esa-sceva/satcom-chunk-collection huggingface dataset:
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 4 Multi-Application Scheduling Algorithm (MASA) Based on DRL  
The MASA proposed in this paper mainly addresses two key issues of multi-application scheduling in dynamic environments: (1) scheduling decisions for multiple job DAGs in streaming mode, and (2) neural network training methods in dynamic mission environments.  
Task scheduling is essentially a sequential decision optimization problem, as shown in Figure 2. This section represents the multi-application scheduling scenario as a Markov Decision Making (MDP) process [24], when the ready task completes (releasing PEs) or a new application arrives (adding a job DAG), SCHEDULER takes the information of the job in the current job Queue as the input state, outputs the scheduling action, and after the action completes and returns a reward to SCHEDULER to determine whether the action is good or bad. The reward is designed according to the framework's final optimization objective.  
Figure 2: MASA Diagram.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 6 Simulation and Performance Evaluation
### Simulation Settings  
Sensor nodes are randomly deployed in an underwater spherical 3D region of \(300\) m \(\times\)\(300\) m \(\times\)\(300\) m. The sink node is deployed at the water surface (the coordinate is \((0,0,300)\)). A random walk mobility model with average speed of 1.5 m/s [29] is used in the simulation. Each sensor node randomly selects a direction and moves to the new position with a random speed between the minimal speed and maximum speed, which are 0.5 m/s and 3 m/s, respectively. There is a source node deployed at the bottom of water (the coordinate is \((0,0,0)\)). We assume that the sink node and the source node are stationary once deployed. Data packets are generated by the source node periodically with a packet size of 50 bytes. The average package-generating interval is set at 2 s. We set the simulation time as 300 s and the initial energy of every node as 10,000 J. To better simulate the energy consumption, we set the transmitting and receiving power as 2 and 0.75, respectively. The idling power of every node is set to 0.008. The maximum range of one-hoptransmission is 100 m.  
We use the following metrics to evaluate the performance of protocols.  
1. _Packet Delivery Ratio_: is defined as the sum of the different ID packets received by the sink (\(P_{dif}\)) divided by the total number of packets generated at the source node (\(P_{gen}\)), \(P_{dif}/P_{gen}\). Although a packet may reach the sink multiple times, these redundant packets are counted only once in \(P_{dif}\).
2. _Average Multi-copy Packets_: is defined as all of the packets received by the sink (\(P_{all}\)) divided by the number of packets generated by the original node (\(P_{gen}\)), \(P_{all}/P_{gen}\). Considering that multiple copies of a packet may reach the sink, the result may be greater than one.
3. _Average End-to-End Delay_: for each packet \(p\), the end-to-end delay for packet \(p\) is the moment a packet arrives at the sink (\(T_{arr}^{p}\)) minus the moment it is generated (\(T_{gen}^{p}\)), \(T_{arr}^{p}-T_{gen}^{p}\). Therefore, the average end-to-end delay is \(\sum{(Tparr-Tpgen)/P_{dif}}\).
4. _Total Energy Consumption_: is defined as the initial energy of all nodes in the system (\(E_{ini}\)) minus the total remaining energy at the end of the experiment (\(E_{rem}\)), \(E_{ini}-E_{rem}\).
## 2 Background and Definition of CIM
### Bibliometric Analysis  
Based on the bibliometric analysis conducted on the Web of Science using the keyword \"City Information Modeling\", research in this field spans several key disciplines, highlighting its broad interdisciplinary applications [36]. Computer Science leads the field, demonstrating its central role in data processing, system integration, machine learning, and artificial intelligence technologies, all of which are crucial for the development of smart city frameworks and big data analytics. Engineering follows closely, focusing on CIM's applications in urban infrastructure, energy management, and environmental monitoring, contributing to sustainable urban development. Environmental Sciences and Ecology also play a significant role, emphasizing CIM's applications in ecological monitoring, environmental impact assessments, and green urban planning. In addition, Geography provides essential spatial data analysis capabilities for urban planning and land use management, while Mathematics offers the theoretical and computational foundation for modeling and optimizing CIM systems. Research in Public Environmental Occupational Health and Meteorology/Atmospheric Sciences highlights the integration of environmental health and weather data in CIM, contributing to urban resilience and sustainability. The presence of research in Science Technology Other Topics further showcases CIM's impact across various technological domains. This distribution of research underscores CIM's versatility as a comprehensive technological framework, applicable in diverse areas ranging from infrastructure and environmental protection to health and weather monitoring. The results are shown in Figure 1.  
