a specific blockade of TNF- or by general immunosuppression [15; 16]. By contrast, in the murine model of _L. monocytogenes_ infection, monocytes play a protective role, as indicated by the higher bacterial load and increased granuloma formation in the liver of _Ccr2-/_ mice (18).  
The aim of this study was to characterize liver Ly6C\({}^{\rm{hi}}\) monocyte subsets from both infection models with regard to their histological localization within infected liver areas, their transcriptome and surface marker expression, and their production of reactive oxygen species (ROS). This study revealed functionally distinct inflammatory Ly6C\({}^{\rm{hi}}\) monocytes in these two infection models and identified a novel combination of a transcription factor and surface markers that allow to distinguish proinflammatory monocyte subsets during inflammatory states.
# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 2 Architectural Foundations of IoT, Blockchains, and MIoT/IoUT
### Overview of MIoT and IoUT Systems
Recent studies have shown that integrating technologies like big data, cloud computing, AI, and blockchains can greatly enhance operational efficiency in marine environments ([PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018). As these technologies continue to advance, incorporating them into MIoT and IoUT frameworks is becoming more essential for effectively processing the large volumes of data these systems produce. Among these advancements, blockchain technology is a crucial enabler of secure data sharing and traceability within these networks. Furthermore, integrating these technologies is essential for developing real-time monitoring systems that will enhance future human-robot interactions and provide innovative solutions for managing marine resources ([PERSON] and [PERSON], 2018).
## 6 Solution Types/Algorithms
### Fuzzy Logic Based Algorithms  
Fuzzy logic is a mathematical discipline developed to present human reasoning in rigorous mathematical notation. Unlike classical reasoning where a proposition is either true or false, fuzzy logic establishes approximate truth value of a proposition based on linguistic variables and inference rules [196]. A common approach to deal with multi-objective optimization problems is to use weighted sum based cost function which usually is not sufficient to reach the desired solution. Fuzzy logic uses a fuzzy aggregation operator, namely the ordered weighted averaging [197] as an alternative to weighted sum approach for dealing with the multi-objective cost function. Fuzzy random variables are used to represent both fuzziness and the randomness of the objectives and constraints in routing optimization model introduced in [154]. The proposed model was used to discover the optimal routes, which were the tradeoff among the multiple objectives of delay, reliability, energy, latency, jitter, communication interference and energy balance. The authors argued that the proposed method fully utilized the advantages of Pareto optimal solution with the single run of the algorithm. In [159], the authors have used a fuzzy logic based algorithm to solve a multi-objective routing problem to simultaneously optimize lifetime and source to sink delay in wireless sensor network. A fuzzy based thermal management strategy has been proposed in [198] to control the temperature of a 3-D stacked system integrating cores, memories, sensors and radio frequency devices. The efficiency of the such a microprocessor system-on-chips is affected by the temperature. The proposed algorithm used a fuzzy controller to efficiently control the temperature without compromising the other performance parameters.
## 5 Conclusions
In this case, the classification results were better than those presented in the literature, proving the proposed method's high effectiveness in detecting atrial fibrillation. The model can work efficiently with test samples of any length, making it more applicable in clinical settings. When developing this method, it is worth considering the ratio of AF events to total AF plus non-AF events. A classifier with too few AF events will be unable to identify AF episodes correctly. However, exceeding the specified lower limit of the abovementioned coefficient would mean an AF episode detection rate of almost 100%. Reducing irrelevant features would lead to a better understanding of which features to focus on to achieve even better AF detection and interpretability by medical staff. Due to computational limitations, the proposed method is currently more suitable for offline ECG analysis than implementation in real-time systems. For new data, the derived model needs to be updated. Future research will focus on improving its performance by appropriately selecting the parameters of the LS-SVM classifier; improving the ECG pre-processing step; and, where possible, using more ECG channels. This research was supported by statutory funds (BK-235/RAU10/2023) of the Department of Cybernetics, Nanotechnology and Data Processing of the Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to it is not fully prepared and enabled to share in the repository. The MIT-BIH Atrial Fibrillation Database used in this study are publicly available. This data can be found here: [[https://physionet.org/content/afdb/1.0.0/](https://physionet.org/content/afdb/1.0.0/)]([https://physionet.org/content/afdb/1.0.0/](https://physionet.org/content/afdb/1.0.0/)) (accessed on 21 October 2023). The author declares no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AA & Atrial activity \\ AF & Atrial fibrillation \\ AFDB & Atrial Fibrillation Database \\ CNN & Convolutional neural network \\ DQ-TQWT & Dual-Q tunable Q-factor wavelet transform \\ ECG & Electrocardiogram \\ EMD & Empirical mode decomposition \\ EEMD & Ensemble empirical mode decomposition \\ LS-SVM & Least-squares support vector machine \\ LSTM & Long short-term memory \\ MODWT & Maximal overlap discrete wavelet transform \\ NSR & Normal sinus rate \\ SVM & Support vector machine \\ TPN & Turning point number \\ WT & Wavelet transform \\ \end{tabular}
# Decoupled Monte Carlo Tree Search for Cooperative Multi-Agent Planning
## 5 Method
### Decoupled-MCTS
#### 5.2.2 Coordination Problem  
When using centralized planning with joint actions, the selected joint action ensures the coordination of the actions. However, in the case of decoupled planning, where each agent evaluates its own actions, there is no mechanism to ensure the coordination of actions. Considering the problem, having the attainable reward structure given in Figure 4, the expected values of actions \(A_{1}\) and \(A_{3}\) are the same for agent 1, and, similarly, the expected values of actions \(B_{1}\) and \(B_{3}\) are the same for agent 2 under uniform action selection. However, if their selection of actions is not coordinated, they may get the reward of 0, due to the selection of \(\langle B_{3},A_{1}\rangle\) or \(\langle B_{1},A_{3}\rangle\) action combinations.  
