esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Dataset Size on Classification Performance:
## 3 Results
### Small Datasets  
The performance of the six classification models, namely AB, RF, NN, DT, NB, and SVM when trained on the eighteen small datasets is presented in Table 5 with respect to accuracy. The performance of the classification models with respect to precision, recall, specificity, f-score, and AUC are shown in Tables A1-A5 in the Appendix A.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification Model** & **Parameter Values** \\ \hline \hline \multirow{4}{*}{AB} & Batch size = 100 \\  & Classifier = decision stump \\  & numIterations = 10 \\  & seed = 1 \\  & weight threshold = 100 \\ \hline \hline NB & Batch size = 100 \\ \hline \multirow{4}{*}{SVM} & Batch size = 100 \\  & Kernel = Polynomial \\  & C = 1 \\  & Random seed = 1 \\  & Tolerance parameter =0.001 \\ \hline \multirow{4}{*}{NN} & Hidden layers = (attributes + classes)/2 \\  & Learning rate = 0.3 \\  & Seed = 0 \\ \hline \multirow{4}{*}{DT} & Batch size = 100 \\  & Binary split = false \\ \cline{1-1}  & Confidence factor = 0.25 \\ \cline{1-1}  & MinNumObj = 2 \\ \cline{1-1}  & Seed = 1 \\ \hline \hline \multirow{4}{*}{RF} & Batch size = 100 \\ \cline{1-1}  & numIterations = 100 \\ \cline{1-1}  & seed = 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification models parameter values.  
Several observations can be made from Table 5. First, we can observe that the average accuracy of classifiers trained on the small datasets ranges from 62% on DS18 to 99% on DS1 and DS8. Second, it can be seen from the table that the average accuracy of classifiers across the small datasets ranges from 79.28% achieved by AB to 82.78% accuracy by DT. Third, we can also see that the standard deviations across classifiers (Std. Dev. For each dataset, last column) are less than the standard deviations across datasets (Std. Dev. For each classifier, last row).  
Similar trends are observed in the performance of classifiers with respect to precision, recall, specificity, f-score, and AUC in Tables A1-A5 in the Appendix A. For instance, the average precision of classifiers in Table A1 ranges from 62.43% on DS18 to 99% on DS1 and DS8, and the average recall ranges from 61.68% on DS18 to 99.12% on DS8 (see Table A2). In addition, the average precision of classifiers across the small datasets ranges from 78.07% precision by AB to 82.21% achieved by NB. For recall, the average performance of classifiers across the small datasets ranges from 79.22% by AB to 82.73% by DT. Furthermore, we can see in Tables A1-A5, and, similar to accuracy in Table 5, that the standard deviations across classifiers are less than the standard deviations across datasets.
## 4 Characterization and Performance Analysis  
The performance of the SQM metrics for multipath detection is now analyzed. The methodology is based on extracting SQM variation profiles and comparing them with the multipath tracking range error envelopes for different discriminators.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 4 Challenges and Future Research Directions  
This section outlines several challenges and suggests some future research directions. Some of the challenges listed are interoperability and standardization, robust, scalable solutions, AI for O-RAN, and security and privacy. Future research directions include O-RAN for IoT applications, security and privacy in O-RAN, explainable AI for IoT and O-RAN integration and standardization, and research and development.
