esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Software  
Figure 3 shows the discrete event simulation software that has been applied in the case studies identified in the articles. Our analysis reveals Arena as the dominant software choice, followed by Simul8, a finding that aligns with previous studies on simulation software adoption in healthcare settings. Arena's prevalence, particularly in the USA healthcare sector, can be attributed to multiple factors identified in prior research: its established industry reputation, extensive support community, and large user base in the healthcare industry, which facilitates collaboration and resource sharing among healthcare professionals [20].  
Interestingly, our analysis identified several comparative studies where multiple software platforms were used within the same case study [21; 22; 23; 24; 25; 26; 27]. This trend of software comparison reflects a growing sophistication in the healthcare simulation community, where practitioners are increasingly focused on identifying the most appropriate tools for specific contexts. The fact that the sum of software implementations exceeds the 349 analyzed case studies due to these comparative studies suggests a methodological maturity in the field, where researchers are actively evaluating and comparing different simulation approaches.  
These findings provide insights into DES software usage patterns in healthcare settings. While Arena's prevalence is notable, particularly in academic research, the successful implementation of multiple software platforms suggests that effective healthcare simulation can be achieved with various tools. Software selection, in practice, is influenced by multiple factors including institutional capabilities, existing infrastructure, user expertise, cost considerations, and integration requirements with hospital information systems. This multi-factorial approach to software selection aligns with [PERSON] and [PERSON]'s [5] findings on the practical considerations in healthcare simulation implementation. Our analysis highlights distinct advantages and limitations among major DES platforms, particularly in their applicability to different healthcare contexts.  
Figure 3: DES software implemented in the case studies.
## 3 Results
### Test 4  
In this test, the ground station (GS) was stationary and positioned at 4 m above ground. At the selected locations, the UAV with the transmitter was moved vertically to determine the data rate's dependency on the UAV altitude. Four measurement location points with different horizontal distances from the GS were selected. The maximal UAV altitude was 100 m. The measurement location was flat, agricultural land, a few kilometers south-southeast of the town of Sinj, Croatia. The first measurement location point (ML1) was at a distance of 1450 m from the GS (Figure 10).  
The second measurement location point (ML2) was at a distance of 2750 m from the GS. The third point (ML3) was at a distance of 4250 m, and the fourth (ML4) at a distance of 5400 m from the GS.  
pMDLL2450 module setup:  
Tx power = 30 dbm;  
Bandwidth = 8 MHz.  
The measured throughput for all four measurement location points is presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measurement Location** & **Throughput (MBps)** & **UAV Altitude (m)** & **Comment** \\ \hline  & 1.5 & 10 & \\ ML1 & 1.6 & 30 & \\ (1450 m distance from GS) & 1.6 & 50 & \\  & 1.85 & 50 & Antenna rotated for 90\({}^{\circ}\) \\ \hline ML2 & 1.4 & 25 & \\ (2750 m distance from GS) & 1.5 & 50 & \\  & 2.1 & 50 & Antenna rotated for 90\({}^{\circ}\) \\ \hline ML3 & 0.275 & 100 & Bandwidth reduced from 8 to \\ (4250 m distance from GS) & 0.35 & 100 & 4 MHz \\ \hline ML4 & 0.35 & 100 & Bandwidth reduced from 8 to \\ (5400 m distance from GS) & 0.35 & 100 & 4 MHz \\ \hline \end{tabular}
\end{table}
Table 4: Test 4—Measured throughput at locations ML1, ML2, ML3 and ML4.  
Figure 10: Test 4—Ground station and four measurement locations (MLs).  
For the first two MLs (ML1 and ML2), the measured throughput was satisfactory. Table 4 indicates that better results were obtained when the antennas on the UAV and ground station were perpendicular to each other.  
For ML3 and ML4, the throughput dropped under the desired values. We assume that this drop occurred due to the height differences and obstacles between the GS and UAV. Although a connection was not possible at heights under 100 m, the encouraging fact is that connection was not lost for the measurements at a 100-m altitude. Moreover, for these last two measurements, the bandwidth parameter at pMDLL2450 was reduced to 4 MHz. The best measured results for all distances are given in Figure 11.  
Although the measured throughput for distances over 3 km drops under the desired (needed) values, the assumption is that the system might work because connection remained stable at higher altitudes (there was no connection lost at distances under 5 km). The desired throughput could be improved using a directional antenna at the ground station that will be oriented towards the UAV direction. This was the motivation for the final set of measurements.