Considering the problem having the penalty reward structure shown in Figure 4, the uncoordinated selection may be penalized. In these cases, the explorations on the action space make these actions unfavorable since their expected values are very low, due to the high penalty values.  
This coordination problem is studied in [1] in the independent reinforcement learning domain. Decoupled-MCTS handles each action selection at each node as an independent multi-armed bandit problem. Therefore, the results derived for game matrices in [1] are valid for the action selection in decoupled-MCTS. They show that an exploration strategy with a gradually increasing exploitation converges to an equilibrium. However, the independent learners approach suffers from miscoordination in high penalty cases [38]. In this paper, we propose an improvement on decoupled-MCTS to address the coordination problem in Section 5.3. The method solved the miscoordination by creating a single search tree over a subset of joint actions. The statistics collected in decoupled-MCTS were used for the pruning of joint actions.
# Real Color Model of a Cadaver for Deep Brain Stimulation of the Subthalamic Nucleus
###### Abstract  
When performing deep brain stimulation (DBS) of the subthalamic nucleus, practitioners should interpret the magnetic resonance images (MRI) correctly so they can place the DBS electrode accurately at the target without damaging the other structures. The aim of this study is to provide a real color volume model of a cadaver head that would help medical students and practitioners to better understand the sectional anatomy of DBS surgery. Sectioned images of a cadaver head were reconstructed into a real color volume model with a voxel size of 0.5 mm \(\times\) 0.5 mm \(\times\) 0.5 mm. According to preoperative MRIs and postoperative computed tomography (CT) of 31 patients, a virtual DBS electrode was rendered on the volume model of a cadaver. The volume model was sectioned at the classical and oblique planes to produce real color images. In addition, segmented images of a cadaver head were formed into volume models. On the classical and oblique planes, the anatomical structures around the course of the DBS electrode were identified. The entry point, waypoint, target point, and nearby structures where the DBS electrode could be misplaced were also elucidated. The oblique planes could be understood concretely by comparing the volume model of the sectioned images with that of the segmented images. The real color and high resolution of the volume model enabled observations of minute structures even on the oblique planes. The volume models can be downloaded by users to be correlated with other patients' data for grasping the anatomical orientation.  
cross-sectional anatomy; deep brain stimulation; magnetic resonance imaging; subthalamic nucleus; visible human projects 2021  
11 May 2021  
26 May 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**C Copyright:** 2021 by the authors. Licenses MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 6 Results and Discussion
### Experimental Results and Discussion
The currents through both inductor L\({}_{1}\) and inductor L\({}_{2}\) are shown in Figure 23. The first inductor carries an average current of about 2.15 A, and the second inductor carries an average current of about 1.3 A for 150 W out power, which is in good agreement with the characteristics equation as described in Section 2. The average output current (i.e., 0.378 A) and the input current (i.e., 3.2 A) is experimentally measured and are shown in Figure 24, respectively. In Table 3 the comparison of the voltage stress across the power MOSFET switch and the power diodes from the simulated results and the experimental results are shown. Both the simulated and experimental results are verified by the characteristics formula for each component. The efficiency of this proposed converter was measured experimentally by considering various loads, as is shown in Figure 25. All of the data points corresponding to each load were measured, and the duty was varied for the low input voltage to obtain the fixed output voltage (i.e., 400 V). The proposed topology was designed for 48 V input voltage and 400 V output voltage; as a result, the efficiency was reduced for lower input voltage for  
Figure 23: The experimental waveform of (**1**) Duty cycle (\(V_{gs}\)) at d = 0.4, (**2**) Inductor (L\({}_{1}\)) Current (I\({}_{\text{L}1}\)), (**3**) Inductor (L\({}_{2}\)) Current (I\({}_{\text{L}2}\)), and (**4**) Capacitor (C\({}_{4}\)) voltage (\(V_{\text{C}4}\)). Figure 24: The experimental waveform of (**1**) Duty cycle (\(V_{gs}\)) at d = 0.4, (**2**) Input Current (\(I_{in}\)), (**3**) Output Current (\(I_{0}\)). the same load. Peak efficiency of about 94.5% was obtained at 150-W output power for this proposed high gain quadratic-based boost DC-DC topology.
