esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 1 Introduction
This connection enables the decoder to apply different levels of features simultaneously, enhancing the ability to perceive information at different scales of the input data, thus realizing multi-scale feature fusion. Multi-scale feature fusion is used to enhance the network's ability to capture the boundary information of the lake water body and make the segmentation results more accurate in the boundary region. (5) Extraction experiments are conducted for the R50A3-LWBENet model and U-Net, AU-Net, RU-Net, ARU-Net, SER34 AUNet, and MU-Net models for lake water bodies, and the performance of the different models is evaluated and compared by three aspects: visual observation, performance evaluation metrics, and model convergence.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### VBDA Core Technologies
#### 3.2.4 Storage and Indexing in VBDA Systems
PostgreSQL is favored for its support of JSONB, spatial queries, and compatibility with tools such as Grafana and Superset, while MySQL remains useful for lightweight, transactional operations [11; 108]. Emerging data lakehouse architectures, including Delta Lake, and Apache Iceberg, bridge the gap between flexible data lakes and structured data warehouses. They support ACID transactions, schema evolution, and real-time ingestion, while integrating tightly with processing engines such as Spark and Flink. These capabilities make them well-suited for hybrid and federated VBDA systems that require both agility and consistency [7]. AI-driven indexing frameworks are transforming video data management by enabling efficient organization, retrieval, and interpretation of content. Technologies such as CLIP, VideoBERT, BLIP, and FAISS [146] support semantic tagging, cross-modal retrieval, and zero-shot learning. When combined with platforms like Elasticsearch or RDF-based engines, these tools enable scalable, multimodal analytics across a variety of domains, including healthcare monitoring, autonomous vehicles, traffic analysis, and public safety [144; 145]. In summary, VBDA storage architectures have transitioned from traditional structured warehouses and standalone data lakes to integrated data lakehouse platforms supported by AI-driven indexing. Each layer, including NoSQL systems, graph databases, and cloud-native object stores, addresses specific requirements for scalability, flexibility, and semantic comprehension. This convergence equips modern VBDA systems with the capability to perform low-latency, real-time analytics while ensuring privacy-aware video intelligence across distributed and heterogeneous environments.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 4 Simulation Scenario
### Applications
#### 4.4.2 Video Surveillance  
The video surveillance application is based on a common CCTV service, and it is modeled with the iperf3 tool. In terms of traffic characterization, it is a real-time high-quality (HQ) video stream with a resolution of 1920 \(\times\) 1080 pixels (Full HD) and coded in H.265, producing an uplink data rate of around 2.5 Mbps per camera.
# Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques
## 4 Results and Discussion
### Cluster Analysis Result  
To obtain the number of clusters (k), we have applied the elbow method in Python to find the best K value. The cost is plotted for a range of K values as shown in Figure 12. The cost is equal to the total of all the dissimilarities within the cluster method. The elbow method plots the cost function value generated by various k values. The number of cluster (k) values is said to be the most acceptable when the cluster value has the greatest decline and forms an angle [34].  
In the above graph, we can see a bend at k = 5 indicating the optimal number of clusters. Therefore, an appropriate value to pick here for cluster analysis is k = 5.  
Table 4 shows the results of the k-modes algorithm applied to the crime dataset for k = 5. From the results of the clustering analysis can be seen that _Crime Type_ 'Theft' is majorly occurring in the different _Crime Event Location_ 'Multan Chungi' and 'G1 Market'. Likewise, _Crime Type_ 'Robbery' is mostly occurring in the different _Crime Event Location_ 'Mughalpura Road' and 'Abbas Block'. Hence it can be concluded that theft and robbery criminal activity is majorly happening in 'Mustafa Town', 'Mughal Pura', and 'Johar Town' areas. Therefore, in case of theft and robbery criminal activity, these particular areas are identified as risky locations.  
Based on the results obtained from the k-modes algorithm shown, an analysis has been made for the identification of possible criminal activities that can also happen at a particular location. Against each clustering solution, we have extracted unique groups of related records with the help of obtained K-modes clustering results in a CSV file generated in Google Colab.  
