esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 State-of-the-Art and Open Challenges
### SLA Enforcement and Guarantees  
There is very little work currently in the literature that considers the enforcement of policies and SLAs by service providers, which revolves around finding out what service providers can guarantee in a NFV/SFC environment. Based on the recent literature, we argue that being able to adapt the QoS to frequent network changes is still a challenge in SFC environments for next-generation networks, and the ability to assess and visualise QoS requirements and parameters [108] will help in satisfying the long-term vision of SFC deployments.  
[PERSON] et al. [109] presented a QoE-driven service chain deployment, which also provides latency prediction features. Their focus was on improving the overall QoE for the user by reducing the number of rejections and waiting time experienced by users when accessing services. According to [PERSON] et al. [110], Content Delivery Network (CDN) providers can negotiate the SLA with the vNF provider by requesting the creation of virtual CDN (vCDN) instances. An example is a CDN providing video streaming services such as YouTube or NetFlix. The focus of the SLA agreement between the CDN and the vNF provider in this scenario will be on the bandwidth and delay requirements of the service that is being provided.  
[PERSON] et al. [111] proposed SLA-NFV, which is an SLA-aware framework that focuses on the SLAs of the tenants (service subscribers). Their framework leverages a hybrid infrastructure, programmable hardware, and software, with the goal of enhancing the capability of NFV in handling different SLAs. The experimental results show that SLA-NFV, which creates a hybrid NFV, reduces latency by approximately 60% when compared to a software service chain. Their work did not consider a solution that helps service providers meet the diverse SLA requirements needed for the operation of next-generation networks.
## 2 Materials and Methods
### Choice of the Optimization Algorithm
#### 2.4.1 NSGA-II Algorithm  
The Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) is one of the most commonly applied GAs in engineering optimization. The main principle of the algorithm is based on fast, nondominated sorting, with diversity preservation, called crowding distance sorting. [PERSON] described the NSGA-II initially in [18]. The following paragraph gives a brief overview of the articles' implementation.  
* Step 0--Define Algorithm inputs: Firstly, the user sets the maximum number of expensive function evaluations, the number of parents, and the mutation rate.
* Step 1--Initial Evaluation Points Selection: The experimental design is processed using expensive simulations for a predefined sampling size.
* Step 2--Iterative Improvement: To select the Pareto points of the sampling and iteratively optimize the designs, the algorithm repeats the steps of fast, nondominated sorting, diversity preservation, crossover, and mutation until it reaches the maximum number of costly evaluations.
* Step 2.1--Fast, Nondominated Sorting: For each design, a domination count, which represents the number of designs dominating the current design, is set up. Furthermore, a set of designs, dominated by the current design, is assigned. By sorting the designs by their domination count, frontiers are built, for which the most dominating frontiers represent the individuals with the highest potential.
* Step 2.2a--Density Estimation: The method calculates the crowding distance by the distance of the current solution to its two closest neighbors for each point of each frontier. It then sorts the designs by their crowding distance.
* Step 2.2b--Crowded Comparison Operator: The method prioritizes designs if they exhibit a prior nondominated rank. Accordingly, if two designs are in the same frontier and have the same nondomination rank, it prefers the design in the less crowded area.
* Step 2.3--Offspring Creation: The initial population based on the sampling is now sorted according to the mentioned strategies. Then, binary tournament selection, recombination, and mutation create the offspring population. Finally, elitism selects a certain percentage of parents.
* Step 2.4--Calculate Responses: Expensive simulations return the demanded objective function values.  
Finally, the procedure is repeated until the maximum number of expensive evaluations is reached.  
The parameter values for the NSGA-II are set by experience. Hence, the number of parents for each generation is set to 20% of the sampling size. A total of 30% of the created offspring are mutated for more extensive exploration. Furthermore, the change of the selected value by mutation is 10%.