###### Abstract  
The proliferation of the Internet and the widespread adoption of mobile devices have given rise to an immense volume of real-time trajectory big data. However, a single computer and conventional databases with limited scalability struggle to manage this data effectively. During the process of visual rendering, issues such as page stuttering and subpar visual outcomes often arise. This paper, founded on a distributed architecture, introduces a multi-level time encoding method using \"minutes\", \"hours\", and \"days\" as fundamental units, achieving a storage model for trajectory data at multi-scale time. Furthermore, building upon an improved DBSCAN clustering algorithm and integrating it with the K-means clustering algorithm, a novel density-based partitioning clustering algorithm has been introduced, which incorporates road coefficients to circumvent architectural obstacles, successfully resolving page stuttering issues and significantly enhancing the quality of visualization. The results indicate the following: (1) when data is extracted using the units of \"minutes\", \"hours\", and \"days\", the retrieval efficiency of this model is 6.206 times, 12.475 times, and 18.634 times higher, respectively, compared to the retrieval efficiency of the original storage model. As the volume of retrieved data increases, the retrieval efficiency of the proposed storage model becomes increasingly superior to that of the original storage model. Under identical experimental conditions, this model's retrieval efficiency also outperforms the space-time-coded storage model; (2) Under a consistent rendering level, the clustered trajectory data, when compared to the unclustered raw data, has shown a 40% improvement in the loading speed of generating heat maps. There is an absence of page stuttering. Furthermore, the heat kernel phenomenon in the heat map was also resolved while enhancing the visualization rendering speed.  
trajectory big data; multi-level time encoding; density partitioning clustering algorithm; data visualization 2023  
13 10714 10.3390/app131910714
# An Embedding-Based Approach to Repairing OWL Ontologies
## 3 Approach
### Diagnosis Generating  
With the information obtained during the preparation step, each axiom in a MIPS needs to be associated with a degree. According to these degrees, a subset is then extracted from each MIPS. Those axioms in such subsets are regarded as candidates to form a diagnosis. Namely, selecting at least one axiom from each subset will resolve the incoherence of the considered ontology.  
To compute a degree for an axiom in an ontology, we consider the semantic relationships between this axiom and others in the ontology. Based on a similarity metric \(sim\), a degree of an axiom \(\alpha\) in an ontology \(O\) can be defined by assuming that \(emb(\alpha)\) indicates the embedding of the axiom \(\alpha\) (See Definition 7). This definition computes the average semantic similarity between the axiom \(\alpha\) and each axiom in \(O\) as the degree of \(\alpha\).  
Definition 7: Given an axiom \(\alpha\) in an ontology \(O\), its degree with respect to the entire ontology \(O\) is defined as below:  
\(d_{global}(\alpha,O)=\frac{1}{|O|}\sum_{\beta\in O}sim(emb(\alpha),emb(\beta))\).  
This degree is called a global degree and is denoted as \(d_{global}\).  
It is noted that two axioms with low similarity often indicate that their semantic relations are weak, or even both axioms have no semantic relations. Thus, a threshold can be used to filter those axioms that have low similarity with \(\alpha\). The degree of \(\alpha\) (see Definition 8) is then computed based on the selected axioms. In Definition 8, the similarity between any axiom in \(S\) and \(\alpha\) is higher than the threshold, and only those axioms in \(S\) will contribute to the degree of \(\alpha\). Namely, this definition computes the average semantic similarity between the axiom \(\alpha\) and each axiom in \(S\) as the degree of \(\alpha\).  
Definition 8: Given an axiom \(\alpha\) in an ontology \(O\), its degree with respect to a pre-defined threshold \(t\), denoted as \(d_{thr}\), is defined as below:  
\(d_{thr}(\alpha,O,t)=\frac{1}{|S|}\sum_{\beta\in S}sim(emb(\alpha),emb(\beta))\),  
where \(S=\{\beta|sim(emb(\alpha),emb(\beta))>t,\beta\in O\}\).  
To ensure two axioms have semantic relations, we provide another definition to compute a degree for an axiom by considering those axioms that contain at least one entity appearing in the axiom (see Definition 9). A signature of an axiom can be a concept name, a property name or an individual name appearing in the axiom. Definition 9 computes the average semantic similarity between the axiom \(\alpha\) and each axiom in \(T\) as the degree of \(\alpha\).  
Definition 9: The signature-based degree of an axiom \(\alpha\) in an ontology \(O\), denoted as \(d_{sig}\), is formally defined as below:  
\(d_{sig}(\alpha,O)=\frac{1}{|T|}\sum_{\beta\in T}sim(emb(\alpha),emb(\beta))\),  
where \(T=\{\beta|sig(\alpha)\cap sig(\beta)\
eq\emptyset,\beta\in O\}\), and \(sig(\alpha)\) returns all signatures in \(\alpha\).
## 8 Experimental Studies
The results of the computed metrics are given in Table 2.  