For the identification of the criminal activities (_Crime Type_) that can also happen in a particular location (_Crime Event Location_), we have fixed the values of the attributes _Crime Time_, _Day Type_, _Distance of Crime Event from Police Station_, and _Direction of Crime Event from Police Station_ as shown in Table 4. The subsection below discusses the risk identification of possible criminal activities in particular locations from the clustering solution in detail.
## 2 Materials and Methods
### Numerical Model and Methods
#### 2.2.3 Boundary Conditions  
At all walls of the larynx models, no-slip no-injection boundary conditions were applied. The walls of vocal folds were defined as moving boundaries. At the inlet and outlet of the flow domain, different pressure boundary conditions were applied to identify the best combination of constant or temporal pressure evolution based on the experiments. Furthermore, two lengths of the supraglottal channel were considered as described in Section 2.2.1. In total, five simulation cases were defined as specified in Table 1. Cases C1-C4 had the shorter supraglottal channel (60 mm) and case C5 had the longer channel (190 mm) equal to the experimental model to evaluate the effect of shortening the supraglottal channel  
Figure 2: **(a)** Schematic of the numerical larynx model. **(b)** Glottal area waveform based on the experimental model flow-induced motion of vocal folds. **(c)** Geometry of the vocal fold at the middle of the cycle. The medial surface is defined with blue. R1 = 1 mm and R2 = 2 mm.  
on the results. The inlet and outlet pressure boundary conditions were either pressure waveforms taken from the experiments (\(\mathrm{P^{\prime}_{s}(t)}\) and \(\mathrm{P^{\prime}_{60}(t)}\) in Figure 3) or their mean values. \(\mathrm{P^{\prime}_{s}(t)}\) and \(\mathrm{P^{\prime}_{60}(t)}\) are the pressure waveforms measured at the subglottal channel and 60 mm downstream of the glottal exit of the synthetic larynx model, respectively. The mean values of the subglottal and supraglottal pressures were \(\overline{\mathrm{P^{\prime}_{s}}}\) = 3251 Pa and \(\overline{\mathrm{P^{\prime}_{60}}}\approx\mathrm{P_{0}}\), respectively. \(\mathrm{P_{0}}\) is 0 Pa, being the relative pressure with respect to the environment. \(\mathrm{P_{0}}\) was also applied at the outlet of case C5 similar to the experimental model. Although the applied subglottal pressure is higher than the physiological values during normal phonation, such high lung pressures have been detected in special cases of phonation such as shouting, loud speech or singing [1].
# Identical Parallel Machine Scheduling Considering Workload Smoothness Index
## 7 Conclusions  
In this work, we study an identical parallel machine scheduling problem, aiming at minimizing the workload smoothness index or the workload imbalance between the parallel machines. We first prove the NP-hardness of the considered problem, and we prove its theoretical upper and lower bounds. An efficient genetic algorithm and an improved list scheduling algorithm are further proposed to solve the problem. Numerical results show the following: (1) the list scheduling algorithm based on the LPT rule performs better than that based on SPT and RPT rules in dealing with parallel machine scheduling problems to minimize workload imbalance; and (2) the designed genetic algorithm can output high-quality feasible solutions in solving small-scale instances (i.e., \(n\leq 15\)), while the improved list scheduling algorithm has better performance in solving large-scale instances (i.e., \(n\geq 50\)).  
In practice, the parallel machines may be non-identical or have job-dependent processing capabilities. Thus, one future topic is to extend the considered problem to the scenario with either uniform or unrelated machines. Another interesting research aspect is to introduce the concept of the job family into the considered problem, where jobs belong to different families and each machine can only process a partial family of the jobs. Finally, it is common that jobs are of uncertain processing times in practice, while the established model in this work is unable to handle the scenario with this uncertainty. Therefore, it is meaningful to extend our study to the scenario with uncertain processing times of jobs.  