# A Study on the Channel Expansion VAE for Content-Based Image Retrieval
## 1 Introduction  
The amount of present image data has been increasing tremendously and image retrieval is an essential technique for utilizing huge databases on the internet. These data look extensive and have various characteristics; thus, image retrieval is a challenging field. The traditional method utilizes metadata, including brief descriptors such as time-stamps, keywords, and related words explaining image contents, which is called text-based image retrieval (TBIR). The procedure for creating metadata is highly inefficient. Moreover, metadata contain ambiguous and subjective meaning and cannot always represent obvious feature of images. On the other hand, content-based image retrieval (CBIR) is based on the visual content of an image, such as its color, texture, or shape. CBIR can be used in many other fields as well criminal investigation [1], medical imaging [2], and remote sensing image analysis [3].  
Figure 1 is a general diagram of CBIR. It consists of feature extraction and similarity measurement stage for searching relevant images. All images are transformed into a feature vector or representation and compared in the vector space. This space has continuity and similarity; s stands for distance within it. Multi-dimensional vector space is in relatively lower level than human recognition, and the gap between vector feature and human recognition is called the \"semantic gap\". In other words, the ultimate objective is to reduce the semantic gap of recognition between human and computer vision [4].  
Traditional hand-crafted features have lower accuracy despite of the huge computation involved, while recent deep learning-based research shows notable results. Therefore, most computer vision studies focus on convolutional neural networks (CNNs). Since the effect of off-the-self features by pre-trained classification models has been proved [5], fine-tuning is a major method in image retrieval. Fine-tuning requires labeled datasets, which means a supervised methodology. However, we studied an unsupervised method to manage massive data with a simple process without annotation. Our model is based on a general self-supervised model, a variational auto-encoder (VAE). This paper proposes a channelexpansion VAE that is able to enhance the feature extraction of image content by expanding the layer. It allows networks to search more kernels and extract better features. VAE attributes generate continuous space such that similar images are placed close to each other in latent space. The encoder of the VAE embeds features into the latent space, and similarity can be measured by using a distance metric. Channel expansion VAE (CEVAE) results in a competitive mAP in comparison to existing VAE for CBIR and traditional unsupervised methods. Furthermore, we studied an activation map derived from the dimension of latent space on a more complex dataset.
###### Abstract  
On 23 January 2024, a \(M_{5}7.1\) earthquake struck Wushi County, Xinjiang Uygur Autonomous Region, marking the largest seismic event in the Southern Tianshan (STS) region in the past century. This study investigates the relationship between hydrothermal fluid circulation and seismic activity by analyzing the chemical composition and origin of fluids in natural hot springs along the Maidan Fracture (MDF). Results reveal two distinct hydrochemical water types (Ca-HCO\({}_{3}\) and Ca-Mg-Cl). The \(\delta\)D and \(\delta^{18}\)O values indicating spring water are influenced by atmospheric precipitation input and altitude. Circulation depths (621-3492 m) and thermal reservoir temperatures (18-90 \({}^{\circ}\)C) were estimated. Notably, the high \({}^{3}\)He/\({}^{4}\)He ratios (3.71 Ra) and mantle-derived \({}^{3}\)He content reached 46.48%, confirming that complex gas-water-rock interactions occur at fracture intersections. Continuous monitoring at site 513 (144 km from the epicenter of the Wushi \(M_{5}7.1\) earthquake) captured pre-and post-seismic hydrogeochemical fingerprints linked to the Wushi \(M_{5}7.1\) earthquake. Stress accumulation along the MDF induced permeability changes, perturbing hydrogeochemical equilibrium. At 42 days pre-Wushi \(M_{5}7.1\) earthquake, \(\delta^{13}\)C DIC exceeded +2\(\sigma\) thresholds (\(-2.12\)%), signaling deep fracture expansion and CO\({}_{2}\) release. By 38 days pre-Wushi \(M_{5}7.1\) earthquake, Na\({}^{+}\), SO\({}_{4}\)\({}^{2-}\), and \(\delta^{18}\)O surpassed 2\(\sigma\) levels, reflecting hydraulic connection between deep-seated and shallow fracture networks. Ion concentrations and isotope values showed dynamic shifts during the earthquake, which revealed episodic stress transfer along fault asperities. Post-Wushi \(M_{5}7.1\) earthquake, fracture closure reduced deep fluid input, causing \(\delta^{13}\)C DIC to drop to \(-4.89\)%, with ion concentrations returning to baseline within 34 days. Trace elements such as Be and Sr exhibited anomalies 12 days before the Wushi \(M_{5}7.1\) earthquake, while elements like Li, B, and Rb showed anomalies 24 days after the Wushi \(M_{5}7.1\) earthquake. Hydrochemical monitoring of hot springs captures such critical stress-induced signals, offering vital insights for earthquake forecasting in tectonically active regions.  