For the binary classification, the SN, SP, PR, and F-score values of the defect class were 0.986, 0.98, 0.983, and 0.984, respectively. The SN, SP, PR, and F-score values of the non-defect class were 0.98, 0.986, 0.984, and 0.982, respectively. For the multi-class classification, the best SN (0.992), SP (0.996), PR (0.985), and F-score (0.988) values were obtained for the scratches, patches and multi-class defects, scratches, and scratches classes, respectively. The worst SN (0.881), SP (0.96), PR (0.985), and F-scores (0.988) values were obtained for the multi-class defects, scratches, crazing, and multi-class defect classes, respectively. Figures 13 and 14 show the ROC curves and AUC values for the two classification problems. In Figure 13, positive classes 1 and 2 include the defect and non-defect classes, respectively. In Figure 14, positive classes 1, 2, 3, 4, and 5 include pitted surfaces, crazing, scratches, and the multi-class defect classes, respectively. As seen in Figure 13a,b, the AUC values were 0.99 for positive classes 1 and 2. As seen in Figure 14a-e, the AUC values were 0.99, 1.00, 0.99, 0.99, and 0.98 for positive classes 1, 2, 3, 4, and 5, respectively. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Classification Mode** & **Classes** & **SN** & **SP** & **PR** & **F-Score** \\ \hline \multirow{2}{*}{Binary} & Defect & 0.986 & 0.98 & 0.983 & 0.984 \\  & Non-defect & 0.98 & 0.986 & 0.984 & 0.982 \\ \hline \multirow{4}{*}{Multi-Class} & Pitted surfaces & 0.961 & 0.995 & 0.963 & 0.962 \\  & Crazing & 0.938 & 0.997 & 0.91 & 0.924 \\ \cline{1-1}  & Scratches & 0.992 & 0.96 & 0.985 & 0.988 \\ \cline{1-1}  & Patches & 0.924 & 0.996 & 0.952 & 0.938 \\ \cline{1-1}  & Multi-class defects & 0.881 & 0.996 & 0.933 & 0.906 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Other performance metric results of the proposed approach for binary and multi-class classification. Figure 12: Confusion matrices of the proposed approach: (**a**) multi-class classification (classes: (1) pitted surfaces, (2) crazing, (3) scratches, (4) patches, (5) multi-class defect), (**b**) binary classification (1: defect 2: non-defect). Figure 14: _Cont._  
Figure 13: ROC curves and AUC values of the proposed approach: binary classification (**(a)** class 1: defect, **(b)** class 2: non-defect).
## 3 The CLICP Construction Scheme
### CLICP Function Design
#### 3.2.2 Freight Information Push Function  
At present, most of the information cues for goods orders in relevant logistics distribution websites are static cues, which are simple and monotonous, lack communication and interaction between consignors and carriers, and have slow information flow. With the increasing degree of freight information and vehicle ownership, existing information prompt methods can no longer meet the needs of carriers to find suitable goods orders efficiently and quickly.  
For this purpose, we designed the CLICP's freight information push module, which mainly pushes the freight demand order information issued by a consignor to the carrier side. This function is divided into two submodules: the screening module and the intelligent push module. The main implementation of the screening module is to eliminate redundant information and carry out appropriate vehicle and cargo matching functions. First, the order contents issued by the consignor, such as the weight and category of the goods or the distribution mode, are set to corresponding rules; then, the freight information is automatically screened according to the set rules and redundant or unnecessary information is excluded. At the same time, using the carrier's basic information, the function analyzes the carrier's history, vehicle type, and performance, and orders and feedback carriers that are not suitable for shipping are excluded from re-evaluation, so as to reduce the consignor's waiting time and reduce redundant system data.
## 2 Experimental Setup and Procedures
### Experimental Process  
In this research, all of the lithium-ion batteries were LYS347094S from Taiwan's LYNO Corporation. These batteries' working voltage and capacity are rated at 3.2 V and 10 Ah. In accordance with the data sheet from the manufacturer, the discharging rates under various temperatures of \(-\)10, 0, 10, 20, 30, 40, and 50 \({}^{\circ}\)C in a temperature-controlled chamber were tested (DBL45 from Taiwan Dengyng Tec Corporation). These LiFePO\({}_{4}\) batteries were discharged under rates of 0.5, 1, 2 and 3 C by a Chroma 17020 battery test station. These batteries were cycled 400 times, within the limits recommended by the manufacturer. Information such as capacity attenuation and number of cycles of the batteriesweare computer recorded. The model was constructed in MATLAB software platform to analyze the recorded discharging curve and perform curve fitting. The experimental setup flowchart is shown in Figure 1.  
Figure 1: Experimental setup flowchart.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 7 Interoperability Standards
### Standards Development Organizations  
Standards development organizations (SDOs) are key players in the advancement of the metaverse [212], ensuring that different platforms, devices, and content within this evolving virtual ecosystem are interoperable, accessible, and scalable. These organizations develop open standards to promote uniformity and collaboration across industries [88], preventing fragmentation and proprietary limitations. The W3C (World Wide Web Consortium) has been instrumental in creating WebVR and WebXR [213], standards that enable immersive VR and AR experiences through web browsers. This makes content more accessible by eliminating the need for standalone applications. Similarly, the Web3D Consortium works on the X3D standard, which enables the sharing and use of 3D graphics across web platforms, crucial for delivering 3D content in the metaverse.  