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 4 Results  
In this part, we evaluate the segmentation performance of our framework on abdominal MR and hippocampal MR images. As described in Section 2, we established our framework by embedding a plug-and-play contour enhancement module into CNNs and training with a voting strategy. **CHAOS(MR):** The CHAOS challenge aims to segment the abdominal organs accurately. Table 3 shows the ablation study results of our proposed framework for the metrics of DSC, mIoU, and HD for the dataset of CHAOS. We conducted two experiments in which we trained Segnet and nn-Unet (2d) as our baselines. We ebbed the contour enhancement modules in these two backbone networks to conduct our contour-aware networks (CN) in the first step and applied the stage of joint optimization using voting strategy (VS). It is distinct that our framework could achieve fairly good results for conducting different backbone networks and improve the accuracy of segmentation in each stage progressively. For the experiments conducted based on Segnet, the results of contour-aware networks can achieve 2.2% and 2.9% improvements and 3.1 mm reduction on DSC, mIoU, and HD, respectively, and the voting strategy can further improve slightly the average DSC and mIoU to 90.4 \(\pm\) 3.0% and 83.7 \(\pm\) 3.9% and can slightly reduce the average HD to 13.9 \(\pm\) 8.4 mm. The results of our framework based on nnU-Net have a similar increasing tendency in each stage and can achieve 1.8% and 2.6% improvements and 5.8 mm reduction, compared to baseline, reaching 91.2 \(\pm\) 2.6%, 84.8 \(\pm\) 3.5%, and 12.6 \(\pm\) 10.8 mm in DSC, mIoU, and HD, respectively. The qualitative results of our method on the CHAOS dataset are displayed in Figure 2.
## 5 Results and Discussion
### Rank Correlations between the Aggregated Rankings
We observed that the international match results principally affected the rankings of the TH method and least affected the rankings of the AL method. The TM ranking had a medium correlation (around 0.7) with both national and international rankings.
## 3 Software-Defined Delay Tolerant Networking Architecture
It is assumed that the clusters are organized in a manner where these control plane links are mostly reliable, although how this is achieved is outside the scope of this paper. It should be noted that there is a large body of literature on SDN clustering and controller placement algorithms [29; 30; 31], and that these clusters could also be determined in a similar manner to regions in the previously discussed DTN inter-regional routing method [26]. This architecture enables decision making that is more optimal than if nodes worked independently and provides reliable propagation of network updates through the control plane links. Additionally, while cluster decisions may be suboptimal compared to decisions made with global knowledge, this clustering approach enables shorter decision making times for large space DTN networks. Additionally in this SDDTN architecture, to account for the event that the control plane link for a given SDDTN node is disrupted, each SDDTN node has its own form of a local controller that enables it to continue making decisions on its own. This local controller could potentially always be running providing secondary services alongside the cluster controller, or it could be used as a backup controller. The services that this local controller would provide are implementation specific but could include behavior such as opportunistic learning, e.g., OCGR, flooding algorithms, or routing based on machine learning methods. Figure 6: A Software-Defined Delay Tolerant Network architecture, with three clusters, each containing an SDDTN controller managing SDDTN nodes via control plane links. A SDDTN node has all the behaviors of a typical DTN node, dictated by the abstract Bundle Protocol Agent, and also has communication with a local controller.
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 4 Evaluation
### Evaluation of Similarity  
The LSH forest can be queried using a linear scan by domain approach, which retrieves the k-nearest neighbours to a given domain through a combination of LSH forest traversal and linear scanning. This method provided an effective means by which to visualise and assess the success and stability of the similarity calculations. For TMAP functionality, the distance to the given domain was inversely related to its proximity, i.e., the closer the domain, the lower the distance value, with an exact match represented as 0.0. To evaluate the results, 20 random domains from each dataset were queried for their nearest ten neighbours, and the distance from the origin (0.0) was plotted against the k-nearest neighbours. Figure 6 displays the results for Dataset A (Mixed Host), in comparison to Dataset B (Cloudflare CDN), as shown in Figure 7:  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Sample Size (n)** & **Dimensionality (d)** & **Improvement** \\ \hline Mixed Host & 17,711 & 2124 features & 4-199\% \\ \hline Cloudflare CDN & 5368 & 847 features & 66.7–4523.7\% \\ \hline Malicious & 4475 & 306 features & 4–118\% \\ \hline \end{tabular}
\end{table}
Table 4: Fingerprint granularity improvement across datasets.  
Figure 6: The Mixed Host dataset displays a diverse number of distance metrics and a broader distribution of similarity scores across the sample space. Each line represents a different domain, with a range of colors to aid in differentiation.