Conceptualization, Z.W. and F.Z.; methodology, Z.W. and F.Z.; software, Z.W.; validation, F.Z. and M.L.; formal analysis, Z.W.; investigation, Z.W.; resources, Z.W.; data curation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W. and F.F.; visualization, Z.W.; supervision, F.Z.; project administration, F.Z.; funding acquisition, F.Z. and M.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 72271051, 71832001, 72021002 and 72071144) and the Fundamental Research Funds for the Central Universities (Grant No. 2232018H-07).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 5 Results and Discussion
### Optimal Time Period for Collecting Satellite Images
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Single Orthoimages**} & \multirow{2}{*}{**OA (\%)**} & \multirow{2}{*}{**Kappa Coefficient**} & \multicolumn{4}{c}{**\(\boldsymbol{F_{\text{g}}}\) (\%)**} \\ \cline{3-6}  & & & **Aubergine** & **Cucumber** & **Pepper** & **Tomato** \\ \hline
**Landsat 15 November** & 64.4 & 0.41 & 38.7 & 52.1 & 60.7 & 72.2 \\
**Landsat 12 September** & 67.0 & 0.41 & 13.5 & 11.8 & 68.8 & 76.2 \\
**Landsat 11 August** & 73.2 & 0.54 & 42.7 & 34.5 & 79.1 & 79.1 \\
**Landsat 26 July** & 69.7 & 0.44 & 36.2 & 0.0 & 72.1 & 77.7 \\
**Landsat 10 July** & 63.8 & 0.36 & 17.8 & 25.0 & 59.9 & 74.9 \\
**Landsat 24 June** & 63.3 & 0.31 & 18.2 & 6.3 & 55.9 & 74.2 \\
**Landsat 8 June** & 65.4 & 0.43 & 41.6 & 6.3 & 63.9 & 76.1 \\
**Landsat 23 May** & 64.0 & 0.37 & 32.4 & 23.4 & 54.9 & 76.1 \\
**WV2 30 September** & 75.1 & 0.59 & 60.3 & 20.8 & 81.5 & 80.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy indices from single orthoimages using the no GLCM strategy.
## 2 Materials and Methods
### Fuzzy System with Takagi-Sugeno-Kang Inference  
Fuzzy inference matches continuous numerical input variables (crisp values) to fuzzy output variables that are the bases for decision-making (pattern recognition) through defuzzification. Fuzzy inference, after assigning a certain degree of truth to the input values using membership functions, applies an operator based on a defined set of if-then rules, to assign values to the output vector [41].  
Between the two main types of fuzzy inference systems, Mamdani-type [42] and Sugeno-type [43], which differ in the way the outputs are determined, the latter was chosen, as it does not require the defuzzification step to generate the output.  
The general flow of a fuzzy inference system is shown in Figure 4.  
The input vector \((x_{1},x_{2},\ldots,x_{n})\) contains \(n\) crisp variables \(x_{i},\forall\ i\in[1,n]\), each of them is transformed into \(K\) fuzzy sets using \(K\) membership function values for each input variable \(x_{i}\), given in the model as \(F_{k,i}(x_{i})\in[0,1],\ k=1,2,\ldots,K\).  
In other words, the fuzzification step generates a \(K\times n\) matrix of \(F_{k,i}(x_{i}),\ i=1,2,\ldots,n\), \(k=1,2,\ldots,K\) values that vary in the interval \([0,1]\). These functions are used to calculate the weight of \(K^{n}\) decision rules \(r=1,2,\ldots,K^{n}\) such that  
\[w_{r}=F_{k(r,1),1}(x_{1})\ \Delta\ F_{k(r,2),2}(x_{2})\ \Delta \tag{4}\] \[\ldots\Delta\ F_{k(r,n),1}(x_{n}),\]  
Figure 4: Fluxogram of a fuzzy inference system.  
where \(k(r,i)\in[1,K]\) is the fuzzy set assigned to the input variable \(x_{i}\) in rule \(r\) and \(\Delta\) represents a T-norm [23].  
The output \(Y\) of the inference consists of the weighted mean of the functions activated by each rule, which is denoted by \(y_{r},\ r=1,2,\ldots,K^{n}\), of the form  
\[Y=\frac{\sum_{r=1}^{K^{n}}w_{r}(x_{1},x_{2},\ldots,x_{n})y_{r}}{\sum_{r=1}^{K^{ n}}w_{r}(x_{1},x_{2},\ldots,x_{n})}. \tag{5}\]  
If the functions activated by the \(y_{r}\) rules are a fuzzy subset, the inference output \(Y\) needs to be defuzzified to translate the fuzzy output \(Y\) into a crisp output \(z\).  