A bibliometric analysis of publications indexed in the Web of Science database using the keyword \"City Information Modeling\" reveals a continuous upward trend in research interest [36]. Although CIM research began in the 1990s, it remained relatively niche until the early 2000s, when the integration of a GIS and BIM laid the foundation for digital urban management. As information and communication technologies advanced, the research expanded beyond geographic data management to include urban infrastructure, resource optimization, and environmental monitoring. The 2010s marked a period of rapid growth, driven by the rise of smart cities, IoT, cloud computing, and big data analytics, leading to widespread applications in urban planning, transportation management, and environmental sustainability. Since 2015, the field has seen a surge in publications, particularly between 2018 and 2021, when the annual output consistently exceeded 5000 papers. Entering the 2020s, the integration of CIM with AI, 5G, and digital twins has further broadened its applications, making it a core technology for smart cities and sustainable urban develop  
Figure 1: Major research disciplines in City Information Modeling (CIM).  
ment. The steady increase in the research output highlights CIM's growing significance in academia and industry. The annual publication trends are illustrated in Figure 2.
In the proposed ASVMR protocol, the SVM is utilized to train the model for the selection of the relay node, where four factors are selected as features for the model. A reasonable routing pipe radius is chosen based on the PDR to minimize latency and extend the network lifetime. Moreover, a waiting time mechanism is designed for the opportunity routing to improve the PDR. To deal with the transmission failure of the routing void, each sensor node can activate the recovery mechanism to bypass the void region and continue forwarding data packets. To the best of our knowledge, this study is the first attempt to employ the SVM model for the design of a routing protocol in the UASN. The main contributions of the paper can be summarized as follows. Unlike the traditional routing protocols which select the relay node with a single parameter, the proposed ASVMR protocol employs a selection process to identify a group of forwarding candidates from neighboring nodes based on four factors, guaranteeing the optimal routing choice and enhancing the performance significantly. The waiting time mechanism for opportunity routing is enhanced by incorporating the distance between sensor nodes, resulting in a reduction in both end-to-end delay and data packet loss. A scheme for adaptive routing pipe radius is proposed to reduce unnecessary transmissions while also maintaining a high PDR. The remainder of the paper is organized as follows. Section 2 contains the preliminaries, including the acoustic propagation model, the network model, and the SVM model. Section 3 presents the proposed ASVMR protocol in detail, and Section 4 elaborates on the design of corresponding routing protocol. In Section 5, the simulation results and discussions are given. Finally, Section 6 concludes the paper.
# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
## 3 The Proposed Technique: Combined Access Barring  
### System Model and Proposed Algorithm  
We consider RA process of LTE-A explained in Section 2. The eNodeB broadcasts transmission probability \(p\in(0,1)\) and 10-bit bitmap. The transmission probability is dynamic, i.e., it changes in every time slot depending on the number of active devices in the network denoted by \(u\), where \(u\subset U\). \(U\) denotes total number of IoT devices within the network. By active devices we mean the backlogged devices within unbarred class. The bitmap can be broadcast through system information block (SIB)-14. Each IoT device is assigned a class number between 0 and 9 which is stored in subscriber identity module (SIM). Each IoT device reads bitmap from SIB-14 and checks whether its class is unbarred or not. If the device is in unbarred class, it generates a random number and compares it with \(p\). If the generated number is less than \(p\), it transmits RA preamble to eNodeB otherwise it waits for the next opportunity. After the transmission attempt, eNodeB observes the number of idle, successful and collided preambles and depending on the outcome it estimates the change in backlog. We denote the  
Figure 3: Importance of choosing optimal access class barring (ACB) factor: No. of devices, \(U\ =\ 1000\), No. of preambles, \(R=40\), simulation time \(=10^{6}\) slots for fixed probabilities, \(p=0.004,0.2\), and 0.04 (optimal).  