## Appendix A  
_Dynamic Programming Formulation_  
1. _State Variables_:  
\(S_{t}=(XBR_{it},\ XAR_{it},\ WBR_{it},\ WAR_{it})\): State at time \(t\) representing the inventory levels before and after replenishment in both the forward and buffer areas for each product \(i\).
2. _Decision Variables_:  
\(a_{t}=\left(Q_{it},\ QN_{it},\ X_{it}^{W},\ D_{it}^{S},\ D_{it}^{W},\ D_{it}^{P},\ C_{it}^{D}\right)\): Action at time \(t\) representing the decisions for each product \(i\).
3. _Value Function_:  
\(V_{t}(S_{t})\): The minimum cost-to-go from the state \((S_{t})\) at time \(t\) to the end of the planning horizon.
4. _Bellman Equation_:  
\[cost_{t}=min_{a_{t}}\left\{\sum_{i=1}^{N}\left(Q_{it}c^{r}+C_{it}^{D}c^{d}+ \left(WAR_{it}-DW_{it}+WBR_{it}\right)c^{w}+X_{it}^{W}c^{avx}\right)\right\}\]  
\[V_{t}(S_{t})=min_{a_{t}}\left\{cost_{t}+E[V_{t+1}(S_{t+1})]\right\}\]\[V_{t}(S_{t}) =min_{a_{t}}\left\{\sum_{i=1}^{N}\left(Q_{it}c^{r}+C_{it}^{D}c^{d}+ \left(\underset{it}{WAR}-DW_{it}+WBR_{it}\right)c^{w}+X_{it}{}^{W}c^{wx}\right)+\right.\] \[\left.E[V_{t+1}(XBR_{it+1},XAR_{it+1},WBR_{it+1},WAR_{it+1})]\right\}\]
5. _Constraints State Transition Equations:_ \[XAR_{it} =XBR_{it}+QN_{it}C_{i}-X_{it}{}^{W} i\in N,\,1\leq t\leq T\] \[XBR_{it+1} =XAR_{it}-D_{it}^{S} i\in N,\,1\leq t\leq T\] \[WAR_{it} =X_{it}{}^{W}+WBR_{it} i\in N,\,1\leq t\leq T\] \[WBR_{it+1} =WAR_{it}-D_{it}^{W}+C_{it}^{D}C_{i}-D_{it}^{P} i\in N,\,1\leq t\leq T\] _Capacity Constraints:_ \[X_{it}{}^{W}\leq Q_{it}C_{i} i\in N,\,1\leq t\leq T\] \[QN_{it}\leq Q_{it}M_{i} i\in N,\,1\leq t\leq T\] \[QN_{it}C_{i}+XBR_{it}-X_{it}{}^{W}\leq F_{i} i\in N,\,1\leq t\leq T\] \[QN_{it}\leq C_{i}F_{i} i\in N,\,1\leq t\leq T\] \[XAR_{it}\leq F_{i} i\in N,\,1\leq t\leq T\] _Demand Fulfillment_: \[D_{it}^{W}+D_{it}^{S}+D_{it}^{P}=D_{it} i\in N,\,1\leq t\leq T\] \[X_{it}{}^{W}\leq XBR_{it} i\in N,\,1\leq t\leq T\] \[D_{it}^{P}\leq C_{it}^{D}C_{i} i\in N,\,1\leq t\leq T\] \[D_{it}^{W}\leq WAR_{it} i\in N,\,1\leq t\leq T\] _Total Replenishment Capacity_: \[\sum_{i=1}^{N}Q_{it}\leq\tau i\in N,\,1\leq t\leq T\] Non-negativity and binary: \[XBR_{it},\,XAR_{it},\,WBR_{it},\,WAR_{it},\,QN_{it},\,X_{it}{}^{W},\,D_{it}^{S},\,D_{it}^{W},\,D_{it}^{P},\,C_{it}^{D}\geq 0 i\in N,\,1\leq t\leq T\] \[Q_{it}\in\{0,1\}\]
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 3 Architecture Proposal
### Custom ICMPv6 Messages
#### 3.2.2 Payload  
Building upon the BPv7 status reports' defined structure, the payload of these ICMPv6 messages can include further details. Relevant information could encompass the creation timestamp of the reported packet, fragmentation-related data such as the fragment offset and payload length, and details about applicable security measures or other metadata. While the source IP address of the reported DTN packet could theoretically be included, this information is redundant since it is already represented as the destination address of the ICMPv6 packet. Further refinements to the payload structure will depend on practical implementation and testing experiences, given that even BPv7 does not provide definitive guidance in some areas.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Hybrid-II