In the Takagi-Sugeno-Kang inference system, the functions \(y_{r}\) are constants or linear functions, by which \(Y\) is crisp and no defuzzification is necessary. On the other hand, the T-norm used to calculate the weights \(w_{r}\) is the norm of the minimum [23], i.e.,  
\[\begin{split} w_{r}&=\min(F_{(r,1),1}(x_{1}),F_{(r,2),2}(x_{2}),\\ &\ldots,F_{(r,n),1}(x_{n})).\end{split} \tag{6}\]
## 5 Simulation of Coded Laser Pilots-Based HILPB WPT System
### Simulation Results  
We used Equations (1)-(7) to calculate the RCPI and the MDP of each WPC. When a PTX receives a PCI request command, it sends its unique ID code at a pulse power of 46 ul through all WPCs available for transmission. Table 6 lists the WPCs of the PTXs available for each PRX to receive the largest amount of power. Each cell displays the PCI of the WPC (\(\theta,\phi\)) of the given PTX and the RCPI. As defined in Equation (6), the RCPI and MDP vary from a WPC of a PTX to another depending on the strength of the laser beam incident on the PRX.  
Tables 7-9 are simulation results of different algorithms for selecting a PTX when PRXs R1 to R3 sequentially request power. Each of the table cells represents the MDP and MPQ of the PRX and the MTP and WPC of the PTX. Completion of PTX-PRX pair has the white color in the corresponding order and then turns to the gray color in the following order. The cells' contents containing '-' indicates that the PTX and PRX have not been paired yet. According to the algorithm, different results could be obtained when the PRX selects an appropriate PTX and generates an optimal WPC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PTX** & **T1** & **T2** & **T3** & **T4** \\ \hline MTP (kW) & 300 & 250 & 200 & 150 \\ \hline Location of the PTX (x, y, z) & (10, 18, 20) & (10, 10, 20) & (18, 10, 20) & (18, 18, 20) \\ \hline \multirow{3}{*}{WPC} & Range & (90–180, 0–359) & (90–180, 0–359) & (90–180, 0–359) & (90–180, 0–359) \\  & Spacing & (1, 1) & (1, 1) & (1, 1) & (1, 1) \\  & (\(\theta,\phi\)), (\({}^{\circ}\)) & (1, 1) & (1, 1) & (1, 1) \\ \hline HILPB & Diameter (m) & 0.01 & 0.01 & 0.01 & 0.01 \\ characteristics & divergence (rad) & 0.01 & 0.01 & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of the PTXs used in the simulation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PRX** & **R1** & **R2** & **R3** \\ \hline MPQ (W) & 200 & 300 & 150 \\ Location of the PRX (x, y, z) (m) & (7, 12, 1.5) & (20, 16, 2) & (12, 20, 1.5) \\ PV array architecture & \(5\times 5\) & \(5\times 5\) & \(5\times 5\) \\ PV array surface area (m\({}^{2}\)) & \(1\) & \(1\) & \(1\) \\ Power conversion efficiency & 30 \% & 30 \% & 30 \% \\ Size of PV array & 1 m & 1 m & 1 m \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics of the PRXs used in the simulation.