Wushi \(M_{5}7.1\) earthquake; hot spring; MDF; STS +
Footnote †: journal: Applied sciences
## 1 Introduction
This study treated the company quality influence issues on the decision making by considering the analytical relation considering the impact of the failure rate, which is also influenced by the variation of production rates, on the average number of failures as well as on the maintenance and quality strategies and by integrating a statistical control process tool. Therefore, the established information gaps following relevant contributions in the current and relevant literature are that so many basic impacts of maintenance contribution towards a reliable, quality, and cost-effective production system remain unexplored, which is why we investigated the multiple correlations of maintenance strategy integrated with production planning, inventory management, and statistical quality control. The present paper coordinates all the aspects (production policy, maintenance strategy, and quality control) joined into the same model, which aimed at minimizing the total cost of the production process under service and quality constraints. The integration of the three functions with a statistical process control tool such as a control chart has an objective to improve the maintenance strategy in order to obtain maintenance planning that is more economic and more precise. The expected total cost consists of production, inventory holding, delivery, delay, maintenance, sampling, and non-quality costs. The inventory system, consisting of several warehouses extended from the principal store, allows satisfying the random customer demand at an economic production rate about maintenance and quality costs' minimization. This paper aimed to determine the economic production planning, optimum delivery planning for various warehouses considering the cost/time of delivery, the delay penalty even at a random demand, and the optimal maintenance and quality strategy. A mathematical model will help to find the decision variables, which will optimize the objective function. The rest of this paper is organized as follows: Section 2 presents the production, maintenance, quality problem description, and the control chart parameters. Section 3 develops, firstly, the mathematical expression of production and delivery strategies considering the impact of the delivery time on the production and delivery plans, and secondly, the total maintenance cost, and then total quality cost. The effectiveness of the solution is shown using a numerical example in Section 4. Finally, the conclusion is presented in Section 5.
## 3 Results and Discussion
### Microstructure
#### 3.2.2 Microstructure after HIP Post-Treatment  
No pores are visible after HIP post-densification. The dark spots seen on the sections in Figure 8 could be partially identified as delta-ferrite when analyzed at higher resolution on the images of the etched microstructure in Figure 9.  
Figure 6: Light microscopic image and detail of the microstructure of an as-sintered specimen in transverse section after chemical etching.  
Figure 7: Back scattered electron image and EDX mappings of a specimen in the as-sintered condition.  
The fraction of delta ferrite was determined by image analysis to be 2% and is thus significantly lower than in the specimens directly after sintering. The reason for this might be that the applied HIP temperature of 1150 \({}^{\circ}\)C is lower than the sintering temperature and that, according to Figure 3, a lower proportion of delta ferrite is present at this temperature. To completely avoid delta ferrite, the HIP cycle, or alternatively, a pressure-less heat treatment, would have to take place at even lower temperatures of about 980 \({}^{\circ}\)C and be followed by rapid cooling.  