#### 4.4.1 Requirement Satisfaction  
The Hybrid-II architecture shares some similarities with the P2P architecture, but there are some differences between them. Firstly, the Hybrid-II design has enhanced the scalability by resolving the resource heterogeneity issue with reliable cloud resources to cover resource deficiency from user departure. The optimal cloud resource assignment problem with respect to system load has been studied in [143] to minimize the economic cost from cloud resource utilization. Resource virtualization can also provide load balance to further improve scalability. In [90], load imbalance is evaluated with the Gini Coefficient. If a device is overloaded, it will migrate some of the VNs to other devices with minimized visual inconsistency.  
Cloud resources also have a contribution to state persistency [135]. The SAM service provides two layers of replication. First, each VN assigned to a user device (called uVN) is backed up with a cloud node (called bVN). An uVN periodically synchronizes its node state to the bVN. This type of fault-tolerant mechanism is called coarse-grain data replication. Moreover, a VN in SAM also dynamically replicates its objects to a VN (i.e., overlay replicas) in the neighbor address space [144], which is called fine-grained data replication. In the case of uVN failure, the SAM service will forward requests to the overlay replica. If an overlay replica has become overloaded, it will inform its clients to forward their requests to the bVN. In such a design, the coarse-grain data replication can reinforce storage reliability, and the fine-grain one can improve service responsiveness through neighbor address access [144].  
Though not mentioned by the authors, we believe that content persistency can be achieved with the same approach as that in the P2P architecture, since the overall Hybrid-II architecture is decentralized. Likewise, the Hybrid-II architecture can achieve the same credibility requirement satisfaction as the P2P architecture, due to the lack of a centralized control entity.
_Article_  
**Nitrogen and Sulfur Co-Doped Porous Carbon Derived from Thiourea and Calcium Citrate for Lithium-Sulfur Batteries**  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1, [PERSON] 3, [PERSON] 1,**  
**[PERSON] 4,* and [PERSON] 2,* 1**  
Busan Center, Korea Basic Science Institute, Busan 46742, Korea; [EMAIL_ADDRESS] (Y.C.); [EMAIL_ADDRESS] (S-Y.L.); [EMAIL_ADDRESS] (J-S.B.); [EMAIL_ADDRESS] (H.K.K.K.)  
Department of Materials Science and Engineering, Pusan National University, Busan 46241, Korea  
Jeonju Center, Korea Basic Science Institute, Jeonju 54907, Korea; [EMAIL_ADDRESS]  
National Research Facilities and Equipment Center, Korea Basic Science Institute, Daejon 34133, Korea  
Correspondence: [EMAIL_ADDRESS] (E.D.J.); [EMAIL_ADDRESS] (H.-C.S.); Tel.: +82-042-865-3480 (E.D.J.)  