## 1 Introduction
Thus, some vascular details could not be detected. Besides, manual and semi-automatic segmentation methods which are the gold standards, are error-prone, time consuming, tedious, and may suffer from intra- or inter-observer variability. The blood supply to the brain is provided by two arterial axes on each side of the neck, the internal carotid giving origin to the anterior circulation (AC) and vertebral arteries giving origin to the posterior circulation (PC). Hypertension and intracranial large artery atherosclerosis are well known risk factors for cerebrovascular diseases including both hemorrhagic and ischemic strokes [14]. Systemic hypertension is also involved in the development of aneurysms via increased shear stress and pressures on the arterial wall, and change in the shear stress gradients precipitate the aneurysms development [15]. Differential associations with vascular risk factors and stroke mechanisms have been cited between the posterior and anterior intracranial vasculatures, and pathological evidence exists that more advanced brain arterial aging is noted in the posterior circulation compared with the anterior circulation [14]. Anterior and posterior circulation (PC) strokes are reviewed as separate entities, with different underlying pathogenesis, natural histories, and potential responsiveness to interventions such as anticoagulation [16]. Also interestingly, hypertensive disease-induced primary intracerebral hemorrhage occurs mainly at the small penetrating blood vessels in the posterior cerebral circulation rather than the anterior cerebral circulation. This may be explained by the fact that the responses of the posterior cerebral blood flow (CBF) in several physiological conditions (e.g., orthostatic stress, hypoxia, dynamic resistance exercise, heat stress, etc.) are different from those of the anterior CBF and posterior cerebral vasculature may not be able to properly regulate an elevation in blood pressure compared with the anterior cerebral vasculature [17]. Another difference between the anterior and posterior circulations is that intracranial aneurysms arise from the anterior cerebral circulation more than the posterior circulation and the AC aneurysms tend to have higher risk of rupture [18]. Based on that, it is obvious that several cerebrovascular diseases have different impacts on the anterior and posterior compartments in human brains and are also affected differently by the anterior and posterior blood circulations. Thus, there is a possibility that hypertension-related cerebrovascular changes may affect one compartment more than the other. In this case, the study of cerebrovascular changes in each compartment independently may improve the clinical and prognostic value of hypertension diagnosis. In this manuscript, we present a computer-aided diagnosis (CAD) system that aims at investigating whether studying the cerebral changes in specific regions of human brains ( specifically, the anterior, and the posterior compartments) separately, would increase the accuracy of hypertension prediction compared to studying vascular changes occurring over the entire brain's vasculature.
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 2 Background and Related Works
### Comparison of Existing Decentralized Approaches
[26] explore a pay-as-you-watch model for decentralized streaming, allowing users to pay only for the content they consume through a subscription-based payment system on the Ethereum blockchain. Their approach leverages IPFS for content storage and uses protocols like Unlock for access control and Superfluid for real-time financial transactions, creating a flexible monetization model. While [26] contributes to the economic structure of decentralized streaming, they do not prioritize real-time performance optimization or user data sovereignty as VidBlock does. Ref. [26] focuses on payment mechanisms and access control but lacks VidBlock's emphasis on low-latency P2P streaming and full decentralization. VidBlock, therefore, provides a more comprehensive solution by addressing both the technical performance of real-time streaming and user-centric data management, whereas [26] primarily focuses on monetization and access. [PERSON] et al. [27] introduce a swarm-based P2P video content distribution framework aimed at optimizing delivery efficiency in decentralized streaming environments. Their architecture leverages dynamic caching and swarm protocols to enhance resource utilization and reduce delivery latency. However, their study focuses primarily on improving content distribution efficiency without addressing broader concerns like user data privacy or ownership. Unlike VidBlock, which combines user-controlled privacy, smart contracts, and comprehensive real-time streaming capabilities, [27] is limited to the technical optimization of P2P distribution. [PERSON] et al. [28] propose a blockchain-based decentralized video platform designed for secure video sharing and intellectual property protection. Their work emphasizes preventing unauthorized access and safeguarding intellectual property but does not address performance optimization or real-time streaming capabilities. The lack of relay node integration further limits their platform's ability to handle large-scale streaming demands efficiently. In contrast, VidBlock integrates relay nodes to enhance content distribution, ensuring low latency and high-quality streaming while maintaining data privacy and security. Unlike VidBlock, which builds a comprehensive decentralized architecture that fully leverages relay nodes for real-time performance optimization, most existing studies limit their scope to specific aspects of blockchain integration, such as content security, monetization, or adaptive streaming within CDN frameworks. These approaches fail to fully explore the potential of end-to-end P2P streaming with relay nodes to bypass CDNs entirely. VidBlock stands out as one of the few solutions that redefine streaming infrastructure, aligning with Web3.0 principles by integrating relay nodes to enhance scalability, reduce latency, and ensure stable, user-focused performance.