number of idle and success preambles as \(i\) and \(s\) respectively. Based on current backlogged IoT devices, the transmission probability for the next slot is calculated.  
Please note that the \(p_{t}\) depends upon the number of IoT devices within the certain class unlike in [13], where the estimation on all the devices is considered. Moreover in [13], the information on only idle preambles used for estimation of devices. However in [19], the authors explained that by early collision estimation, the information on idle as well as success preambles can be used. Hence, in this paper we use the information on idle as well as success preambles. We explain the transmission probability \(p_{t}\) and estimation of devices \(x_{t}\) in the next section.
# Experimental Demonstration of Beam Scanning of Dual-Metasurface Antenna
## 4 Discussion
Finally, the radiating surface employs the standard sinusoidal modulation scheme; the scan range can be improved (for the same impedance variation of the bottom plane) by more advanced impedance modulation schemes, and especially by ad-hoc numerical optimization of the profile [33; 34]; this has shown the potential of increasing sensitivity to phase velocity, thus enhancing the scanning range.
## 3 Results and Discussion
### Finite Element Analysis for \(h\) = 1 \(\upmu\)m with Three Different Orientations
Furthermore, different types of polymers will behave differently under the same experimental conditions [34; 36; 37]. It was shown that hardness and modulus for the glassy polymer tend to  
Figure 10: Von Mises stress [MPa] distributions for \(h=1\) μm with different orientations and different nominal separations. (**a**) 0\({}^{\circ}\), (**b**) 90\({}^{\circ}\), and (**c**) 45\({}^{\circ}\); (**1**) 4/\(h\) = 5, (**2**) 4/\(h\) = 10, (**3**) 4/\(h\) = 15, and (**4**) von Misses stress vs. depth taken from the finite element modelling cross-section at the center between two indents. decrease with slower loading time and increased holding time at maximum load [34; 36]. It was also demonstrated that the rate at which indents are performed and/or hold time at defined maximum conditions can change the indent area by 20-40% for polymer materials [34; 36], this in turn will increase the deformed area/volume around the indent and influence the minimum spacing. In the majority of cases, experiments usually are set up as matrix in a load-controlled mode, and because of the dwell time added to compensate creep, the final depth, and thus indent area, will be larger than the estimated one. For the indent example shown in Figure 6, _d/h_ = 10, it was estimated that if the indent area increases by 40% because of loading rate and creep set up, but the nominal separation between indents will be set as 10, the actual indent separation will look like as so it is 7-8. For the matrix set up, the distance is too close (Figure 6b), especially for polymers with high pile-up preferences, the deviation in hardness and modulus in general is still large (Figure 9). Therefore, the recommendation would be to have spacing of at least 15 for all polymeric materials independent on indentation depth. Furthermore, for very soft polymers, the Berkovich tip might not be the best choice to execute these tests and flat punch or spherical tips have to be utilized, and therefore, the optimal spacing will differ greatly than the results reported here.