Received: 13 December 2019; Accepted: 8 February 2020; Published: 13 February 2020  
**Abstract:** Lithium-sulfur (Li-S) batteries have shown a high theoretical specific capacity of 1675 mAh g\({}^{-1}\). However, amongst the issues they have, the low electron conductivity of sulfur and its dissolution represent the biggest challenge limiting its practical applications. This contributes to the low utilization of the active sulfur at the cathode--a phenomenon known as the \"shuttling effect.\" To overcome these limitations, some strategies such as physical confinement (sulfur-carbon composite), chemical adsorption (N and/or S doping) electrolyte design, and separator design have already been proposed. Calcium citrate is the most attractive carbon source because no activation process is necessary and the fabrication process is very simple. In this experiment, we synthesized calcium citrate and sulfur (S) to conduct a charging-discharging test and compared them by adding thiourea (TU) as well as S in the carbonized calcium citrate (CaC). This effective and simple technique for material production can accommodate the charge/discharge reactions and preserve the structure over long cycles. A CaC/TU-S composite is acceptable for an initial discharge capacity of 1051.6 mAh g\({}^{-1}\) over 100 cycles at 1 C. The results show that the CaC-S and CaC/TU-S composites have a good, stable specific capacity.  
**Keywords:** Li-S batteries; calcium citrate; thiourea; mesoporous carbon
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
###### Abstract  
This paper presents experimental validation of a distributed optimization-based voltage control system. The dual-decomposition method is used in this paper to solve the voltage optimization problem in a fully distributed way. Device-to-device communication is implemented to enable peer-to-peer data exchange between agents of the proposed voltage control system. The paper presents the design, development and hardware setup of a laboratory-based testbed used to validate the performance of the proposed dual-decomposition-based peer-to-peer voltage control. The architecture of the setup consists of four layers: microgrid, control, communication, and monitoring. The key question motivating this research was whether distributed voltage control systems are a technically effective alternative to centralized ones. The results discussed in this paper show that distributed voltage control systems can indeed provide satisfactory regulation of the voltage profiles.  
peer-to-peer; distributed control; device-to-device communication; voltage control; experimentation +
Footnote †: journal:
# Robotics Applications in the Hospital Domain:
## 2 Methodology for the Literature Review  
Given the inherent limitations of the healthcare domain, we endeavored to assess the quality of each potential source by applying methodological rigor to its contribution to the field. To ensure that only impactful and high-quality studies were included in our review, we employed a rigorous process of study selection.  
In this review article, we use the following methodology for our research:  
* Initial search: An extensive search of prominent databases was conducted, including PubMed, IEEE Xplore, MDPI, ScienceDirect, ACM Digital Library, BioMed Central, Springer, and others.
* The search was divided into three phases. First, based on their titles, we searched databases for papers related to \"robotics\", \"healthcare\", and \"hospital\". This gave a general summary of the volume of research conducted in this area. The records screened equaled n = 936. We then restricted our search.
* Inclusion criteria: The research papers selected were those most relevant to the central themes of this article, namely, (1) the application of robotics in the hospital domain and (2) future prospects for the use of robotics in healthcare and emerging technologies in the context of improving healthcare quality. All the other papers were excluded.  
To ensure that the information was up to date, articles published from 2015 onwards were prioritized. The following papers were excluded from the review on the grounds of not meeting the inclusion criteria.  
Papers that did not address the application of robotics in the hospital domain: records screened (n = 349), reports not retrieved (n = 290).  
Papers that were not related to future prospects for the use of robotics in healthcare and emerging technologies in the context of improving healthcare quality: records screened (n = 120), reports not retrieved (n = 64); 11 duplicated articles were excluded.  
Cross-referencing was employed to ensure the inclusion of only the most relevant studies. To expand the scope of our literature review, we also examined the references of the articles analyzed, as well as those of the selected articles, to identify any additional relevant contributions that could enhance the value of our work. The articles included ensure a mix of theoretical research, practical case studies, and academic analyses, providing a comprehensive perspective on the topic (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Databases** & **Found Documents** & **Documents Excluded** & **Selected Papers** \\ \hline PubMed & 71 & 51 & 20 \\ IEEE Xplore & 86 & 74 & 12 \\ MDPI & 49 & 42 & 7 \\ ScienceDirect & 19 & 14 & 5 \\ ACM Digital Library & 8 & 5 & 3 \\ BioMed Central & 5 & 3 & 2 \\ Springer & 26 & 24 & 2 \\ Others & 85 & 77 & 8 \\ Total & 349 & 290 & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Indications of the number of articles from the databases consulted and those that were included in the list of references.