The ISO/IEC MPEG group has a longstanding role in developing standards for coding, compression, and transmission of audio and video content [214]. Their MPEG-V standard ensures smooth interoperability of immersive media, such as 3D and VR/AR content, across various devices. Meanwhile, the Khronos Group, through standards like OpenXR and gITF, is focused on providing open and royalty-free interoperability standards for XR devices and 3D content, helping developers create cross-platform applications [215]. The International Telecommunication Union Telecommunication Standardization Sector (ITU-T) Correspondence Group (CG)-metaverse is dedicated to the standardization of metaverse technologies, with a focus on enabling global interoperability through telecommunication and infrastructure standards [216]. The Open Metaverse Interoperability Group complements this effort by promoting open-source interoperability in the metaverse, advocating for user-friendly transitions between virtual worlds.  
IEEE's Metaverse Standards Committee, through its P2048 standards, is developing frameworks for VR/AR technologies on a global scale, ensuring technical coherence and best practices [212]. Similarly, the World Metaverse Council provides guidance on global policies and standards [217], focusing on ethical and regulatory frameworks for the metaverse. The Universal Scene Description (USD) standard improves 3D content  
Figure 9: Interoperability of the metaverse.  
interoperability for creation and collaboration in virtual environments [218]. Pixar, Adobe, and other companies lead the Alliance for OpenUSD [88]. Lastly, the Metaverse Standards Forum [96], which includes over 1,200 organizations, promotes open standards for the metaverse, driving industry-wide collaboration to create a more inclusive and interoperable virtual ecosystem. These SDOs collectively shape the future of the metaverse, ensuring it remains accessible, scalable, and technologically coherent across platforms, benefiting users and developers alike.
# A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
## 2 The Developed Solution Framework for PF Analysis
### Handling Equipment Limits  
Handling equipment limits (such as those imposed by e.g., generators, on-load tap changers, etc.) is an important topic in PF analysis. The developed solution frameworks (8) and (13) do not directly take into account these limits; however, they could be easily incorporated. In this case, we have considered the same strategy used in [21]. Particularizing to the case of reactive limits in generation units, this approach consists of checking the reactive limits of all generators after a PF solution is calculated. Then, if any limit has been violated, those buses are converted to PQ, and the violated limit is taken as the reactive power injection of this new PQ bus. Then, taking the obtained solution as a starting point, the process is repeated until a feasible solution is reached. This strategy is described in the flowchart of Figure 1. One should note that other control parameters such as transformer tap changers can be taken into account using similar strategies. It is worth mentioning that the developed solution framework is versatile enough to easily incorporate other strategies, such as that described in [2].
## 7 Nanomedicine to Enhance the Therapeutic Effectiveness of BC
### Nanoparticle-Mediated Drug Delivery  
Nanomedicine offers numerous advantages over traditional cancer treatments, including better protection against in vivo biochemical degradation, fewer adverse effects as a result of enhanced biocompatibility and precision targeting, and a higher dose of chemotherapy reaching the cancerous tissue. The field of delivery systems has seen significant advancements aimed at transporting therapeutic agents or naturally derived active compounds to targeted locations for treating various ailments. Despite recent successes with several drug delivery systems, there are persisting challenges that necessitate attention. Advanced technology is crucial to ensure the effective delivery of drugs to targeted sites [96]. Consequently, the current research is dedicated to advancing nano-based drug delivery systems as the next-generation approach. NPs possess active or passive targeting capabilities and are enveloped in layers of various organic or inorganic materials that define their characteristics. These attributes can enhance drug concentration within tumors while minimizing toxicity in healthy tissues. Numerous studies underscore the benefits of utilizing NPs in drug delivery systems (DDSs) for BC treatment, including factors like water dispersion, biocompatibility, biodegradability, stability, circulation half-life, renal processing, accumulation, and cellular uptake [94; 95]. Thus, DDSs play a crucial role in probing the response of nanoparticles toward living systems at cellular and tissue levels. For instance, tailored liposomes can securely hold drugs until triggered; thus, they can facilitate prolonged drug release. Additionally, they tend to accumulate in cancer cells, enhancing drug selectivity and consequently reducing toxicity. Nanomedicine holds significant promise in targeting and eradicating BC stem cells, which could play a crucial role in BC initiation, recurrence, and resistance to chemo/radiotherapy. Several nanoparticle-based chemotherapy delivery systems are either FDA-approved or undergoing clinical trials for cancer treatment [47; 97].