###### Abstract  
Climate change and technological development are pushing buildings to become more sophisticated. The installation of modern building automation systems, smart meters, and IoT devices is increasing the amount of available building operational data. The common term for this kind of building is a smart building but producing large amounts of raw data does not automatically offer intelligence that would offer new insights to the building's operation. Smart meters are mainly used only for tracking the energy or water consumption in the building. On the other hand, building occupancy is usually not monitored in the building at all, even though it is one of the main influencing factors of consumption and indoor climate parameters. This paper is bringing the true smart building closer to practice by using machine learning methods with sub-metered electricity and water consumptions to predict the building occupancy. In the first approach, the number of occupants was predicted in an office floor using a supervised data mining method Random Forest. The model performed the best with the use of all predictors available, while from individual predictors, the sub-metered electricity used for office equipment showed the best performance. Since the supervised approach requires the continuous long-term collection of ground truth reference data (between one to three months, by this study), an unsupervised data mining method k-means clustering was tested in the second approach. With the unsupervised method, this study was able to predict the level of occupancy in a day as zero, medium, or high in a case study office floor using the equipment electricity consumption.  
Keywords:occupancy prediction; smart meter; machine learning; cluster analysis; data-driven; smart building +
Footnote †: journal: Applied Sciences
# Polarimetric Detection of Chemotherapy-Induced Cancer Cell Death
## 4 Results and Discussion
### Measurement of the Mueller Matrices of Cells  
We performed a polarimetric study on three different cell lines, measuring the Mueller matrix and decomposing it in order to calculate different polarimetric magnitudes. As an example, Figure 3 shows the **MM** for a sample of HT29 cells on a microscope slide. Cells had an approximate diameter of 10-15 \(\upmu\)m. The element \(m_{11}\) of the Mueller matrix represents the intensity (a regular image of the object plane) where the locations of individual cells and their membranes are clearly visible.  
At first glance, the Mueller matrices of cells resembled a unitary matrix, showing that the optical activity of this sample under transmission observation and ordinary illumination conditions was very small. The different decompositions showed that samples hardly introduced any significant amount of diattenuation, retardance, optical rotation, or depolarization. However, some signal can be found apparently in the boundaries of the cells. Among all the polarimetric magnitudes introduced in the analysis methods previously mentioned, we focused our attention on diattenuation, since it seemed to offer the most significant results. Other magnitudes such as \(A\), \(TD\), and \(TB\) were also addressed.  
Figure 3: Experimental Mueller matrix image of HT29 cells performed at \(\lambda\) = 634 nm with a 5\(\times\) microscope objective.  
Figure 2: (a) Diattenuation (\(D_{T}\)), anisotropy (\(A\)), total dichroism (\(TD\)), total birefringence (\(TB\)), and depolarization index (\(D_{I}\)) obtained from a calibration measurement. The error bar is the standard deviation. (b) Imaging calibration Mueller matrix. The black scale bar represents 50 \(\upmu\)m.
# Modeling Trust in IoT Systems for Drinking-Water Management
## 4 Results and Discussion
### Experiment
#### 4.1.2 Calculation of Trust Levels  
To assess trust levels between critical elements, we used a mathematical model based on the (AHP).  
The (AHP) is a method used for multi-criteria decision-making that involves breaking down a complex decision problem into a hierarchical structure and assessing the relative importance of different criteria. In the context of trust level calculation, the AHP method is used to evaluate the trustworthiness of different critical elements based on factors such as accuracy, reliability, and experience. Here is a brief explanation of how the trust levels might be calculated using the AHP model:  
Step 1: Define the Hierarchy  
We define the hierarchy of factors and sub-factors. In this example, the main factors are accuracy, reliability, and experience.  
Level 1: Trust Evaluation factors  
Level 2: Accuracy  
Level 2: Reliability  
Level 2: Experience  
Step 2: Pairwise Comparisons  
We create pairwise comparison matrices for each level 2 factor. For example, for accuracy, reliability, and experience, see Table 4:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Critical Element** & **Precision** & **Reliability** & **Experience** \\ \hline Sensor1 & 0.78 & 0.92 & 5 \\ Sensor2 & 0.82 & 0.89 & 7 \\ PLC1 & 0.75 & 0.91 & 4 \\ PLC2 & 0.80 & 0.88 & 6 \\ Operator1 & 0.85 & 0.94 & 8 \\ Operator2 & 0.79 & 0.90 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance Evaluation of System Components.  