# Fully Reconfigurable Photonic Filter for Flexible Payloads
## 2 Reconfigurable Photonic Filters
In particular, the bandwidth and center frequency of the MPF are adjusted from 0.70 to 2 GHz and from 5.20 to 35.80 GHz, respectively, by electrically feeding the electrodes placed on the coupling region and ring resonators' portions. However, the tuning of the coupling coefficient leads to a loss in coupling ratio of about 20 dB when the bandwidth is tuned. In 2023, [PERSON] et al. [19] proposed two cascaded silicon nitride ring resonators, with an MZI as the coupling section, experimentally performing bandwidth reconfigurability from 0.38 GHz to 15.74 GHz and frequency tuning from 4 GHz to 21.50 GHz. However, the transition bandwidth shows a roll-off of 20 dB/octave, far from the \"ideal\" square-shaped filter. Also, non-linear effects have been exploited in similar configurations [20], achieving wide reconfigurability at the expense of large bandwidth (26 GHz) and very slow roll-off (\(\approx\)22 dB/octave). The proposed device shows a box-like filtering shape, together with a roll-off of 42.44 dB/octave, a bandwidth of 501 MHz, thermo-optically (TO) tunable within 20 MHz range, a tunable central frequency within the 180 MHz range, and a stopband rejection of about 45 dB.
# Privacy-Preserving Federated Singular Value Decomposition
## 5 Differentially Private Federated SVD Solution
: _If we assume \(|m_{ij}^{\prime}|\leq\hat{m}\) for all \(i,j\in[1,d]\), then the sensitivity (calculated via the Euclidean distance) of the client-side operations (i.e., Step 3 in Algorithm 3 is bounded by \(  
```
0: Datasets \(\{\mathbb{M}_{i}\}_{i=1}^{n}\), target rank \(k\), iteration rank \(r\), number of iteration \(T\), the clipping bound \(\mathbb{Z}\), the variance of noise \(\sigma\)
0: Approximated eigenspace \(\overline{\mathbb{Z}}_{T}\)
1: initialise \(\mathbb{Z}_{0}^{(i)}=\mathbb{Z}_{0}\in\mathbb{R}^{d\times r}\sim\mathsf{N}(0,1) ^{d\times r}\) with orthonormal columns
2:for\(t=1\) to \(T\)do
3: each node device \(i\) computes \(\mathbb{Y}_{t}^{(i)}=\mathbb{M}_{i}^{r}\mathbb{Z}_{t-1}^{(i)}\), where \(\mathbb{M}_{i}^{\prime}=\frac{1}{s_{i}}\mathbb{M}_{i}^{T}\mathbb{M}_{i}\)
4: each node device \(i\) adds Gaussian noise: \(\mathbb{Y}_{t}^{\prime(i)}=\mathbb{Y}_{t}^{(i)}+\mathbb{N}^{(i)}\sim\mathsf{N} (0,\sigma)^{d\times r}\)
5:if\(t\equiv 0\pmod{p}\)then
6: SA protocol is executed among the server and all node devices, with inputs \(\mathbb{Y}_{t}^{\prime(i)}\) and output \(\mathbb{Y}_{t}\)
7: the server performs orthogonalization and clipping \(\mathbb{Z}_{t}=\mathsf{clip}(\mathsf{orth}(\mathbb{Y}_{t}^{\prime}),\mathbb{ z})\)
8: the server broadcasts \(\mathbb{Z}_{t}\) to all node devices
9: each node device \(i\) sets \(\mathbb{Z}_{t}^{(i)}=\mathbb{Z}_{t}\)
10:else
11: each node device \(i\) calculates the latest \(\mathbb{Z}_{t}^{(i)}=\mathsf{clip}(\mathsf{orth}(\mathbb{Y}_{t}^{\prime(i)}), \mathbb{z})\)
12:endif
13:endfor
14:return approximated eigenspace
```  
**Algorithm 3** Privacy-enhanced FedPower. One can easily extend this result for \(T\geq 1\) with the composition property of DP: Algorithm 3 satisfies \((T\cdot\varepsilon,T\cdot\delta)\)-DP. Besides this basic loose composition, one can obtain better results by utilizing more involved composition theorems such as in [34]. We leave this for future work. **Analysis. ** Similarly to Section 4, we protect the individual intermediate results with SA. On the other hand, it is equivalent to generate \(n\) Gaussian noise with variance \(\sigma\) and select one, or to generate \(n\) Gaussian noise with variance \(\frac{\sigma}{n}\) and sum them all up. Consequently, instead of relying on an SMPC protocol to eliminate most of the local noise, we could merely scale them down. combining SA with such a downsized local noise is, in fact, a common practice in FL: this is what distributed differential privacy (DDP) [35] does, i.e., DDP combined with SA provides LDP but with \(n\) times smaller noise, where \(n\) is the number of participants.