In addition to the angular delta ferrite phase, a very round phase, marked by arrows as inclusions in Figure 9, is visible.  
The EDX mappings in Figure 10 reveal that these round inclusions are silicon- and manganese-rich oxides with contents of aluminum. Both silicon and manganese are present in the alloy system of the material. Aluminum is not part of the material composition and seems to be an impurity. However, Al is not only detected in combination with the Mn-Si-oxides, but also near the Mo-rich areas. In the as-sintered condition, Al and oxygen were only detected at pores. After HIP, both are found primary in combination with inclusions.  
Figure 8: Light microscopic overview of a specimen after HIP in (**a**) the transverse section and in (**b**) the longitudinal section.  
Figure 9: Light microscopic image of the microstructure of a specimen after HIP in the transverse section after chemical etching in a different magnification.
## 6 Security Analysis
### Performance Metrics  
The performance of the proposed HMPS scheme was evaluated based on its computational efficiency and execution time. These metrics were calculated to capture the end-to-end process of signature generation, verification, and hierarchical aggregation. For the experiment, parameters were set as follows: message size \(m=256\) bits, symmetric key \(s=260\) bits, ElGamal prime numbers \(p=264\) bits, the generator \(g=32\), private keys \(k=230\) bits.  
The execution time achieved by HMPS for three signers was recorded as 2.2 ms, including both signature generation and verification. This performance reflects the capability of HMPS to operate efficiently in real-time systems, providing robust security with minimal delay. The lightweight nature of HMPS underscores its potential in applications demanding high throughput and low latency, such as intelligent vehicle monitoring and IoT-based issue detection systems.
# On Finding Optimal (Dynamic) Arborescences
## 5 Experimental Evaluation
### Datasets  
Graph datasets comprising sparse and dense graphs were generated according to well known random models. To generate _sparse_ graphs, we considered three different models. One of them was the Erdos-Renyi (ER) model [30], with \(p=\frac{c\log n}{n}\) and \(c\geq 1\), where \(p\) denotes the probability of linking a node \(u\) with a node \(v\), and \(n\) is the number of nodes in the network. Whenever \(p\) has the previously defined value, the network has one giant component and some isolated nodes. Moreover, these graphs were generated using the fast_gnp_random_graph generator of the NetworkX library [31], with \(p=0.02\).  
Sparse scale-free directed graphs were also generated using the model derived by [PERSON] et al. [32] (identified as _scale-free_ in our experiments) and a variant of the _duplication_ model derived by [PERSON] et al. [33]. The first were generated using the scale_free_graph function of the NetworkX library, with all the parameters set with their omission value except for the number of nodes. The latter were generated using our own implementation, where given \(0\leq p\leq 1\), the partial duplication model builds a graph \(G=(V,E)\) according to partial duplication as follows: start with a single vertex at time \(t=1\), and, at time \(t>1\), perform a duplication step: uniformly select a random vertex \(u\) of \(G\); add a new vertex \(v\) and edges \((u,v)\) and \((v,u)\) with (different) random weights; for each neighbor \(w\) of \(u\), add edges \((v,w)\) and/or \((w,v)\) with probability \(p\); and find random integer weights chosen uniformly from \([0,1000]\).  
Dense graphs were generated using the complete_graph generator of the NetworkX library, which creates a _complete_ graph, i.e., all pairs of distinct nodes have an edge connecting them. Edge weights were assigned randomly.  
The running time and memory were averaged over five runs and for five different graphs of each size for all models.  
We also used real phylogenetic data in the dynamic updating evaluation, namely, real dense graphs using phylogenetic datasets available on EnteroBase [34]; the respective details are shown in Table 1. The graphs were built based on the pairwise distance among genetic profiles, as usual using distance-based phylogenetic inference [7]. The experiments on these data were carried out by considering increasing volumes of data, namely, \([10\%,20\%,30\%,\ldots,100\%]\).