These values represent the relative importance of each factor compared to the others. The values are assigned based on expert judgment.  
Step 3: Calculate Weighted Sum  
We calculate the weighted sum for each level 1 factor by multiplying the values in the comparison matrix by their respective weights.  
Weighted Sum for Accuracy:  
\[(1\times\text{Weight for Accuracy})+\left(\frac{1}{3}\times\text{Weight for Reliability}\right)+\left(\frac{1}{2}\times\text{Weight for Experience}\right) \tag{6}\]  
Step 4: Normalize the Weights  
We normalize the weighted sums to obtain the relative weights for each level 1 factor.  
Step 5: Calculate Trust Levels  
Using the normalized weights, we calculate the trust levels for each critical element.  
Trust Level (critical element) = \(\sum\)(Normalized Weight for factor \(\times\) Trust Level for factor)  
Example Calculation for Trust Level (Sensor1):  
\[\text{Trust}(S1)=(\text{NWA}\times 0.65)+(\text{NWR}\times 0.52)+(\text{NWE} \times 0.68) \tag{8}\]  
where:  
Trust(\(S1\)) represents the trust level of Sensor 1. NWA is the Normalized Weight for Accuracy. NWR is the Normalized Weight for Reliability. NWE is the Normalized Weight for Experience.  
We repeat similar calculations for the other critical elements.
## 3 Results and Discussion
### Content of Volatile Compounds in Must and Wine  
Aroma is an important factor determining the quality of all foods, but in the case of wine, it is probably among the most important indicators contributing to consumer attraction and enjoyment. A distinctive bouquet and flavor of wine depends on several factors such as the type of cultivar, year of harvest, fruit ripening, wine fermentation, maturation of the final product, and production method [39]. Aurore wine and must confirmed the presence of 52 volatile compounds that belonged to different chemical families, including fatty acids, alcohols, esters, phenols, C\({}_{13}\)-norisoprenoids, monoterpenoids, and acetates (short-, medium- and long-chain ethyl and methyl esters). In this study and in the papers published by other authors [40], ten compounds, i.e., ethyl octanoate, ethyl decanoate, diethyl succinate, ethyl hexanoate, phenethyl acetate, 3-methylbutyl acetate, 3-methyl-1-butanol, 1-hexanol, 2-methyl-1-propanol, and 2-phenylethanol were identified as the main volatiles for the white wine aroma of Aurora cv. Table 3 shows the quantification of aroma constituents identified in Aurore wine. Total volatile compounds in the must amounted to 1.3 mg/L. In the course of the wine-making process, their content initially increased, particularly during fermentation (1.6-8.0 mg/L), but then decreased during maturation (1.6-4.2 mg/L). Esters constitute the most common chemical group in the volatile fraction of Aurore wines and musts and are associated with fruity and favorable properties in all types of wine. Ester content in the must amounted to 1.0 mg/L but the wine-making process contributed to its increase. Wine esters come from two distinct sources, i.e., enzyme esterification during fermentation and chemical esterification during long-term aging. The most prevalent wine esters include acetates of higher alcohols (formed in a reaction of acetyl-CoA with higher alcohols) and ethyl esters of fatty acids (formed by esterification of fatty acids and ethanol). Contrary to yeast strain, fermentation temperature did not significantly affect (\(p>0.05\)) ester content. Fresh wine produced by \(S\). _cerevisiae_ SIHA(r) Cryaroma featured the highest (6.5 mg/L), and the wine fermented spontaneously the lowest (1.4 mg/L) number of esters. The effect of maturation period and different fermentation temperature induced several changes in the aroma profile of Aurore wines. As expected, the number of esters decreased after storage but was still higher than in the must. The same non-significant role of the fermentation temperature was confirmed for the matured wine.