## 3 Materials and Methods
### Description of the WELAD
#### 3.2.2 Stage II: Generate the Weighted Ensemble Classifier  
The purpose of this stage is to determine the weight values of a set of ensemble classifiers. Given a training dataset used for the weighted ensemble, which has \(G\) samples, under the effect of weight \(\mathbf{w}\), the prediction result of the weighted ensemble classifier for \(x_{i}\) is expressed by Equation (20), and the training error is expressed by Equation (21).  
\[\mathbf{wq}(x_{i})\ =\ \left\{\begin{array}{cc}1,&\quad H_{2}(x_{i},\mathbf{w})\ =\ y_{i}\\ 0,&\quad H_{2}(x_{i},\mathbf{w})\
eq y_{i}\end{array}\right. \tag{20}\]  
\[\mathit{trainErr}(\mathbf{h},\mathbf{w})\ =\ \frac{1}{G}\sum\limits_{i=1}^{G}\mathbf{wq}(x_{ i}) \tag{21}\]  
At this stage, the PSO algorithm is used to solve the following problems:  
\[\begin{split}&\mathit{min}\ \ \mathit{trainErr}(\mathbf{h},\mathbf{w})- \lambda\mathit{Div}_{2}(\mathbf{h},\mathbf{w})\\ &\mathit{s.t.}\quad\sum\limits_{i=1}^{L}w_{i}\ =\ 1,w_{i}\in[0,1]\end{split} \tag{22}\]  
where \(\mathit{Div}_{2}(\mathbf{h},\mathbf{w})\) is a regularization term, which is used to prevent overfitting. \(\lambda\in(0,\,1)\) is a regularization coefficient, which is used to balance the accuracy and diversity of the ensemble learning.  
Algorithm 2 is implemented to assign different weights to the obtained set of classifiers with appropriate diversity. Let the prediction results of the \(L\) classifiers in the weighted ensemble training dataset be \(\left\{\mathbf{q}_{1},\mathbf{q}_{2},\ldots,\mathbf{q}_{L}\right\}\); their pseudocodes are as follows.  
```
1. Input:
2. \(\left\{\mathbf{q}_{1},\mathbf{q}_{2},\ldots,\mathbf{q}_{L}\right\}\), \(T_{max},SS,L\)
3. Process:
4. for\(j\ =\ 1\) to \(SS\) do
5. Initialize \(\mathbf{w},\mathbf{w}\gets rand(0,1)\), \(\mathbf{w}\leftarrow\mathbf{w}/\sum\limits_{i\ =\ 1}^{L}w_{i}\);
6. \(\mathbf{x}_{j}^{0}\leftarrow\mathbf{w}\);
7. end
8. Calculate the diversity measure \(\mathit{Div}_{2}(\mathbf{h},\mathbf{w})\) according to Equation (7);
9. Calculate \(\mathit{trainErr}(\mathbf{h},\mathbf{w})\) according to Equation (21);
10. Evaluate the fitness of particles \(\mathit{trainErr}(\mathbf{h},\mathbf{w})-\lambda\mathit{Div}_{2}(\mathbf{h},\mathbf{w})\);
11. Set \(\mathbf{pbest^{0}}\) and \(\mathbf{gbest^{0}}\);
12. for\(t\ =\ 1\) to \(T_{max}\) do
13. Update \(\mathbf{x}_{j}^{t}\) using Equation (11);
14. Calculate the diversity measure \(\mathit{Div}_{2}(\mathbf{h},\mathbf{w})\) and \(\mathit{trainErr}(\mathbf{h},\mathbf{w})\);
15. Evaluate the fitness of particles, and set \(\mathbf{pbest^{t}}\) and \(\mathbf{gbest^{t}}\) ;
16. end
17. Output: \(\mathbf{w}\)
```  
**Algorithm 2** Using PSO to optimize the weight parameters of the classifier \(\mathbf{w}\)