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 5 Conclusions  
The paper introduced and successfully demonstrated the concept of generic virtual components, which bridge the gap between FMI-based models and event-based automation systems. The analysis of periodic and extrapolation-based predictive event mapping showed that:  
* only soft real-time operation was feasible, in general,
* predictive operation introduced strict bounds of a delay-free synchronization that were rarely met in practice, and
* periodic event mapping always had to delay and accumulate events.  
Experiments designed to study the boundaries of the mapping approaches via a prototypical interface application demonstrated the benefits of event-accurate predictive mapping. Although the hybrid model regularly violated the strict real-time condition, a satisfying best-effort synchronization could be achieved, and deviations when using periodic event mapping could be eliminated. In particular, FMI-based models without good real-time simulator support, such as various thermal models and models that require event-accurate coupling, may specifically profit from the presented work.  
Figure 5: Exemplary switching operation.  
**Author Contributions:** Conceptualization, M.H.S., E.W., and B.H.; methodology, M.H.S.; software, M.H.S. and E.W.; validation, M.H.S.; formal analysis, M.H.S. and B.H.; investigation, M.H.S. and N.A.; writing, original draft preparation, M.H.S. and B.H.; writing, review and editing, B.H. and E.W.; visualization, M.H.S.; supervision, W.K., B.H., and E.W.; project administration, E.W. All authors read and agreed to the published version of the manuscript.  
**Funding:** This work is partly supported by the European Union's Horizon 2020 research and innovation program (H2020/2014-2020) under project \"ERIGrid\" (Grant Agreement No. 654113).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BFB & Basic Function Block \\ CFB & Composite Function Block \\ FB & Function Block \\ FMU & Functional Mock-up Unit \\ FMI & Functional Mock-up Interface \\ HIL & Hardware-in-the-Loop \\ ODE & Ordinary Differential Equation \\ OLTC & On-Load Tap Changer \\ PLC & Programmable Logic Controller \\ RTI & Run-time Infrastructure \\ SCADA & Supervisory Control and Data Acquisition \\ SIFB & Service Interface Function Block \\ SSD & Service Sequence Diagram \\ WCET & Worst Case Execution Time \\ \end{tabular}
# Instrumental Uncertainty of Conductance Transducers for
## 2 LNEC Conductance Transducer for Maritime Reduced-Scale Models  
The measurement principle related to the LNEC conductance transducer is supported in the electrical resistance established between two metallic electrodes vertically immersed in water. This electrical quantity is influenced by the water resistivity, \(\rho\), the distance between the two electrodes, \(D\), the electrodes diameters, \(d_{1}\) and \(d_{2}\), and the water level, \(h\), being expressed by  
\[R=\frac{\rho}{2\pi}\frac{1}{h}\cdot\ln\left(\frac{4D^{2}}{d_{1}\cdot d_{2}} \right), \tag{1}\]  
when \(d_{1}\) and \(d_{2}\) are equal or lower than \(0.25\,D\).  
If the water resistivity can be assumed constant or compensated, the inverse resistance between the two vertical electrodes, which corresponds to the conductance quantity, \(G\) (expressed in Siemens), is directly proportional to the water level, thus allowing to measure this quantity in maritime hydraulic experimental activities. Conductance transducers are connected to a power supply and a signal conditioner, which provides an output voltage, usually recorded by a data acquisition system.  
The conductance transducers developed by LNEC (an example is shown in Figure 1), have an input voltage of 15 V (DC) and their output voltage varies between 0 V and 10 V (DC), available in different sizes and, therefore, covering a wide range of wave level measurement in experimental activities involving maritime reduced-scale models.  
In order to reduce the measurement uncertainty related to the variation of the water resistivity during and between experimental sets of measurements, additional compensation horizontal electrodes were considered in the lower region of the sensor (see Figure 1).  
The main concerns regarding to this type of conductance transducer are corrosion, water electrolysis and polarization effects in the metallic electrodes.